[FreeCourseSite.com] Udemy - The Data Science Course 2022 Complete Data Science Bootcamp

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12 - Probability - Distributions/015 A Practical Example of Probability Distributions.mp4 138.3 MB
11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference.mp4 125.5 MB
05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data.mp4 105.5 MB
40 - Part 6_ Mathematics/011 Why is Linear Algebra Useful_.mp4 86.2 MB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/001 Practical Example_ Linear Regression (Part 1).mp4 84.8 MB
20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis.mp4 80.8 MB
05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods.mp4 74.7 MB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/004 Business Case_ Preprocessing.mp4 74.4 MB
51 - Deep Learning - Business Case Example/004 Business Case_ Preprocessing the Data.mp4 73.8 MB
56 - Software Integration/003 Taking a Closer Look at APIs.mp4 65.3 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature.mp4 63.8 MB
05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning.mp4 61.8 MB
05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data.mp4 60.5 MB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/001 Business Case_ Getting Acquainted with the Dataset.mp4 60.3 MB
56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints_.mp4 58.8 MB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/006 Creating a Data Provider.mp4 56.2 MB
02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords_ Why are there so Many_.mp4 54.7 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set.mp4 54.3 MB
18 - Statistics - Inferential Statistics_ Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score.mp4 52.2 MB
51 - Deep Learning - Business Case Example/001 Business Case_ Exploring the Dataset and Identifying Predictors.mp4 51.4 MB
05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques.mp4 51.3 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence.mp4 51.3 MB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/008 Practical Example_ Linear Regression (Part 5).mp4 50.4 MB
02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science_ An Introduction.mp4 50.0 MB
01 - Part 1_ Introduction/002 What Does the Course Cover.mp4 49.7 MB
05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques.mp4 47.8 MB
18 - Statistics - Inferential Statistics_ Confidence Intervals/009 Confidence intervals. Two means. Dependent samples.mp4 45.0 MB
36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function.mp4 44.0 MB
51 - Deep Learning - Business Case Example/009 Business Case_ Setting an Early Stopping Mechanism.mp4 43.8 MB
62 - Appendix - Additional Python Tools/005 List Comprehensions.mp4 43.2 MB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/007 Business Case_ Model Outline.mp4 42.5 MB
15 - Statistics - Descriptive Statistics/001 Types of Data.mp4 42.5 MB
10 - Probability - Combinatorics/011 A Practical Example of Combinatorics.mp4 42.2 MB
56 - Software Integration/005 Software Integration - Explained.mp4 42.0 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python.mp4 41.3 MB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau.mp4 40.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set.mp4 40.1 MB
13 - Probability - Probability in Other Fields/001 Probability in Finance.mp4 39.7 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the _Date_ Column.mp4 38.9 MB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau.mp4 38.7 MB
20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level.mp4 38.2 MB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/009 MNIST_ Results and Testing.mp4 38.2 MB
63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames.mp4 37.3 MB
16 - Statistics - Practical Example_ Descriptive Statistics/001 Practical Example_ Descriptive Statistics.mp4 37.2 MB
20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known.mp4 37.0 MB
15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques.mp4 36.7 MB
38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful_.mp4 36.5 MB
09 - Part 2_ Probability/003 Frequency.mp4 36.4 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing.mp4 36.1 MB
02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI.mp4 35.9 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained.mp4 35.6 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy.mp4 35.3 MB
37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters.mp4 35.1 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables.mp4 35.1 MB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/004 MNIST_ Model Outline.mp4 34.7 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem.mp4 34.4 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared.mp4 34.2 MB
38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2).mp4 34.1 MB
02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic.mp4 34.0 MB
62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions.mp4 33.7 MB
20 - Statistics - Hypothesis Testing/007 p-value.mp4 33.1 MB
22 - Part 4_ Introduction to Python/004 Installing Python and Jupyter.mp4 32.9 MB
20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples.mp4 32.8 MB
50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST_ Preprocess the Data - Shuffle and Batch.mp4 32.7 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression.mp4 32.5 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model.mp4 32.0 MB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2).mp4 31.9 MB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/008 MNIST_ Learning.mp4 31.9 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn.mp4 31.6 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created.mp4 31.6 MB
15 - Statistics - Descriptive Statistics/002 Levels of Measurement.mp4 31.4 MB
50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST_ Learning.mp4 31.0 MB
52 - Deep Learning - Conclusion/004 An overview of CNNs.mp4 30.5 MB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4).mp4 30.1 MB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/006 Practical Example_ Linear Regression (Part 4).mp4 29.8 MB
63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes.mp4 29.8 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python.mp4 29.6 MB
09 - Part 2_ Probability/002 Computing Expected Values.mp4 29.2 MB
09 - Part 2_ Probability/001 The Basic Probability Formula.mp4 29.1 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 28.9 MB
12 - Probability - Distributions/008 Characteristics of Continuous Distributions.mp4 28.9 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table.mp4 28.7 MB
12 - Probability - Distributions/002 Types of Probability Distributions.mp4 28.7 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module.mp4 28.6 MB
18 - Statistics - Inferential Statistics_ Confidence Intervals/001 What are Confidence Intervals_.mp4 28.4 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 28.0 MB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/007 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases.mp4 28.0 MB
51 - Deep Learning - Business Case Example/008 Business Case_ Learning and Interpreting the Result.mp4 27.8 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence.mp4 27.6 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3_ Normality and Homoscedasticity.mp4 27.4 MB
44 - Deep Learning - TensorFlow 2.0_ Introduction/001 How to Install TensorFlow 2.0.mp4 27.3 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights.mp4 27.2 MB
44 - Deep Learning - TensorFlow 2.0_ Introduction/006 Outlining the Model with TensorFlow 2.mp4 27.0 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept.mp4 27.0 MB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/008 Business Case_ Optimization.mp4 26.9 MB
40 - Part 6_ Mathematics/010 Dot Product of Matrices.mp4 26.4 MB
63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique().mp4 26.3 MB
51 - Deep Learning - Business Case Example/003 Business Case_ Balancing the Dataset.mp4 26.2 MB
38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering.mp4 26.1 MB
60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II.mp4 26.0 MB
39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps.mp4 25.7 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment.mp4 25.5 MB
12 - Probability - Distributions/006 Discrete Distributions_ The Binomial Distribution.mp4 24.9 MB
28 - Python - Sequences/005 Dictionaries.mp4 24.9 MB
20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2).mp4 24.5 MB
13 - Probability - Probability in Other Fields/003 Probability in Data Science.mp4 23.9 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation.mp4 23.7 MB
29 - Python - Iterations/001 For Loops.mp4 23.6 MB
63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[].mp4 23.5 MB
28 - Python - Sequences/002 Using Methods.mp4 23.4 MB
50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST_ Preprocess the Data - Create a Validation Set and Scale It.mp4 22.9 MB
17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem.mp4 22.9 MB
42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm_ 1-Parameter Gradient Descent.mp4 22.7 MB
18 - Statistics - Inferential Statistics_ Confidence Intervals/008 Margin of Error.mp4 22.7 MB
50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST_ Testing the Model.mp4 22.6 MB
05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML).mp4 22.4 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS_.mp4 22.4 MB
63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series.mp4 22.2 MB
19 - Statistics - Practical Example_ Inferential Statistics/001 Practical Example_ Inferential Statistics.mp4 22.1 MB
50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST_ Outline the Model.mp4 22.1 MB
40 - Part 6_ Mathematics/006 Addition and Subtraction of Matrices.mp4 22.1 MB
29 - Python - Iterations/004 Conditional Statements and Loops.mp4 21.9 MB
36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python.mp4 21.9 MB
03 - The Field of Data Science - Connecting the Data Science Disciplines/001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 21.7 MB
62 - Appendix - Additional Python Tools/001 Using the .format() Method.mp4 21.7 MB
36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model.mp4 21.6 MB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/003 The Importance of Working with a Balanced Dataset.mp4 21.6 MB
