[FreeCourseSite.com] Udemy - Complete Data Science & Machine Learning A-Z with Python

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Last Access Time	Nov. 19, 2024, 5:12 p.m.
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42. Competition Section on Kaggle/2. Competitions on Kaggle Lesson 2.mp4 191.7 MB
42. Competition Section on Kaggle/1. Competitions on Kaggle Lesson 1.mp4 188.2 MB
44. Code Section on Kaggle/3. Examining the Code Section in Kaggle Lesson 3.mp4 159.9 MB
43. Dataset Section on Kaggle/1. Datasets on Kaggle.mp4 133.2 MB
41. First Contact with Kaggle/1. What is Kaggle.mp4 129.7 MB
48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/6. Recognizing Variables In Dataset.mp4 126.9 MB
41. First Contact with Kaggle/5. Getting to Know the Kaggle Homepage.mp4 122.9 MB
1. Installations/1. Installing Anaconda Distribution for Windows.mp4 118.3 MB
48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/1. First Step to the Hearth Attack Prediction Project.mp4 117.1 MB
1. Installations/5. Installing Anaconda Distribution for Linux.mp4 114.8 MB
21. Matplotlib/8. Basic Plots in Matplotlib I.mp4 111.2 MB
46. Other Most Used Options on Kaggle/2. Ranking Among Users on Kaggle.mp4 107.0 MB
27. Linear Regression Algorithm in Machine Learning A-Z/3. Linear Regression Algorithm With Python Part 2.mp4 106.9 MB
44. Code Section on Kaggle/2. Examining the Code Section in Kaggle Lesson 2.mp4 105.8 MB
48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/3. Notebook Design to be Used in the Project.mp4 104.9 MB
25. Evaluation Metrics in Machine Learning/2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 100.3 MB
22. Seaborn/5. Basic Plots in Seaborn.mp4 98.8 MB
25. Evaluation Metrics in Machine Learning/4. Machine Learning With Python.mp4 92.2 MB
50. Preparation For Exploratory Data Analysis (EDA) in Data Science/4. Examining Statistics of Variables.mp4 91.4 MB
14. Functions That Can Be Applied on a DataFrame/3. Aggregation Functions in Pandas DataFrames.mp4 90.7 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 90.7 MB
27. Linear Regression Algorithm in Machine Learning A-Z/5. Linear Regression Algorithm With Python Part 4.mp4 90.0 MB
14. Functions That Can Be Applied on a DataFrame/5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp4 88.1 MB
51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 84.1 MB
47. Details on Kaggle/1. User Page Review on Kaggle.mp4 81.5 MB
29. Logistic Regression Algorithm in Machine Learning A-Z/3. Logistic Regression Algorithm with Python Part 2.mp4 81.5 MB
23. Geoplotlib/3. Example - 2.mp4 81.1 MB
51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 80.4 MB
44. Code Section on Kaggle/1. Examining the Code Section in Kaggle Lesson 1.mp4 79.5 MB
48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/5. Examining the Project Topic.mp4 76.5 MB
27. Linear Regression Algorithm in Machine Learning A-Z/2. Linear Regression Algorithm With Python Part 1.mp4 76.2 MB
19. Fundamentals of Python 3/5. Lists, Tuples, Dictionaries and Sets in pyhton.mp4 75.3 MB
51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 74.7 MB
47. Details on Kaggle/2. Treasure in The Kaggle.mp4 74.6 MB
29. Logistic Regression Algorithm in Machine Learning A-Z/2. Logistic Regression Algorithm with Python Part 1.mp4 72.2 MB
6. Operations in Numpy Library/2. Arithmetic Operations in Numpy.mp4 71.8 MB
27. Linear Regression Algorithm in Machine Learning A-Z/4. Linear Regression Algorithm With Python Part 3.mp4 70.3 MB
21. Matplotlib/4. Figure, Subplot and Axex.mp4 69.9 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 68.1 MB
11. Structural Operations on Pandas DataFrame/3. Null Values in Pandas Dataframes.mp4 67.0 MB
16. File Operations in Pandas Library/2. Data Entry with Csv and Txt Files.mp4 64.3 MB
49. First Organization/3. Initial analysis on the dataset.mp4 64.0 MB
13. Structural Concatenation Operations in Pandas DataFrame/1. Concatenating Pandas Dataframes Concat Function.mp4 63.8 MB
49. First Organization/1. Required Python Libraries.mp4 63.6 MB
21. Matplotlib/5. Figure Customization.mp4 63.3 MB
20. Object Oriented Programming (OOP)/5. Overriding and Overloading in Object Oriented Programming (OOP).mp4 62.7 MB
13. Structural Concatenation Operations in Pandas DataFrame/4. Merge Pandas Dataframes Merge() Function Lesson 3.mp4 60.2 MB
22. Seaborn/7. Regression Plots and Squarify in Seaborn.mp4 60.1 MB
2. NumPy Library Introduction/2. The Power of NumPy.mp4 59.9 MB
31. K Nearest Neighbors Algorithm in Machine Learning A-Z/3. K Nearest Neighbors Algorithm with Python Part 2.mp4 59.4 MB
19. Fundamentals of Python 3/4. Loops in Python.mp4 58.8 MB
54. Modelling for Machine Learning/4. Hyperparameter Optimization (with GridSearchCV).mp4 58.8 MB
47. Details on Kaggle/4. What Should Be Done to Achieve Success in Kaggle.mp4 58.5 MB
13. Structural Concatenation Operations in Pandas DataFrame/2. Merge Pandas Dataframes Merge() Function Lesson 1.mp4 57.3 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 56.3 MB
13. Structural Concatenation Operations in Pandas DataFrame/6. Joining Pandas Dataframes Join() Function.mp4 56.1 MB
28. Bias Variance Trade-Off in Machine Learning/1. What is Bias Variance Trade-Off.mp4 55.0 MB
22. Seaborn/3. Example in Seaborn.mp4 54.9 MB
21. Matplotlib/9. Basic Plots in Matplotlib II.mp4 54.8 MB
15. Pivot Tables in Pandas Library/2. Pivot Tables in Pandas Library.mp4 54.2 MB
51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/5. Examining the Missing Data According to the Analysis Result.mp4 53.8 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/8. Creating a New DataFrame with the Melt() Function.mp4 52.9 MB
54. Modelling for Machine Learning/8. Hyperparameter Optimization (with GridSearchCV).mp4 52.7 MB
46. Other Most Used Options on Kaggle/1. Courses in Kaggle.mp4 52.1 MB
19. Fundamentals of Python 3/10. Exercise - Solution in Python.mp4 51.9 MB
11. Structural Operations on Pandas DataFrame/5. Filling Null Values Fillna() Function.mp4 51.6 MB
23. Geoplotlib/4. Example - 3.mp4 51.3 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 49.4 MB
33. Decision Tree Algorithm in Machine Learning A-Z/3. Decision Tree Algorithm with Python Part 2.mp4 48.9 MB
22. Seaborn/4. Color Palettes in Seaborn.mp4 48.3 MB
8. Series Structures in the Pandas Library/6. Most Applied Methods on Pandas Series.mp4 48.2 MB
32. Hyperparameter Optimization/2. Hyperparameter Optimization with Python.mp4 47.5 MB
29. Logistic Regression Algorithm in Machine Learning A-Z/4. Logistic Regression Algorithm with Python Part 3.mp4 47.3 MB
29. Logistic Regression Algorithm in Machine Learning A-Z/5. Logistic Regression Algorithm with Python Part 4.mp4 47.2 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 47.1 MB
14. Functions That Can Be Applied on a DataFrame/8. Advanced Aggregation Functions Transform() Function.mp4 47.1 MB
19. Fundamentals of Python 3/1. Data Types in Python.mp4 47.1 MB
14. Functions That Can Be Applied on a DataFrame/4. Examining the Data Set 2.mp4 46.6 MB
10. Element Selection Operations in DataFrame Structures/6. Element Selection with Conditional Operations in.mp4 46.4 MB
1. Installations/3. Installing Anaconda Distribution for MacOs.mp4 46.3 MB
50. Preparation For Exploratory Data Analysis (EDA) in Data Science/1. Examining Missing Values.mp4 45.8 MB
5. Indexing, Slicing, and Assigning NumPy Arrays/7. Fancy Indexing of Two-Dimensional Arrrays.mp4 45.7 MB
25. Evaluation Metrics in Machine Learning/3. Evaluating Performance Regression Error Metrics in Python.mp4 45.7 MB
2. NumPy Library Introduction/1. Introduction to NumPy Library.mp4 45.3 MB