05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods.mp4 21.2 MB
38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1).mp4 21.2 MB
11 - Probability - Bayesian Inference/011 Bayes' Law.mp4 20.9 MB
63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[].mp4 20.7 MB
12 - Probability - Distributions/010 Continuous Distributions_ The Standard Normal Distribution.mp4 20.7 MB
28 - Python - Sequences/001 Lists.mp4 20.5 MB
40 - Part 6_ Mathematics/008 Transpose of a Matrix.mp4 20.5 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization).mp4 20.4 MB
36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model.mp4 20.3 MB
15 - Statistics - Descriptive Statistics/015 Variance.mp4 20.2 MB
29 - Python - Iterations/002 While Loops and Incrementing.mp4 20.2 MB
15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation.mp4 20.1 MB
11 - Probability - Bayesian Inference/010 The Multiplication Law.mp4 19.8 MB
38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters.mp4 19.8 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python.mp4 19.8 MB
23 - Python - Variables and Data Types/003 Python Strings.mp4 19.7 MB
20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown.mp4 19.7 MB
15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots.mp4 19.7 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on _Education_, _Children_, and _Pets_.mp4 19.7 MB
12 - Probability - Distributions/009 Continuous Distributions_ The Normal Distribution.mp4 19.7 MB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework.mp4 19.6 MB
06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.mp4 19.5 MB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/007 Backpropagation.mp4 19.5 MB
11 - Probability - Bayesian Inference/004 Union of Sets.mp4 19.5 MB
62 - Appendix - Additional Python Tools/004 Triple Nested For Loops.mp4 19.4 MB
15 - Statistics - Descriptive Statistics/021 Correlation Coefficient.mp4 19.4 MB
05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI).mp4 19.4 MB
12 - Probability - Distributions/001 Fundamentals of Probability Distributions.mp4 19.3 MB
28 - Python - Sequences/003 List Slicing.mp4 19.2 MB
56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses.mp4 19.2 MB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/003 Digging into a Deep Net.mp4 19.1 MB
11 - Probability - Bayesian Inference/002 Ways Sets Can Interact.mp4 19.0 MB
40 - Part 6_ Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices.mp4 19.0 MB
25 - Python - Other Python Operators/002 Logical and Identity Operators.mp4 19.0 MB
10 - Probability - Combinatorics/006 Solving Combinations.mp4 19.0 MB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/009 Business Case_ Interpretation.mp4 18.6 MB
18 - Statistics - Inferential Statistics_ Confidence Intervals/004 Confidence Interval Clarifications.mp4 18.6 MB
36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression.mp4 18.5 MB
15 - Statistics - Descriptive Statistics/019 Covariance.mp4 18.4 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients.mp4 18.3 MB
20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error.mp4 18.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings.mp4 18.0 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python.mp4 18.0 MB
63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II.mp4 17.8 MB
15 - Statistics - Descriptive Statistics/011 Mean, median and mode.mp4 17.5 MB
11 - Probability - Bayesian Inference/001 Sets and Events.mp4 17.4 MB
47 - Deep Learning - Initialization/001 What is Initialization_.mp4 17.4 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter.mp4 17.3 MB
39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram.mp4 17.3 MB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/009 Basic NN Example with TF_ Model Output.mp4 17.1 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section.mp4 17.0 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn.mp4 16.9 MB
17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution.mp4 16.9 MB
63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I.mp4 16.8 MB
44 - Deep Learning - TensorFlow 2.0_ Introduction/008 Customizing a TensorFlow 2 Model.mp4 16.8 MB
36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip.mp4 16.8 MB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model.mp4 16.6 MB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/004 TensorFlow Intro.mp4 16.6 MB
29 - Python - Iterations/006 How to Iterate over Dictionaries.mp4 16.5 MB
08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions.mp4 16.4 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression.mp4 16.4 MB
42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm_ n-Parameter Gradient Descent.mp4 16.4 MB
11 - Probability - Bayesian Inference/007 The Conditional Probability Formula.mp4 16.3 MB
21 - Statistics - Practical Example_ Hypothesis Testing/001 Practical Example_ Hypothesis Testing.mp4 16.3 MB
42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs.mp4 16.2 MB
10 - Probability - Combinatorics/009 Combinatorics in Real-Life_ The Lottery.mp4 16.2 MB
17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution.mp4 16.2 MB
17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates.mp4 16.1 MB
12 - Probability - Distributions/014 Continuous Distributions_ The Logistic Distribution.mp4 15.9 MB
57 - Case Study - What's Next in the Course_/001 Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 15.8 MB
12 - Probability - Distributions/013 Continuous Distributions_ The Exponential Distribution.mp4 15.8 MB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/008 Basic NN Example with TF_ Loss Function and Gradient Descent.mp4 15.7 MB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3).mp4 15.7 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression).mp4 15.7 MB
52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches.mp4 15.7 MB
64 - Bonus Lecture/35215106-365-Data-Science-Data-Science-Interview-Questions-Guide.pdf 15.6 MB
63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas.mp4 15.4 MB
22 - Part 4_ Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks.mp4 15.4 MB
57 - Case Study - What's Next in the Course_/003 Introducing the Data Set.mp4 15.3 MB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2).mp4 15.2 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression.mp4 15.2 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data.mp4 15.1 MB
44 - Deep Learning - TensorFlow 2.0_ Introduction/003 TensorFlow 1 vs TensorFlow 2.mp4 14.9 MB
44 - Deep Learning - TensorFlow 2.0_ Introduction/002 TensorFlow Outline and Comparison with Other Libraries.mp4 14.9 MB
12 - Probability - Distributions/005 Discrete Distributions_ The Bernoulli Distribution.mp4 14.8 MB
10 - Probability - Combinatorics/005 Solving Variations without Repetition.mp4 14.8 MB
12 - Probability - Distributions/007 Discrete Distributions_ The Poisson Distribution.mp4 14.6 MB
29 - Python - Iterations/003 Lists with the range() Function.mp4 14.5 MB
22 - Part 4_ Introduction to Python/001 Introduction to Programming.mp4 14.3 MB
13 - Probability - Probability in Other Fields/002 Probability in Statistics.mp4 14.3 MB
26 - Python - Conditional Statements/003 The ELIF Statement.mp4 14.2 MB
10 - Probability - Combinatorics/003 Simple Operations with Factorials.mp4 14.0 MB
10 - Probability - Combinatorics/002 Permutations and How to Use Them.mp4 14.0 MB
05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data.mp4 13.9 MB
51 - Deep Learning - Business Case Example/006 Business Case_ Load the Preprocessed Data.mp4 13.8 MB
10 - Probability - Combinatorics/004 Solving Variations with Repetition.mp4 13.7 MB
44 - Deep Learning - TensorFlow 2.0_ Introduction/007 Interpreting the Result and Extracting the Weights and Bias.mp4 13.7 MB
40 - Part 6_ Mathematics/003 Linear Algebra and Geometry.mp4 13.6 MB
46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification.mp4 13.5 MB
10 - Probability - Combinatorics/007 Symmetry of Combinations.mp4 13.5 MB
17 - Statistics - Inferential Statistics Fundamentals/007 Standard error.mp4 13.3 MB
18 - Statistics - Inferential Statistics_ Confidence Intervals/005 Student's T Distribution.mp4 13.3 MB
63 - Appendix - pandas Fundamentals/006 Using .sort_values().mp4 13.2 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model.mp4 13.2 MB
01 - Part 1_ Introduction/001 A Practical Example_ What You Will Learn in This Course.mp4 13.1 MB
18 - Statistics - Inferential Statistics_ Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2).mp4 13.0 MB
36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables.mp4 12.9 MB
10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces.mp4 12.9 MB
15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table.mp4 12.8 MB
04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines.mp4 12.4 MB
62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops.mp4 12.3 MB
50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST_ Importing the Relevant Packages and Loading the Data.mp4 12.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several _Straightforward_ Columns for this Exercise.mp4 12.2 MB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/004 Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 12.0 MB
18 - Statistics - Inferential Statistics_ Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1).mp4 12.0 MB
10 - Probability - Combinatorics/010 A Recap of Combinatorics.mp4 12.0 MB
11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets.mp4 12.0 MB
49 - Deep Learning - Preprocessing/003 Standardization.mp4 12.0 MB
22 - Part 4_ Introduction to Python/002 Why Python_.mp4 11.8 MB
40 - Part 6_ Mathematics/001 What is a Matrix_.mp4 11.7 MB
40 - Part 6_ Mathematics/005 What is a Tensor_.mp4 11.6 MB
18 - Statistics - Inferential Statistics_ Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score.mp4 11.6 MB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/005 MNIST_ Loss and Optimization Algorithm.mp4 11.6 MB
09 - Part 2_ Probability/004 Events and Their Complements.mp4 11.4 MB
11 - Probability - Bayesian Inference/008 The Law of Total Probability.mp4 11.4 MB
36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean.mp4 11.4 MB
40 - Part 6_ Mathematics/009 Dot Product.mp4 11.4 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset.mp4 11.1 MB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/002 What is a Deep Net_.mp4 11.1 MB