50. Preparation For Exploratory Data Analysis (EDA) in Data Science/2. Examining Unique Values.mp4 44.5 MB
53. Preparation for Modelling in Machine Learning/4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 43.9 MB
19. Fundamentals of Python 3/6. Data Type Operators and Methods in Python.mp4 43.9 MB
41. First Contact with Kaggle/3. Registering on Kaggle and Member Login Procedures.mp4 43.5 MB
3. Creating NumPy Array in Python/8. Creating NumPy Array with Random() Function.mp4 43.3 MB
22. Seaborn/6. Multi-Plots in Seaborn.mp4 43.0 MB
14. Functions That Can Be Applied on a DataFrame/2. Examining the Data Set 1.mp4 42.9 MB
53. Preparation for Modelling in Machine Learning/3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 42.8 MB
12. Multi-Indexed DataFrame Structures/1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp4 42.7 MB
33. Decision Tree Algorithm in Machine Learning A-Z/5. Decision Tree Algorithm with Python Part 4.mp4 42.5 MB
22. Seaborn/2. Controlling Figure Aesthetics in Seaborn.mp4 41.8 MB
35. Support Vector Machine Algorithm in Machine Learning A-Z/3. Support Vector Machine Algorithm with Python Part 2.mp4 41.7 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 41.7 MB
54. Modelling for Machine Learning/3. Roc Curve and Area Under Curve (AUC).mp4 41.7 MB
14. Functions That Can Be Applied on a DataFrame/9. Advanced Aggregation Functions Apply() Function.mp4 41.4 MB
19. Fundamentals of Python 3/3. Conditionals in Python.mp4 41.2 MB
46. Other Most Used Options on Kaggle/3. Blog and Documentation Sections.mp4 40.9 MB
13. Structural Concatenation Operations in Pandas DataFrame/5. Merge Pandas Dataframes Merge() Function Lesson 4.mp4 40.7 MB
45. Discussion Section on Kaggle/1. What is Discussion on Kaggle.mp4 40.6 MB
11. Structural Operations on Pandas DataFrame/6. Setting Index in Pandas DataFrames.mp4 39.7 MB
29. Logistic Regression Algorithm in Machine Learning A-Z/6. Logistic Regression Algorithm with Python Part 5.mp4 39.3 MB
8. Series Structures in the Pandas Library/1. Creating a Pandas Series with a List.mp4 39.2 MB
15. Pivot Tables in Pandas Library/1. Examining the Data Set 3.mp4 39.1 MB
23. Geoplotlib/2. Example - 1.mp4 38.9 MB
34. Random Forest Algorithm in Machine Learning A-Z/3. Random Forest Algorithm with Pyhon Part 2.mp4 38.7 MB
34. Random Forest Algorithm in Machine Learning A-Z/2. Random Forest Algorithm with Pyhon Part 1.mp4 38.6 MB
4. Functions in the NumPy Library/4. Concatenating Numpy Arrays Concatenate() Functio.mp4 38.4 MB
10. Element Selection Operations in DataFrame Structures/3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4 38.3 MB
47. Details on Kaggle/3. Publishing Notebooks on Kaggle.mp4 38.2 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 38.1 MB
39. Principal Component Analysis (PCA) in Machine Learning A-Z/1. Principal Component Analysis (PCA) Theory.mp4 38.0 MB
14. Functions That Can Be Applied on a DataFrame/1. Loading a Dataset from the Seaborn Library.mp4 37.7 MB
35. Support Vector Machine Algorithm in Machine Learning A-Z/5. Support Vector Machine Algorithm with Python Part 4.mp4 37.6 MB
39. Principal Component Analysis (PCA) in Machine Learning A-Z/4. Principal Component Analysis (PCA) with Python Part 3.mp4 37.2 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 36.4 MB
53. Preparation for Modelling in Machine Learning/5. Dealing with Outliers – Thalach Variable.mp4 36.2 MB
53. Preparation for Modelling in Machine Learning/6. Dealing with Outliers – Oldpeak Variable.mp4 36.1 MB
20. Object Oriented Programming (OOP)/2. Constructor in Object Oriented Programming (OOP).mp4 35.8 MB
33. Decision Tree Algorithm in Machine Learning A-Z/1. Decision Tree Algorithm Theory.mp4 35.8 MB
4. Functions in the NumPy Library/6. Splitting Two-Dimensional Numpy Arrays Split(),.mp4 35.7 MB
19. Fundamentals of Python 3/2. Operators in Python.mp4 35.7 MB
16. File Operations in Pandas Library/4. Outputting as an CSV Extension.mp4 35.7 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 35.6 MB
35. Support Vector Machine Algorithm in Machine Learning A-Z/2. Support Vector Machine Algorithm with Python Part 1.mp4 35.6 MB
38. Hierarchical Clustering Algorithm in machine learning data science/2. Hierarchical Clustering Algorithm with Python Part 2.mp4 35.5 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 35.5 MB
5. Indexing, Slicing, and Assigning NumPy Arrays/5. Assigning Value to Two-Dimensional Array.mp4 35.4 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/7. Feature Scaling with the Robust Scaler Method.mp4 35.2 MB
31. K Nearest Neighbors Algorithm in Machine Learning A-Z/2. K Nearest Neighbors Algorithm with Python Part 1.mp4 35.0 MB
53. Preparation for Modelling in Machine Learning/2. Visualizing Outliers.mp4 34.9 MB
35. Support Vector Machine Algorithm in Machine Learning A-Z/4. Support Vector Machine Algorithm with Python Part 3.mp4 34.8 MB
30. K-fold Cross-Validation in Machine Learning A-Z/2. K-Fold Cross-Validation with Python.mp4 34.7 MB
16. File Operations in Pandas Library/1. Accessing and Making Files Available.mp4 34.6 MB
20. Object Oriented Programming (OOP)/4. Inheritance in Object Oriented Programming (OOP).mp4 34.6 MB
11. Structural Operations on Pandas DataFrame/4. Dropping Null Values Dropna() Function.mp4 34.5 MB
5. Indexing, Slicing, and Assigning NumPy Arrays/3. Slicing Two-Dimensional Numpy Arrays.mp4 34.3 MB
23. Geoplotlib/1. What is Geoplotlib.mp4 34.2 MB
27. Linear Regression Algorithm in Machine Learning A-Z/1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 34.1 MB
7. Pandas Library Introduction/1. Introduction to Pandas Library.mp4 33.9 MB
11. Structural Operations on Pandas DataFrame/1. Adding Columns to Pandas Data Frames.mp4 33.6 MB
32. Hyperparameter Optimization/1. Hyperparameter Optimization Theory.mp4 33.1 MB
33. Decision Tree Algorithm in Machine Learning A-Z/6. Decision Tree Algorithm with Python Part 5.mp4 32.7 MB
6. Operations in Numpy Library/3. Statistical Operations in Numpy.mp4 32.0 MB
10. Element Selection Operations in DataFrame Structures/2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4 31.8 MB
26. Supervised Learning with Machine Learning/1. What is Supervised Learning in Machine Learning.mp4 31.7 MB
33. Decision Tree Algorithm in Machine Learning A-Z/2. Decision Tree Algorithm with Python Part 1.mp4 31.5 MB
10. Element Selection Operations in DataFrame Structures/4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4 31.4 MB
31. K Nearest Neighbors Algorithm in Machine Learning A-Z/4. K Nearest Neighbors Algorithm with Python Part 3.mp4 31.4 MB
12. Multi-Indexed DataFrame Structures/3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4 31.3 MB
13. Structural Concatenation Operations in Pandas DataFrame/3. Merge Pandas Dataframes Merge() Function Lesson 2.mp4 30.5 MB
54. Modelling for Machine Learning/2. Cross Validation.mp4 30.2 MB
37. K Means Clustering Algorithm in Machine Learning A-Z/2. K Means Clustering Algorithm with Python Part 1.mp4 30.0 MB
10. Element Selection Operations in DataFrame Structures/1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4 29.9 MB
8. Series Structures in the Pandas Library/7. Indexing and Slicing Pandas Series.mp4 29.9 MB
54. Modelling for Machine Learning/7. Random Forest Algorithm.mp4 29.8 MB
53. Preparation for Modelling in Machine Learning/11. Separating Data into Test and Training Set.mp4 29.8 MB
37. K Means Clustering Algorithm in Machine Learning A-Z/3. K Means Clustering Algorithm with Python Part 2.mp4 29.6 MB
3. Creating NumPy Array in Python/1. Creating NumPy Array with The Array() Function.mp4 29.5 MB