12 - Probability - Distributions/012 Continuous Distributions_ The Chi-Squared Distribution.mp4 11.0 MB
38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering.mp4 10.9 MB
11 - Probability - Bayesian Inference/009 The Additive Rule.mp4 10.9 MB
14 - Part 3_ Statistics/001 Population and Sample.mp4 10.9 MB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau.mp4 10.9 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared.mp4 10.8 MB
37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis.mp4 10.7 MB
50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST_ Select the Loss and the Optimizer.mp4 10.7 MB
63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I.mp4 10.6 MB
38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering.mp4 10.5 MB
38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize.mp4 10.5 MB
46 - Deep Learning - Overfitting/001 What is Overfitting_.mp4 10.5 MB
42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks.mp4 10.4 MB
38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data.mp4 10.3 MB
12 - Probability - Distributions/004 Discrete Distributions_ The Uniform Distribution.mp4 10.1 MB
15 - Statistics - Descriptive Statistics/013 Skewness.mp4 9.9 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise.mp4 9.9 MB
42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning.mp4 9.8 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn.mp4 9.8 MB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/004 Non-Linearities and their Purpose.mp4 9.7 MB
42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions_ Cross-Entropy Loss.mp4 9.7 MB
52 - Deep Learning - Conclusion/001 Summary on What You've Learned.mp4 9.7 MB
37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering.mp4 9.5 MB
28 - Python - Sequences/004 Tuples.mp4 9.5 MB
07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for.mp4 9.5 MB
56 - Software Integration/004 Communication between Software Products through Text Files.mp4 9.3 MB
12 - Probability - Distributions/003 Characteristics of Discrete Distributions.mp4 9.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the _Date_ Column.mp4 9.1 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2_ No Endogeneity.mp4 9.0 MB
49 - Deep Learning - Preprocessing/001 Preprocessing Introduction.mp4 9.0 MB
23 - Python - Variables and Data Types/001 Variables.mp4 8.9 MB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/006 Types of File Formats, supporting Tensors.mp4 8.9 MB
11 - Probability - Bayesian Inference/003 Intersection of Sets.mp4 8.8 MB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/007 MNIST_ Batching and Early Stopping.mp4 8.7 MB
12 - Probability - Distributions/17971238-FIFA19.csv 8.6 MB
12 - Probability - Distributions/17971248-FIFA19-post.csv 8.6 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability.mp4 8.6 MB
17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution.mp4 8.6 MB
36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression.mp4 8.6 MB
30 - Python - Advanced Python Tools/004 Importing Modules in Python.mp4 8.5 MB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/005 Activation Functions.mp4 8.5 MB
27 - Python - Python Functions/007 Built-in Functions in Python.mp4 8.5 MB
46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training.mp4 8.5 MB
30 - Python - Advanced Python Tools/001 Object Oriented Programming.mp4 8.4 MB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/006 Activation Functions_ Softmax Activation.mp4 8.4 MB
40 - Part 6_ Mathematics/002 Scalars and Vectors.mp4 8.4 MB
60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I.mp4 8.4 MB
49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding.mp4 8.4 MB
27 - Python - Python Functions/002 How to Create a Function with a Parameter.mp4 8.3 MB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/006 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 8.2 MB
51 - Deep Learning - Business Case Example/011 Business Case_ Testing the Model.mp4 8.2 MB
46 - Deep Learning - Overfitting/003 What is Validation_.mp4 8.1 MB
02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics.mp4 8.0 MB
22 - Part 4_ Introduction to Python/003 Why Jupyter_.mp4 8.0 MB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/003 MNIST_ Relevant Packages.mp4 7.9 MB
42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version).mp4 7.9 MB
62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects.mp4 7.8 MB
42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs.mp4 7.8 MB
46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets.mp4 7.7 MB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST.mp4 7.7 MB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/008 Backpropagation Picture.mp4 7.7 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4_ No Autocorrelation.mp4 7.7 MB
50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST.mp4 7.7 MB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent.mp4 7.6 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/29545318-Absenteeism-Exercise-Preprocessing-LECTURES.ipynb 7.6 MB
39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering.mp4 7.6 MB
20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1).mp4 7.6 MB
42 - Deep Learning - Introduction to Neural Networks/002 Training the Model.mp4 7.6 MB
41 - Part 7_ Deep Learning/001 What to Expect from this Part_.mp4 7.6 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/007 Using Seaborn for Graphs.mp4 7.4 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5_ No Multicollinearity.mp4 7.4 MB
24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python.mp4 7.3 MB
36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting.mp4 7.3 MB
44 - Deep Learning - TensorFlow 2.0_ Introduction/005 Types of File Formats Supporting TensorFlow.mp4 7.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/020 Reordering Columns in a Pandas DataFrame in Python.mp4 7.2 MB
02 - The Field of Data Science - The Various Data Science Disciplines/13075166-365-DataScience.png 6.9 MB
02 - The Field of Data Science - The Various Data Science Disciplines/13075168-365-DataScience.png 6.9 MB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/004 Practical Example_ Linear Regression (Part 3).mp4 6.9 MB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation).mp4 6.9 MB
57 - Case Study - What's Next in the Course_/002 The Business Task.mp4 6.8 MB
52 - Deep Learning - Conclusion/005 An Overview of RNNs.mp4 6.8 MB
27 - Python - Python Functions/003 Defining a Function in Python - Part II.mp4 6.5 MB
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42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks.mp4 6.4 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages.mp4 6.2 MB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/005 Actual Introduction to TensorFlow.mp4 6.2 MB
27 - Python - Python Functions/005 Conditional Statements and Functions.mp4 6.0 MB
42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function_.mp4 6.0 MB
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63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II.mp4 5.8 MB
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33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression.mp4 5.5 MB
12 - Probability - Distributions/011 Continuous Distributions_ The Students' T Distribution.mp4 5.4 MB
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30 - Python - Advanced Python Tools/003 What is the Standard Library_.mp4 4.9 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/003 Selecting the Inputs for the Logistic Regression.mp4 4.6 MB
23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python.mp4 4.6 MB
37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites.mp4 4.5 MB
42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions_ L2-norm Loss.mp4 4.5 MB
36 - Advanced Statistical Methods - Logistic Regression/001 Introduction to Logistic Regression.mp4 4.4 MB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/010 Business Case_ Testing the Model.mp4 4.4 MB
22 - Part 4_ Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard.mp4 4.4 MB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/001 MNIST_ What is the MNIST Dataset_.mp4 4.2 MB
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47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 4.2 MB
18 - Statistics - Inferential Statistics_ Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3).mp4 4.2 MB
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34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section_.mp4 4.0 MB
15 - Statistics - Descriptive Statistics/007 The Histogram.mp4 3.9 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression.mp4 3.7 MB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/002 How to Install TensorFlow 1.mp4 3.7 MB
52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning.mp4 3.7 MB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent.mp4 3.5 MB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/001 What is a Layer_.mp4 3.5 MB
40 - Part 6_ Mathematics/007 Errors when Adding Matrices.mp4 3.3 MB
26 - Python - Conditional Statements/004 A Note on Boolean Values.mp4 3.3 MB
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27 - Python - Python Functions/001 Defining a Function in Python.mp4 3.2 MB
10 - Probability - Combinatorics/001 Fundamentals of Combinatorics.mp4 3.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/015 More on Dummy Variables_ A Statistical Perspective.mp4 3.2 MB
25 - Python - Other Python Operators/001 Comparison Operators.mp4 3.1 MB
17 - Statistics - Inferential Statistics Fundamentals/001 Introduction.mp4 2.9 MB
31 - Part 5_ Advanced Statistical Methods in Python/001 Introduction to Regression Analysis.mp4 2.9 MB
29 - Python - Iterations/005 Conditional Statements, Functions, and Loops.mp4 2.9 MB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/002 Business Case_ Outlining the Solution.mp4 2.9 MB
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32 - Advanced Statistical Methods - Linear Regression with StatsModels/003 Geometrical Representation of the Linear Regression Model.mp4 1.8 MB
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45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/13070016-Course-Notes-Section-6.pdf 936.4 KB
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11 - Probability - Bayesian Inference/17970686-CDS-2017-2018-Hamilton.pdf 845.3 KB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588630-sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb 711.0 KB
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43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/13070602-Shortcuts-for-Jupyter.pdf 619.2 KB
44 - Deep Learning - TensorFlow 2.0_ Introduction/13070604-Shortcuts-for-Jupyter.pdf 619.2 KB
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35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588618-sklearn-Linear-Regression-Practical-Example-Part-4-with-comments.ipynb 407.6 KB
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11 - Probability - Bayesian Inference/17431622-Course-Notes-Bayesian-Inference.pdf 386.0 KB
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09 - Part 2_ Probability/17431614-Course-Notes-Basic-Probability.pdf 371.1 KB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588602-sklearn-Dummies-and-VIF-Exercise-Solution.ipynb 370.2 KB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588558-sklearn-Linear-Regression-Practical-Example-Part-3-with-comments.ipynb 351.5 KB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588604-sklearn-Dummies-and-VIF-Exercise.ipynb 344.6 KB
12 - Probability - Distributions/17431628-Solving-Integrals.pdf 343.9 KB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588552-sklearn-Linear-Regression-Practical-Example-Part-3.ipynb 343.6 KB
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36 - Advanced Statistical Methods - Logistic Regression/23412976-Course-Notes-Logistic-Regression.pdf 335.2 KB
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35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588462-sklearn-Linear-Regression-Practical-Example-Part-2.ipynb 328.7 KB
02 - The Field of Data Science - The Various Data Science Disciplines/13075156-365-DataScience-Diagram.pdf 323.1 KB
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13 - Probability - Probability in Other Fields/23224540-Probability-Cheat-Sheet.pdf 320.3 KB
31 - Part 5_ Advanced Statistical Methods in Python/22685780-Course-notes-regression-analysis.pdf 312.2 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/22685784-Course-notes-regression-analysis.pdf 312.2 KB
01 - Part 1_ Introduction/16507136-FAQ-The-Data-Science-Course.pdf 306.1 KB
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10 - Probability - Combinatorics/19540858-Additional-Exercises-Combinatorics-Solutions.pdf 245.7 KB
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35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588446-1.04.Real-life-example.csv 219.8 KB
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37 - Advanced Statistical Methods - Cluster Analysis/23413656-Course-Notes-Cluster-Analysis.pdf 208.7 KB
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13 - Probability - Probability in Other Fields/19327648-Probability-in-Finance-Solutions.pdf 184.5 KB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/21993772-Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 182.4 KB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588454-sklearn-Linear-Regression-Practical-Example-Part-1-with-comments.ipynb 171.4 KB
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16 - Statistics - Practical Example_ Descriptive Statistics/13129220-2.13.Practical-example.Descriptive-statistics-lesson.xlsx 146.5 KB
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12 - Probability - Distributions/17862366-Poisson-Expected-Value-and-Variance.pdf 146.0 KB
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58 - Case Study - Preprocessing the 'Absenteeism_data'/15271322-data-preprocessing-homework.pdf 134.5 KB
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36 - Advanced Statistical Methods - Logistic Regression/29588898-Testing-the-Model-Solution.ipynb 111.1 KB
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60 - Case Study - Loading the 'absenteeism_module'/29545372-Absenteeism-Exercise-Integration.ipynb 62.4 KB
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21 - Statistics - Practical Example_ Hypothesis Testing/27047254-4.10.Hypothesis-testing-section-practical-example.xlsx 51.9 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591454-TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb 50.0 KB
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11 - Probability - Bayesian Inference/18886392-Bayesian-Homework-Solutions.pdf 30.4 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/29588416-sklearn-Making-Predictions-with-the-Standardized-Coefficients.ipynb 29.8 KB
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59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/15364076-Absenteeism-preprocessed.csv 29.1 KB
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34 - Advanced Statistical Methods - Linear Regression with sklearn/29588206-sklearn-Simple-Linear-Regression.ipynb 26.1 KB
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39 - Advanced Statistical Methods - Other Types of Clustering/29589070-Heatmaps-with-comments.ipynb 17.7 KB
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40 - Part 6_ Mathematics/011 Why is Linear Algebra Useful___en.srt 12.4 KB
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02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI__en.srt 12.0 KB
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02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science_ An Introduction__en.srt 11.0 KB
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38 - Advanced Statistical Methods - K-Means Clustering/29589052-Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb 10.7 KB
05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data__en.srt 10.7 KB
63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series__en.srt 10.7 KB
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43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4)__en.srt 10.6 KB
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34 - Advanced Statistical Methods - Linear Regression with sklearn/29588340-sklearn-Feature-Selection-with-F-regression.ipynb 10.4 KB
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38 - Advanced Statistical Methods - K-Means Clustering/15452987-Categorical.csv 10.3 KB
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58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature__en.srt 10.3 KB
18 - Statistics - Inferential Statistics_ Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score__en.srt 10.3 KB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau__en.srt 10.2 KB
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58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence__en.srt 10.1 KB
28 - Python - Sequences/001 Lists__en.srt 10.0 KB
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13 - Probability - Probability in Other Fields/001 Probability in Finance__en.srt 9.8 KB
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12 - Probability - Distributions/002 Types of Probability Distributions__en.srt 9.7 KB
12 - Probability - Distributions/17971264-Customers-Membership.xlsx 9.7 KB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau__en.srt 9.7 KB
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40 - Part 6_ Mathematics/010 Dot Product of Matrices__en.srt 9.6 KB
12 - Probability - Distributions/17971258-Daily-Views.xlsx 9.5 KB
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38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2)__en.srt 9.3 KB
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03 - The Field of Data Science - Connecting the Data Science Disciplines/001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML__en.srt 9.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/29588310-sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared.ipynb 9.1 KB
22 - Part 4_ Introduction to Python/004 Installing Python and Jupyter__en.srt 9.1 KB
20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level__en.srt 9.1 KB
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09 - Part 2_ Probability/001 The Basic Probability Formula__en.srt 9.0 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/004 MNIST_ Model Outline__en.srt 9.0 KB
05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques__en.srt 8.9 KB
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13 - Probability - Probability in Other Fields/002 Probability in Statistics__en.srt 8.6 KB
50 - Deep Learning - Classifying on the MNIST Dataset/29589878-TensorFlow-MNIST-Part3-with-comments.ipynb 8.6 KB
51 - Deep Learning - Business Case Example/29589984-TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.6 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591944-TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.6 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression__en.srt 8.6 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set__en.srt 8.5 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591520-12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.5 KB
56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints___en.srt 8.5 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/29545334-Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/29589008-How-to-Choose-the-Number-of-Clusters-Solution.ipynb 8.5 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing__en.srt 8.5 KB
28 - Python - Sequences/002 Using Methods__en.srt 8.4 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/009 MNIST_ Results and Testing__en.srt 8.3 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/29545316-Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.3 KB
20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known__en.srt 8.3 KB
62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops__en.srt 8.3 KB
36 - Advanced Statistical Methods - Logistic Regression/15452035-Bank-data-testing.csv 8.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/15453017-Countries-exercise.csv 8.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/29588950-Countries-exercise.csv 8.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering_en.vtt 8.3 KB
18 - Statistics - Inferential Statistics_ Confidence Intervals/009 Confidence intervals. Two means. Dependent samples__en.srt 8.2 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables__en.srt 8.2 KB
63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[]__en.srt 8.1 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem__en.srt 8.0 KB