54. Modelling for Machine Learning/1. Logistic Regression.mp4 29.3 MB
14. Functions That Can Be Applied on a DataFrame/6. Advanced Aggregation Functions Aggregate() Function.mp4 29.2 MB
37. K Means Clustering Algorithm in Machine Learning A-Z/5. K Means Clustering Algorithm with Python Part 4.mp4 29.0 MB
19. Fundamentals of Python 3/8. Functions in Python.mp4 28.9 MB
38. Hierarchical Clustering Algorithm in machine learning data science/3. Hierarchical Clustering Algorithm with Python Part 2.mp4 28.9 MB
55. Conclusion/1. Project Conclusion and Sharing.mp4 28.7 MB
31. K Nearest Neighbors Algorithm in Machine Learning A-Z/1. K Nearest Neighbors Algorithm Theory.mp4 28.7 MB
38. Hierarchical Clustering Algorithm in machine learning data science/1. Hierarchical Clustering Algorithm Theory.mp4 28.6 MB
21. Matplotlib/3. Pyplot – Pylab - Matplotlib.mp4 28.4 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 28.3 MB
21. Matplotlib/2. Using Pyplot.mp4 28.2 MB
29. Logistic Regression Algorithm in Machine Learning A-Z/1. What is Logistic Regression Algorithm in Machine Learning.mp4 27.8 MB
37. K Means Clustering Algorithm in Machine Learning A-Z/4. K Means Clustering Algorithm with Python Part 3.mp4 27.8 MB
24. First Contact with Machine Learning/1. What is Machine Learning.mp4 27.6 MB
21. Matplotlib/6. Plot Customization.mp4 27.4 MB
53. Preparation for Modelling in Machine Learning/1. Dropping Columns with Low Correlation.mp4 26.8 MB
5. Indexing, Slicing, and Assigning NumPy Arrays/1. Indexing Numpy Arrays,.mp4 26.6 MB
4. Functions in the NumPy Library/1. Reshaping a NumPy Array Reshape() Function.mp4 26.2 MB
39. Principal Component Analysis (PCA) in Machine Learning A-Z/2. Principal Component Analysis (PCA) with Python Part 1.mp4 26.0 MB
9. DataFrame Structures in Pandas Library/4. Examining the Properties of Pandas DataFrames.mp4 25.9 MB
54. Modelling for Machine Learning/5. Decision Tree Algorithm.mp4 25.7 MB
53. Preparation for Modelling in Machine Learning/7. Determining Distributions of Numeric Variables.mp4 25.2 MB
20. Object Oriented Programming (OOP)/3. Methods in Object Oriented Programming (OOP).mp4 25.1 MB
12. Multi-Indexed DataFrame Structures/2. Element Selection in Multi-Indexed DataFrames.mp4 24.6 MB
54. Modelling for Machine Learning/6. Support Vector Machine Algorithm.mp4 24.5 MB
14. Functions That Can Be Applied on a DataFrame/7. Advanced Aggregation Functions Filter() Function.mp4 24.4 MB
6. Operations in Numpy Library/4. Solving Second-Degree Equations with NumPy.mp4 24.2 MB
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 24.1 MB
53. Preparation for Modelling in Machine Learning/9. Applying One Hot Encoding Method to Categorical Variables.mp4 24.1 MB
3. Creating NumPy Array in Python/2. Creating NumPy Array with Zeros() Function.mp4 24.1 MB
53. Preparation for Modelling in Machine Learning/8. Transformation Operations on Unsymmetrical Data.mp4 24.0 MB
19. Fundamentals of Python 3/7. Modules in Python.mp4 23.9 MB
21. Matplotlib/7. Grid, Spines, Ticks.mp4 23.9 MB
40. Recommender System Algorithm in Machine Learning A-Z/1. What is the Recommender System Part 1.mp4 23.0 MB
34. Random Forest Algorithm in Machine Learning A-Z/1. Random Forest Algorithm Theory.mp4 22.9 MB
9. DataFrame Structures in Pandas Library/1. Creating Pandas DataFrame with List.mp4 22.6 MB
5. Indexing, Slicing, and Assigning NumPy Arrays/2. Slicing One-Dimensional Numpy Arrays.mp4 22.3 MB
10. Element Selection Operations in DataFrame Structures/5. Top Level Element Selection in Pandas DataFramesLesson 3.mp4 22.1 MB
3. Creating NumPy Array in Python/9. Properties of NumPy Array.mp4 22.0 MB
35. Support Vector Machine Algorithm in Machine Learning A-Z/1. Support Vector Machine Algorithm Theory.mp4 21.8 MB
16. File Operations in Pandas Library/3. Data Entry with Excel Files.mp4 21.8 MB