29 - Python - Iterations/003 Lists with the range() Function__en.srt 8.0 KB
62 - Appendix - Additional Python Tools/004 Triple Nested For Loops__en.srt 8.0 KB
44 - Deep Learning - TensorFlow 2.0_ Introduction/006 Outlining the Model with TensorFlow 2__en.srt 8.0 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python__en.srt 7.9 KB
51 - Deep Learning - Business Case Example/009 Business Case_ Setting an Early Stopping Mechanism__en.srt 7.9 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the _Date_ Column__en.srt 7.9 KB
12 - Probability - Distributions/001 Fundamentals of Probability Distributions__en.srt 7.9 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591514-12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 7.9 KB
15 - Statistics - Descriptive Statistics/015 Variance__en.srt 7.9 KB
50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST_ Learning__en.srt 7.9 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/29588244-sklearn-Multiple-Linear-Regression.ipynb 7.8 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization)__en.srt 7.8 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/006 Creating a Data Provider__en.srt 7.8 KB
42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm_ n-Parameter Gradient Descent__en.srt 7.8 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python__en.srt 7.8 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/009 Basic NN Example with TF_ Model Output__en.srt 7.7 KB
29 - Python - Iterations/004 Conditional Statements and Loops__en.srt 7.7 KB
22 - Part 4_ Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks__en.srt 7.7 KB
63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II__en.srt 7.6 KB
29 - Python - Iterations/006 How to Iterate over Dictionaries__en.srt 7.6 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared__en.srt 7.6 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/007 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases__en.srt 7.6 KB
36 - Advanced Statistical Methods - Logistic Regression/29588876-Testing-the-model-with-comments.ipynb 7.6 KB
23 - Python - Variables and Data Types/29544578-Strings-Lecture-Py3.ipynb 7.6 KB
11 - Probability - Bayesian Inference/011 Bayes' Law__en.srt 7.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/29589000-Selecting-the-number-of-clusters-with-comments.ipynb 7.5 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights__en.srt 7.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters__en.srt 7.4 KB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau__en.srt 7.4 KB
38 - Advanced Statistical Methods - K-Means Clustering/29589048-Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb 7.4 KB
06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science__en.srt 7.3 KB
20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis__en.srt 7.3 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/29545314-Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1)__en.srt 7.3 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591504-12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.3 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression__en.srt 7.3 KB
39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram__en.srt 7.3 KB
50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST_ Outline the Model__en.srt 7.3 KB
28 - Python - Sequences/004 Tuples__en.srt 7.2 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/29588436-sklearn-Train-Test-Split.ipynb 7.2 KB
09 - Part 2_ Probability/004 Events and Their Complements__en.srt 7.2 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set__en.srt 7.1 KB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2)_en.vtt 7.1 KB
23 - Python - Variables and Data Types/003 Python Strings__en.srt 7.1 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588120-Dummy-variables-with-comments.ipynb 7.1 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/007 Business Case_ Model Outline__en.srt 7.1 KB
63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I__en.srt 7.1 KB
22 - Part 4_ Introduction to Python/001 Introduction to Programming__en.srt 6.9 KB
63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I__en.srt 6.9 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2)__en.srt 6.9 KB
56 - Software Integration/005 Software Integration - Explained__en.srt 6.9 KB
22 - Part 4_ Introduction to Python/002 Why Python___en.srt 6.8 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model__en.srt 6.8 KB
38 - Advanced Statistical Methods - K-Means Clustering/29589038-Market-segmentation-example-Part2-with-comments.ipynb 6.8 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589230-Minimal-example-Part-3.ipynb 6.8 KB
46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training__en.srt 6.8 KB
36 - Advanced Statistical Methods - Logistic Regression/29588894-Testing-the-Model-Exercise.ipynb 6.8 KB
20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples__en.srt 6.8 KB
50 - Deep Learning - Classifying on the MNIST Dataset/29589956-TensorFlow-MNIST-complete.ipynb 6.8 KB
02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords_ Why are there so Many___en.srt 6.8 KB
60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II_en.vtt 6.8 KB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/003 Digging into a Deep Net__en.srt 6.7 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression)__en.srt 6.7 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared__en.srt 6.7 KB
09 - Part 2_ Probability/002 Computing Expected Values__en.srt 6.7 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn_en.vtt 6.7 KB
52 - Deep Learning - Conclusion/004 An overview of CNNs__en.srt 6.7 KB
15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots__en.srt 6.7 KB
29 - Python - Iterations/001 For Loops__en.srt 6.7 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept__en.srt 6.7 KB
13 - Probability - Probability in Other Fields/003 Probability in Data Science__en.srt 6.6 KB
15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation__en.srt 6.6 KB
60 - Case Study - Loading the 'absenteeism_module'/29545374-absenteeism-module.py 6.6 KB
50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST_ Preprocess the Data - Create a Validation Set and Scale It__en.srt 6.6 KB
26 - Python - Conditional Statements/003 The ELIF Statement__en.srt 6.6 KB
12 - Probability - Distributions/007 Discrete Distributions_ The Poisson Distribution__en.srt 6.6 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn__en.srt 6.6 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3_ Normality and Homoscedasticity__en.srt 6.6 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table__en.srt 6.6 KB
04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines__en.srt 6.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful___en.srt 6.5 KB
44 - Deep Learning - TensorFlow 2.0_ Introduction/001 How to Install TensorFlow 2.0__en.srt 6.5 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created__en.srt 6.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering__en.srt 6.5 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/008 Business Case_ Optimization__en.srt 6.5 KB
36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model__en.srt 6.5 KB
15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques__en.srt 6.4 KB
01 - Part 1_ Introduction/001 A Practical Example_ What You Will Learn in This Course__en.srt 6.4 KB
50 - Deep Learning - Classifying on the MNIST Dataset/29589876-TensorFlow-MNIST-Part2-with-comments.ipynb 6.4 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy_en.vtt 6.3 KB
09 - Part 2_ Probability/003 Frequency__en.srt 6.3 KB
51 - Deep Learning - Business Case Example/008 Business Case_ Learning and Interpreting the Result__en.srt 6.3 KB
18 - Statistics - Inferential Statistics_ Confidence Intervals/008 Margin of Error__en.srt 6.3 KB
44 - Deep Learning - TensorFlow 2.0_ Introduction/007 Interpreting the Result and Extracting the Weights and Bias__en.srt 6.3 KB
63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes__en.srt 6.3 KB
39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps__en.srt 6.2 KB
36 - Advanced Statistical Methods - Logistic Regression/15451783-Example-bank-data.csv 6.2 KB
49 - Deep Learning - Preprocessing/003 Standardization__en.srt 6.2 KB
20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown__en.srt 6.2 KB
37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters__en.srt 6.2 KB
29 - Python - Iterations/002 While Loops and Incrementing__en.srt 6.2 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence__en.srt 6.2 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29590046-5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.2 KB
28 - Python - Sequences/29544994-Dictionaries-Solution-Py3.ipynb 6.2 KB
15 - Statistics - Descriptive Statistics/001 Types of Data__en.srt 6.1 KB
17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution__en.srt 6.1 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/004 Learning Rate Schedules, or How to Choose the Optimal Learning Rate__en.srt 6.1 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591494-12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.1 KB
30 - Python - Advanced Python Tools/001 Object Oriented Programming__en.srt 6.1 KB
18 - Statistics - Inferential Statistics_ Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1)__en.srt 6.1 KB
42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks__en.srt 6.1 KB
11 - Probability - Bayesian Inference/004 Union of Sets__en.srt 6.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table_en.vtt 6.1 KB
38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize__en.srt 6.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/29588434-sklearn-Feature-Scaling-Exercise.ipynb 6.1 KB
50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST_ Testing the Model__en.srt 6.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/29588166-sklearn-Simple-Linear-Regression-with-comments.ipynb 6.1 KB
62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects__en.srt 6.0 KB
40 - Part 6_ Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices__en.srt 6.0 KB
15 - Statistics - Descriptive Statistics/011 Mean, median and mode__en.srt 6.0 KB
25 - Python - Other Python Operators/002 Logical and Identity Operators__en.srt 5.9 KB
56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses__en.srt 5.9 KB
36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python__en.srt 5.9 KB
38 - Advanced Statistical Methods - K-Means Clustering/29589022-Market-segmentation-example-with-comments.ipynb 5.9 KB
25 - Python - Other Python Operators/29544754-Logical-and-Identity-Operators-Lecture-Py3.ipynb 5.9 KB
25 - Python - Other Python Operators/29544770-Logical-and-Identity-Operators-Lecture-Py3.ipynb 5.9 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients__en.srt 5.8 KB
18 - Statistics - Inferential Statistics_ Confidence Intervals/004 Confidence Interval Clarifications__en.srt 5.8 KB
38 - Advanced Statistical Methods - K-Means Clustering/29588940-Country-clusters-with-comments.ipynb 5.8 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588138-Making-predictions.ipynb 5.8 KB
36 - Advanced Statistical Methods - Logistic Regression/29588864-Testing-the-model.ipynb 5.8 KB
05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data__en.srt 5.7 KB
46 - Deep Learning - Overfitting/001 What is Overfitting___en.srt 5.7 KB
10 - Probability - Combinatorics/006 Solving Combinations__en.srt 5.7 KB
40 - Part 6_ Mathematics/008 Transpose of a Matrix__en.srt 5.7 KB
36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression__en.srt 5.7 KB
17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem__en.srt 5.7 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/29588382-sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.7 KB
18 - Statistics - Inferential Statistics_ Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score__en.srt 5.7 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on _Education_, _Children_, and _Pets___en.srt 5.6 KB
42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions_ Cross-Entropy Loss__en.srt 5.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/29588968-Categorical-data-with-comments.ipynb 5.6 KB
14 - Part 3_ Statistics/001 Population and Sample__en.srt 5.6 KB
63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique()__en.srt 5.6 KB
51 - Deep Learning - Business Case Example/29589970-TensorFlow-Audiobooks-Preprocessing.ipynb 5.6 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591734-TensorFlow-Audiobooks-Preprocessing.ipynb 5.6 KB
20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1)__en.srt 5.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/29589006-How-to-Choose-the-Number-of-Clusters-Exercise.ipynb 5.5 KB
63 - Appendix - pandas Fundamentals/006 Using .sort_values()__en.srt 5.5 KB
57 - Case Study - What's Next in the Course_/001 Game Plan for this Python, SQL, and Tableau Business Exercise__en.srt 5.5 KB
27 - Python - Python Functions/29544926-Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb 5.5 KB
36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables__en.srt 5.5 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module__en.srt 5.5 KB
63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[]__en.srt 5.5 KB
56 - Software Integration/004 Communication between Software Products through Text Files__en.srt 5.5 KB
63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas__en.srt 5.5 KB
20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2)__en.srt 5.5 KB
23 - Python - Variables and Data Types/29544586-Strings-Solution-Py3.ipynb 5.5 KB
08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions__en.srt 5.4 KB
12 - Probability - Distributions/014 Continuous Distributions_ The Logistic Distribution__en.srt 5.4 KB
42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs__en.srt 5.4 KB
12 - Probability - Distributions/010 Continuous Distributions_ The Standard Normal Distribution__en.srt 5.4 KB
36 - Advanced Statistical Methods - Logistic Regression/29588854-Calculating-the-Accuracy-of-the-Model-Exercise.ipynb 5.4 KB
11 - Probability - Bayesian Inference/001 Sets and Events__en.srt 5.4 KB
20 - Statistics - Hypothesis Testing/007 p-value__en.srt 5.4 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model__en.srt 5.3 KB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/005 Activation Functions__en.srt 5.3 KB
36 - Advanced Statistical Methods - Logistic Regression/29588644-Admittance-with-comments.ipynb 5.3 KB
42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning__en.srt 5.3 KB
52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches__en.srt 5.3 KB
11 - Probability - Bayesian Inference/007 The Conditional Probability Formula__en.srt 5.3 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2_ No Endogeneity__en.srt 5.3 KB
20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error__en.srt 5.3 KB
52 - Deep Learning - Conclusion/001 Summary on What You've Learned__en.srt 5.3 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python__en.srt 5.2 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/006 Adaptive Learning Rate Schedules (AdaGrad and RMSprop )__en.srt 5.2 KB
28 - Python - Sequences/003 List Slicing__en.srt 5.2 KB
02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic__en.srt 5.2 KB
01 - Part 1_ Introduction/002 What Does the Course Cover__en.srt 5.1 KB
36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean__en.srt 5.1 KB
02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics__en.srt 5.0 KB
28 - Python - Sequences/29544952-List-Slicing-Lecture-Py3.ipynb 5.0 KB
17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution__en.srt 5.0 KB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2)__en.srt 5.0 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler)__en.srt 5.0 KB
15 - Statistics - Descriptive Statistics/019 Covariance__en.srt 5.0 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment_en.vtt 5.0 KB
12 - Probability - Distributions/009 Continuous Distributions_ The Normal Distribution__en.srt 4.9 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/29588164-sklearn-Simple-Linear-Regression.ipynb 4.9 KB
38 - Advanced Statistical Methods - K-Means Clustering/29588986-Clustering-Categorical-Data-Solution.ipynb 4.9 KB
46 - Deep Learning - Overfitting/003 What is Validation___en.srt 4.9 KB
36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function__en.srt 4.9 KB
36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting__en.srt 4.9 KB
60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I__en.srt 4.9 KB
51 - Deep Learning - Business Case Example/006 Business Case_ Load the Preprocessed Data__en.srt 4.8 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4_ No Autocorrelation__en.srt 4.8 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/29545298-Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb 4.8 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework__en.srt 4.8 KB
18 - Statistics - Inferential Statistics_ Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2)__en.srt 4.8 KB
36 - Advanced Statistical Methods - Logistic Regression/29588700-Understanding-Logistic-Regression-Tables-Solution.ipynb 4.8 KB
49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding__en.srt 4.8 KB
37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis__en.srt 4.8 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/008 Basic NN Example with TF_ Loss Function and Gradient Descent__en.srt 4.8 KB
15 - Statistics - Descriptive Statistics/021 Correlation Coefficient__en.srt 4.7 KB
39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering__en.srt 4.7 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent__en.srt 4.7 KB
10 - Probability - Combinatorics/005 Solving Variations without Repetition__en.srt 4.7 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation_en.vtt 4.7 KB
44 - Deep Learning - TensorFlow 2.0_ Introduction/002 TensorFlow Outline and Comparison with Other Libraries_en.vtt 4.7 KB
51 - Deep Learning - Business Case Example/003 Business Case_ Balancing the Dataset__en.srt 4.7 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/003 The Importance of Working with a Balanced Dataset__en.srt 4.7 KB
15 - Statistics - Descriptive Statistics/002 Levels of Measurement__en.srt 4.7 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5_ No Multicollinearity__en.srt 4.7 KB
38 - Advanced Statistical Methods - K-Means Clustering/29589036-Market-segmentation-example-Part2.ipynb 4.7 KB
11 - Probability - Bayesian Inference/010 The Multiplication Law__en.srt 4.7 KB
38 - Advanced Statistical Methods - K-Means Clustering/29588954-A-Simple-Example-of-Clustering-Solution.ipynb 4.6 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework_en.vtt 4.6 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset__en.srt 4.6 KB
22 - Part 4_ Introduction to Python/003 Why Jupyter___en.srt 4.6 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588094-Dummy-Variables.ipynb 4.6 KB
51 - Deep Learning - Business Case Example/29590000-TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb 4.6 KB
41 - Part 7_ Deep Learning/001 What to Expect from this Part___en.srt 4.6 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/004 TensorFlow Intro_en.vtt 4.6 KB
28 - Python - Sequences/29544978-Tuples-Solution-Py3.ipynb 4.6 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model_en.vtt 4.6 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation__en.srt 4.6 KB
40 - Part 6_ Mathematics/29589122-Scalars-Vectors-and-Matrices.ipynb 4.5 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression__en.srt 4.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/29588998-Selecting-the-number-of-clusters.ipynb 4.5 KB
23 - Python - Variables and Data Types/001 Variables__en.srt 4.5 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1)__en.srt 4.5 KB
27 - Python - Python Functions/29544922-Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb 4.5 KB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/006 Activation Functions_ Softmax Activation__en.srt 4.5 KB
36 - Advanced Statistical Methods - Logistic Regression/29588832-Binary-Predictors-in-a-Logistic-Regression-Solution.ipynb 4.5 KB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/007 Backpropagation__en.srt 4.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/29589044-Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb 4.5 KB
30 - Python - Advanced Python Tools/004 Importing Modules in Python__en.srt 4.4 KB
36 - Advanced Statistical Methods - Logistic Regression/29588678-Building-a-Logistic-Regression-Solution.ipynb 4.4 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several _Straightforward_ Columns for this Exercise__en.srt 4.4 KB
40 - Part 6_ Mathematics/001 What is a Matrix___en.srt 4.4 KB
42 - Deep Learning - Introduction to Neural Networks/002 Training the Model__en.srt 4.4 KB