6. Operations in Numpy Library/1. Operations with Comparison Operators.mp4 21.1 MB
4. Functions in the NumPy Library/5. Splitting One-Dimensional Numpy Arrays The Split.mp4 20.9 MB
5. Indexing, Slicing, and Assigning NumPy Arrays/6. Fancy Indexing of One-Dimensional Arrrays.mp4 20.5 MB
25. Evaluation Metrics in Machine Learning/1. Classification vs Regression in Machine Learning.mp4 19.9 MB
51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 19.7 MB
16. File Operations in Pandas Library/5. Outputting as an Excel File.mp4 19.7 MB
8. Series Structures in the Pandas Library/4. Object Types in Series.mp4 19.6 MB
21. Matplotlib/1. What is Matplotlib.mp4 19.1 MB
8. Series Structures in the Pandas Library/5. Examining the Primary Features of the Pandas Seri.mp4 18.9 MB
8. Series Structures in the Pandas Library/2. Creating a Pandas Series with a Dictionary.mp4 18.3 MB
5. Indexing, Slicing, and Assigning NumPy Arrays/4. Assigning Value to One-Dimensional Arrays.mp4 18.2 MB
40. Recommender System Algorithm in Machine Learning A-Z/2. What is the Recommender System Part 2.mp4 18.0 MB
30. K-fold Cross-Validation in Machine Learning A-Z/1. K-Fold Cross-Validation Theory.mp4 17.4 MB
20. Object Oriented Programming (OOP)/1. Logic of Object Oriented Programming.mp4 17.4 MB
37. K Means Clustering Algorithm in Machine Learning A-Z/1. K Means Clustering Algorithm Theory.mp4 17.1 MB
4. Functions in the NumPy Library/7. Sorting Numpy Arrays Sort() Function.mp4 17.0 MB
36. Unsupervised Learning with Machine Learning/1. Unsupervised Learning Overview.mp4 16.9 MB
5. Indexing, Slicing, and Assigning NumPy Arrays/9. Combining Fancy Index with Normal Slicing.mp4 16.5 MB
3. Creating NumPy Array in Python/3. Creating NumPy Array with Ones() Function.mp4 15.9 MB
9. DataFrame Structures in Pandas Library/3. Creating Pandas DataFrame with Dictionary.mp4 15.8 MB
50. Preparation For Exploratory Data Analysis (EDA) in Data Science/3. Separating variables (Numeric or Categorical).mp4 15.8 MB
11. Structural Operations on Pandas DataFrame/2. Removing Rows and Columns from Pandas Data frames.mp4 15.6 MB
4. Functions in the NumPy Library/2. Identifying the Largest Element of a Numpy Array.mp4 15.1 MB
33. Decision Tree Algorithm in Machine Learning A-Z/4. Decision Tree Algorithm with Python Part 3.mp4 14.7 MB
24. First Contact with Machine Learning/2. Machine Learning Terminology.mp4 14.0 MB
22. Seaborn/1. What is Seaborn.mp4 13.6 MB
18. Introduction to Data Visualization with Python/1. Introduction to Data Visualization with Python.mp4 12.8 MB
5. Indexing, Slicing, and Assigning NumPy Arrays/8. Combining Fancy Index with Normal Indexing.mp4 12.7 MB
3. Creating NumPy Array in Python/6. Creating NumPy Array with Eye() Function.mp4 12.6 MB
3. Creating NumPy Array in Python/5. Creating NumPy Array with Arange() Function.mp4 12.1 MB
9. DataFrame Structures in Pandas Library/2. Creating Pandas DataFrame with NumPy Array.mp4 12.1 MB
8. Series Structures in the Pandas Library/3. Creating Pandas Series with NumPy Array.mp4 12.0 MB
53. Preparation for Modelling in Machine Learning/10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 11.4 MB
3. Creating NumPy Array in Python/4. Creating NumPy Array with Full() Function.mp4 11.2 MB
4. Functions in the NumPy Library/3. Detecting Least Element of Numpy Array Min(), Ar.mp4 10.2 MB
49. First Organization/2. Loading the Statistics Dataset in Data Science.mp4 10.0 MB
19. Fundamentals of Python 3/9. Exercise - Analyse in Python.mp4 8.5 MB
39. Principal Component Analysis (PCA) in Machine Learning A-Z/3. Principal Component Analysis (PCA) with Python Part 2.mp4 8.4 MB
3. Creating NumPy Array in Python/7. Creating NumPy Array with Linspace() Function.mp4 7.3 MB
48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/2. FAQ about Machine Learning, Data Science.html 15.3 KB