36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model__en.srt 4.4 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the _Date_ Column__en.srt 4.4 KB
11 - Probability - Bayesian Inference/002 Ways Sets Can Interact__en.srt 4.4 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3)__en.srt 4.4 KB
28 - Python - Sequences/29544938-Help-Yourself-with-Methods-Lecture-Py3.ipynb 4.4 KB
15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table__en.srt 4.4 KB
28 - Python - Sequences/29544988-Dictionaries-Lecture-Py3.ipynb 4.4 KB
07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for__en.srt 4.3 KB
40 - Part 6_ Mathematics/009 Dot Product__en.srt 4.3 KB
27 - Python - Python Functions/002 How to Create a Function with a Parameter__en.srt 4.3 KB
12 - Probability - Distributions/005 Discrete Distributions_ The Bernoulli Distribution__en.srt 4.3 KB
27 - Python - Python Functions/007 Built-in Functions in Python__en.srt 4.3 KB
46 - Deep Learning - Overfitting/005 N-Fold Cross Validation__en.srt 4.3 KB
24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python__en.srt 4.3 KB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/008 Backpropagation Picture__en.srt 4.3 KB
28 - Python - Sequences/29544960-List-Slicing-Solution-Py3.ipynb 4.3 KB
18 - Statistics - Inferential Statistics_ Confidence Intervals/005 Student's T Distribution__en.srt 4.2 KB
24 - Python - Basic Python Syntax/29544620-Arithmetic-Operators-Solution-Py3.ipynb 4.2 KB
10 - Probability - Combinatorics/002 Permutations and How to Use Them__en.srt 4.2 KB
10 - Probability - Combinatorics/007 Symmetry of Combinations__en.srt 4.2 KB
12 - Probability - Distributions/013 Continuous Distributions_ The Exponential Distribution__en.srt 4.2 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability__en.srt 4.2 KB
44 - Deep Learning - TensorFlow 2.0_ Introduction/008 Customizing a TensorFlow 2 Model__en.srt 4.2 KB
35 - Advanced Statistical Methods - Practical Example_ Linear Regression/004 Practical Example_ Linear Regression (Part 3)__en.srt 4.2 KB
37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites__en.srt 4.2 KB
10 - Probability - Combinatorics/009 Combinatorics in Real-Life_ The Lottery__en.srt 4.2 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data__en.srt 4.2 KB
17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution__en.srt 4.2 KB
57 - Case Study - What's Next in the Course_/003 Introducing the Data Set__en.srt 4.1 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/29545338-Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb 4.1 KB
36 - Advanced Statistical Methods - Logistic Regression/29588660-Admittance-regression-tables-fixed-error.ipynb 4.1 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings__en.srt 4.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/33130182-Simple-Linear-Regression-with-sklearn-Exercise.ipynb 4.1 KB
40 - Part 6_ Mathematics/003 Linear Algebra and Geometry__en.srt 4.1 KB
38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering_en.vtt 4.1 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/29588016-Simple-linear-regression-with-comments.ipynb 4.1 KB
40 - Part 6_ Mathematics/006 Addition and Subtraction of Matrices__en.srt 4.0 KB
50 - Deep Learning - Classifying on the MNIST Dataset/29589868-TensorFlow-MNIST-Part1-with-comments.ipynb 4.0 KB
42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version)__en.srt 3.9 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591484-12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb 3.9 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python__en.srt 3.9 KB
17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates__en.srt 3.9 KB
40 - Part 6_ Mathematics/002 Scalars and Vectors__en.srt 3.8 KB
47 - Deep Learning - Initialization/002 Types of Simple Initializations__en.srt 3.8 KB
10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces__en.srt 3.8 KB
49 - Deep Learning - Preprocessing/001 Preprocessing Introduction__en.srt 3.8 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn_en.vtt 3.8 KB
38 - Advanced Statistical Methods - K-Means Clustering/29589020-Market-segmentation-example.ipynb 3.8 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS___en.srt 3.8 KB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/004 Non-Linearities and their Purpose__en.srt 3.8 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/29587976-Simple-linear-regression.ipynb 3.8 KB
23 - Python - Variables and Data Types/29544612-Variables-Solution-Py3.ipynb 3.8 KB
38 - Advanced Statistical Methods - K-Means Clustering/29588982-Clustering-Categorical-Data-Exercise.ipynb 3.8 KB
50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST__en.srt 3.8 KB
52 - Deep Learning - Conclusion/005 An Overview of RNNs__en.srt 3.8 KB
44 - Deep Learning - TensorFlow 2.0_ Introduction/003 TensorFlow 1 vs TensorFlow 2__en.srt 3.8 KB
10 - Probability - Combinatorics/010 A Recap of Combinatorics__en.srt 3.7 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST__en.srt 3.7 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter__en.srt 3.7 KB
57 - Case Study - What's Next in the Course_/002 The Business Task__en.srt 3.7 KB
22 - Part 4_ Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard__en.srt 3.7 KB
40 - Part 6_ Mathematics/005 What is a Tensor___en.srt 3.7 KB
47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization__en.srt 3.7 KB
30 - Python - Advanced Python Tools/003 What is the Standard Library___en.srt 3.7 KB
27 - Python - Python Functions/29544924-Notable-Built-In-Functions-in-Python-Exercise-Py3.ipynb 3.7 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589218-Minimal-example-Part-2.ipynb 3.7 KB
10 - Probability - Combinatorics/004 Solving Variations with Repetition__en.srt 3.6 KB
15 - Statistics - Descriptive Statistics/013 Skewness__en.srt 3.6 KB
36 - Advanced Statistical Methods - Logistic Regression/29588838-Accuracy.ipynb 3.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/15453059-iris-with-answers.csv 3.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/29588952-A-Simple-Example-of-Clustering-Exercise.ipynb 3.6 KB
23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python__en.srt 3.6 KB
27 - Python - Python Functions/005 Conditional Statements and Functions__en.srt 3.6 KB
23 - Python - Variables and Data Types/29544526-Variables-Lecture-Py3.ipynb 3.6 KB
40 - Part 6_ Mathematics/29589194-Dot-product-Part-2.ipynb 3.6 KB
63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II__en.srt 3.6 KB
47 - Deep Learning - Initialization/001 What is Initialization___en.srt 3.6 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/005 MNIST_ Loss and Optimization Algorithm__en.srt 3.6 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum__en.srt 3.6 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/29588024-Simple-Linear-Regression-Exercise-Solution.ipynb 3.6 KB
44 - Deep Learning - TensorFlow 2.0_ Introduction/005 Types of File Formats Supporting TensorFlow__en.srt 3.6 KB
10 - Probability - Combinatorics/003 Simple Operations with Factorials__en.srt 3.6 KB
36 - Advanced Statistical Methods - Logistic Regression/29588642-Admittance.ipynb 3.5 KB
26 - Python - Conditional Statements/001 The IF Statement__en.srt 3.5 KB
05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods__en.srt 3.5 KB
24 - Python - Basic Python Syntax/29544616-Arithmetic-Operators-Lecture-Py3.ipynb 3.5 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/001 MNIST_ What is the MNIST Dataset___en.srt 3.5 KB
50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST_ The Dataset__en.srt 3.5 KB
11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets__en.srt 3.5 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting__en.srt 3.4 KB
36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression__en.srt 3.4 KB
25 - Python - Other Python Operators/29544776-Logical-and-Identity-Operators-Solution-Py3.ipynb 3.4 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation)__en.srt 3.4 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/003 Selecting the Inputs for the Logistic Regression__en.srt 3.4 KB
46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets__en.srt 3.4 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588130-real-estate-price-size-year-view.csv 3.4 KB
11 - Probability - Bayesian Inference/008 The Law of Total Probability__en.srt 3.4 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages__en.srt 3.4 KB
23 - Python - Variables and Data Types/29544572-Numbers-and-Boolean-Values-Lecture-Py3.ipynb 3.4 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29590038-5.3.TensorFlow-Minimal-example-Part-1.ipynb 3.4 KB
38 - Advanced Statistical Methods - K-Means Clustering/29588960-Categorical-data.ipynb 3.3 KB
37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering__en.srt 3.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data__en.srt 3.3 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression__en.srt 3.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/29588936-Country-clusters.ipynb 3.3 KB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/002 What is a Deep Net___en.srt 3.3 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/006 Types of File Formats, supporting Tensors__en.srt 3.3 KB
27 - Python - Python Functions/29544866-Another-Way-to-Define-a-Function-Lecture-Py3.ipynb 3.3 KB
18 - Statistics - Inferential Statistics_ Confidence Intervals/001 What are Confidence Intervals___en.srt 3.3 KB
26 - Python - Conditional Statements/29544814-Else-If-for-Brief-Elif-Lecture-Py3.ipynb 3.2 KB
23 - Python - Variables and Data Types/29544594-Numbers-and-Boolean-Values-Solution-Py3.ipynb 3.2 KB
40 - Part 6_ Mathematics/29589134-Adding-and-subtracting-matrices.ipynb 3.2 KB
42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs__en.srt 3.2 KB
50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST_ Select the Loss and the Optimizer__en.srt 3.2 KB
28 - Python - Sequences/29544932-Lists-Solution-Py3.ipynb 3.2 KB
40 - Part 6_ Mathematics/29589174-Errors-when-adding-scalars-vectors-and-matrices-in-Python.ipynb 3.2 KB
36 - Advanced Statistical Methods - Logistic Regression/29588694-Understanding-Logistic-Regression-Tables-Exercise.ipynb 3.2 KB
36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip__en.srt 3.1 KB
15 - Statistics - Descriptive Statistics/007 The Histogram__en.srt 3.1 KB
26 - Python - Conditional Statements/002 The ELSE Statement__en.srt 3.1 KB