41. First Contact with Kaggle/2. FAQ about Kaggle.html 10.9 KB
18. Introduction to Data Visualization with Python/2. FAQ regarding Data Visualization, Python.html 8.6 KB
24. First Contact with Machine Learning/5. FAQ regarding Machine Learning.html 6.6 KB
24. First Contact with Machine Learning/4. FAQ regarding Python.html 6.2 KB
1. Installations/4. 6 Article Advice And Links about Numpy, Numpy Pyhon.html 4.2 KB
56. Extra/1. Complete Data Science & Machine Learning A-Z with Python.html 266 bytes
24. First Contact with Machine Learning/3. Machine Learning Project Files.html 254 bytes
10. Element Selection Operations in DataFrame Structures/7. Quiz.html 205 bytes
11. Structural Operations on Pandas DataFrame/7. Quiz.html 205 bytes
12. Multi-Indexed DataFrame Structures/4. Quiz.html 205 bytes
13. Structural Concatenation Operations in Pandas DataFrame/7. Quiz.html 205 bytes
14. Functions That Can Be Applied on a DataFrame/10. Quiz.html 205 bytes
15. Pivot Tables in Pandas Library/3. Quiz.html 205 bytes
16. File Operations in Pandas Library/6. Quiz.html 205 bytes
19. Fundamentals of Python 3/11. Quiz.html 205 bytes
2. NumPy Library Introduction/3. Quiz.html 205 bytes
20. Object Oriented Programming (OOP)/6. Quiz.html 205 bytes
21. Matplotlib/10. Quiz.html 205 bytes
22. Seaborn/8. Quiz.html 205 bytes
23. Geoplotlib/5. Quiz.html 205 bytes
24. First Contact with Machine Learning/6. Quiz.html 205 bytes
25. Evaluation Metrics in Machine Learning/5. Quiz.html 205 bytes
26. Supervised Learning with Machine Learning/2. Quiz.html 205 bytes
28. Bias Variance Trade-Off in Machine Learning/2. Quiz.html 205 bytes
29. Logistic Regression Algorithm in Machine Learning A-Z/7. Quiz.html 205 bytes
3. Creating NumPy Array in Python/10. Quiz.html 205 bytes
31. K Nearest Neighbors Algorithm in Machine Learning A-Z/5. Quiz.html 205 bytes
33. Decision Tree Algorithm in Machine Learning A-Z/7. Quiz.html 205 bytes
35. Support Vector Machine Algorithm in Machine Learning A-Z/6. Quiz.html 205 bytes
37. K Means Clustering Algorithm in Machine Learning A-Z/6. Quiz.html 205 bytes
4. Functions in the NumPy Library/8. Quiz.html 205 bytes
41. First Contact with Kaggle/6. quiz.html 205 bytes
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48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/7. Quiz.html 205 bytes
49. First Organization/4. Quiz.html 205 bytes
50. Preparation For Exploratory Data Analysis (EDA) in Data Science/5. Quiz.html 205 bytes
51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/6. Quiz.html 205 bytes
52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/15. Quiz.html 205 bytes
53. Preparation for Modelling in Machine Learning/12. Quiz.html 205 bytes
54. Modelling for Machine Learning/9. Quiz.html 205 bytes
55. Conclusion/2. Quiz.html 205 bytes
7. Pandas Library Introduction/3. Quiz.html 205 bytes
8. Series Structures in the Pandas Library/8. Quiz.html 205 bytes
9. DataFrame Structures in Pandas Library/5. Quiz.html 205 bytes
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17. Code Files And Resources Python data analysis and visualization/1. Data Visualisation - Matplotlib Files.html 170 bytes
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26. Supervised Learning with Machine Learning/0. Websites you may like/[FreeCourseSite.com].url 127 bytes
0. Websites you may like/[CourseClub.Me].url 122 bytes
11. Structural Operations on Pandas DataFrame/0. Websites you may like/[CourseClub.Me].url 122 bytes
26. Supervised Learning with Machine Learning/0. Websites you may like/[CourseClub.Me].url 122 bytes
48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html 108 bytes
41. First Contact with Kaggle/4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html 97 bytes
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