24 - Python - Basic Python Syntax/29544648-Reassign-Values-Lecture-Py3.ipynb 3.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/012 Creating a Summary Table with P-values__en.srt 3.0 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588128-Multiple-Linear-Regression-with-Dummies-Exercise.ipynb 3.0 KB
50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST_ Importing the Relevant Packages and Loading the Data__en.srt 3.0 KB
12 - Probability - Distributions/011 Continuous Distributions_ The Students' T Distribution__en.srt 3.0 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions__en.srt 3.0 KB
29 - Python - Iterations/29545074-Use-Conditional-Statements-and-Loops-Together-Solution-Py3.ipynb 3.0 KB
05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML)__en.srt 3.0 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise__en.srt 2.9 KB
28 - Python - Sequences/29544992-Dictionaries-Exercise-Py3.ipynb 2.9 KB
36 - Advanced Statistical Methods - Logistic Regression/29588676-Building-a-Logistic-Regression-Exercise.ipynb 2.9 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent__en.srt 2.9 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/009 Business Case_ Interpretation__en.srt 2.9 KB
26 - Python - Conditional Statements/004 A Note on Boolean Values__en.srt 2.9 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/002 How to Install TensorFlow 1_en.vtt 2.9 KB
28 - Python - Sequences/29544972-Tuples-Lecture-Py3.ipynb 2.9 KB
12 - Probability - Distributions/012 Continuous Distributions_ The Chi-Squared Distribution__en.srt 2.9 KB
40 - Part 6_ Mathematics/29589180-Tranpose-of-a-matrix.ipynb 2.9 KB
27 - Python - Python Functions/003 Defining a Function in Python - Part II__en.srt 2.9 KB
29 - Python - Iterations/29545120-Iterating-over-Dictionaries-Solution-Py3.ipynb 2.9 KB
49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data__en.srt 2.9 KB
64 - Bonus Lecture/001 Bonus Lecture_ Next Steps.html 2.8 KB
12 - Probability - Distributions/004 Discrete Distributions_ The Uniform Distribution__en.srt 2.8 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/007 MNIST_ Batching and Early Stopping__en.srt 2.8 KB
42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks__en.srt 2.8 KB
28 - Python - Sequences/29544946-Help-Yourself-with-Methods-Solution-Py3.ipynb 2.8 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/005 What's Regression Analysis - a Quick Refresher.html 2.8 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588066-Multiple-linear-regression-and-Adjusted-R-squared-with-comments.ipynb 2.8 KB
11 - Probability - Bayesian Inference/009 The Additive Rule__en.srt 2.8 KB
28 - Python - Sequences/29544956-List-Slicing-Exercise-Py3.ipynb 2.8 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/29588026-Simple-Linear-Regression-Exercise.ipynb 2.8 KB
42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions_ L2-norm Loss__en.srt 2.8 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/002 How to Install TensorFlow 1__en.srt 2.7 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/010 Business Case_ Testing the Model__en.srt 2.7 KB
28 - Python - Sequences/29544928-Lists-Lecture-Py3.ipynb 2.7 KB
46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification__en.srt 2.7 KB
11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets__en.srt 2.7 KB
52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning__en.srt 2.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering__en.srt 2.6 KB
24 - Python - Basic Python Syntax/29544618-Arithmetic-Operators-Exercise-Py3.ipynb 2.6 KB
23 - Python - Variables and Data Types/29544582-Strings-Exercise-Py3.ipynb 2.6 KB
40 - Part 6_ Mathematics/007 Errors when Adding Matrices__en.srt 2.6 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test)__en.srt 2.6 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section__en.vtt 2.6 KB
36 - Advanced Statistical Methods - Logistic Regression/29588712-2.02.Binary-predictors.csv 2.6 KB
36 - Advanced Statistical Methods - Logistic Regression/29588826-Binary-Predictors-in-a-Logistic-Regression-Exercise.ipynb 2.5 KB
25 - Python - Other Python Operators/29544734-Comparison-Operators-Lecture-Py3.ipynb 2.5 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section__en.srt 2.5 KB
12 - Probability - Distributions/003 Characteristics of Discrete Distributions__en.srt 2.5 KB
55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/002 Business Case_ Outlining the Solution__en.srt 2.5 KB
25 - Python - Other Python Operators/001 Comparison Operators__en.srt 2.5 KB
11 - Probability - Bayesian Inference/003 Intersection of Sets__en.srt 2.5 KB
36 - Advanced Statistical Methods - Logistic Regression/29588668-Admittance-regression-summary-error.ipynb 2.5 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588072-Multiple-Linear-Regression-Exercise.ipynb 2.5 KB
45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/001 What is a Layer___en.srt 2.4 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/001 What to Expect from the Following Sections_.html 2.4 KB
27 - Python - Python Functions/001 Defining a Function in Python__en.srt 2.4 KB
36 - Advanced Statistical Methods - Logistic Regression/29588716-Binary-predictors.ipynb 2.4 KB
25 - Python - Other Python Operators/29544744-Comparison-Operators-Solution-Py3.ipynb 2.4 KB
38 - Advanced Statistical Methods - K-Means Clustering/15453029-iris-dataset.csv 2.4 KB
38 - Advanced Statistical Methods - K-Means Clustering/15453055-iris-dataset.csv 2.4 KB
26 - Python - Conditional Statements/29544822-Else-If-for-Brief-Elif-Solution-Py3.ipynb 2.4 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/006 A1_ Linearity__en.srt 2.4 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588076-real-estate-price-size-year.csv 2.4 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/29588378-real-estate-price-size-year.csv 2.4 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/29588430-real-estate-price-size-year.csv 2.4 KB
29 - Python - Iterations/005 Conditional Statements, Functions, and Loops__en.srt 2.3 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/014 Dropping a Dummy Variable from the Data Set.html 2.3 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/005 Actual Introduction to TensorFlow__en.srt 2.3 KB
23 - Python - Variables and Data Types/29544590-Numbers-and-Boolean-Values-Exercise-Py3.ipynb 2.3 KB
53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/003 A Note on Installing Packages in Anaconda.html 2.3 KB
29 - Python - Iterations/29545048-Create-Lists-with-the-range-Function-Solution-Py3.ipynb 2.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression__en.srt 2.2 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/003 MNIST_ Relevant Packages__en.srt 2.2 KB
20 - Statistics - Hypothesis Testing/002 Further Reading on Null and Alternative Hypothesis.html 2.2 KB
23 - Python - Variables and Data Types/29544602-Variables-Exercise-Py3.ipynb 2.2 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/011 MNIST_ Solutions.html 2.2 KB
05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data__en.srt 2.2 KB
31 - Part 5_ Advanced Statistical Methods in Python/001 Introduction to Regression Analysis__en.srt 2.2 KB
51 - Deep Learning - Business Case Example/011 Business Case_ Testing the Model__en.srt 2.2 KB
26 - Python - Conditional Statements/29544792-Introduction-to-the-If-Statement-Solution-Py3.ipynb 2.2 KB
29 - Python - Iterations/29545118-Iterating-over-Dictionaries-Exercise-Py3.ipynb 2.2 KB
24 - Python - Basic Python Syntax/007 Structuring with Indentation__en.srt 2.2 KB
24 - Python - Basic Python Syntax/29544694-Indexing-Elements-Solution-Py3.ipynb 2.2 KB
54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/010 MNIST_ Exercises.html 2.2 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588064-Multiple-linear-regression-and-Adjusted-R-squared.ipynb 2.1 KB
28 - Python - Sequences/29544930-Lists-Exercise-Py3.ipynb 2.1 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/005 Learning Rate Schedules Visualized__en.srt 2.1 KB
05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI)__en.srt 2.1 KB
42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function___en.srt 2.1 KB
40 - Part 6_ Mathematics/29589188-Dot-product.ipynb 2.1 KB
24 - Python - Basic Python Syntax/29544658-Reassign-Values-Solution-Py3.ipynb 2.1 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/014 ARTICLE - A Note on 'pickling'.html 2.1 KB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/24453624-Absenteeism-predictions.csv 2.1 KB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/29545266-Absenteeism-predictions.csv 2.1 KB
29 - Python - Iterations/29545070-Use-Conditional-Statements-and-Loops-Together-Exercise-Py3.ipynb 2.1 KB
36 - Advanced Statistical Methods - Logistic Regression/29588666-Admittance-regression.ipynb 2.1 KB
40 - Part 6_ Mathematics/29589126-Tensors.ipynb 2.1 KB
27 - Python - Python Functions/004 How to Use a Function within a Function__en.srt 2.1 KB
28 - Python - Sequences/29544976-Tuples-Exercise-Py3.ipynb 2.1 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression__en.srt 2.0 KB
27 - Python - Python Functions/29544874-Another-Way-to-Define-a-Function-Solution-Py3.ipynb 2.0 KB
50 - Deep Learning - Classifying on the MNIST Dataset/011 MNIST - Exercises.html 2.0 KB
29 - Python - Iterations/29545058-Use-Conditional-Statements-and-Loops-Together-Lecture-Py3.ipynb 1.9 KB
18 - Statistics - Inferential Statistics_ Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3)__en.srt 1.9 KB
51 - Deep Learning - Business Case Example/002 Business Case_ Outlining the Solution__en.srt 1.9 KB
17 - Statistics - Inferential Statistics Fundamentals/007 Standard error__en.srt 1.9 KB
28 - Python - Sequences/29544942-Help-Yourself-with-Methods-Exercise-Py3.ipynb 1.9 KB
05 - The Field of Data Science - Popular Data Science Techniques/004 Real Life Examples of Big Data__en.srt 1.9 KB
29 - Python - Iterations/29545102-All-In-Solution-Py3.ipynb 1.9 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/020 Reordering Columns in a Pandas DataFrame in Python__en.srt 1.9 KB
60 - Case Study - Loading the 'absenteeism_module'/29545382-Absenteeism-new-data.csv 1.9 KB
60 - Case Study - Loading the 'absenteeism_module'/29545388-scaler 1.9 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/29588022-real-estate-price-size.csv 1.9 KB
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