[DesireCourse.Net] Udemy - Machine Learning, Data Science and Deep Learning with Python

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Last Access Time	Oct. 15, 2024, 4:33 a.m.
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6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.mp4 200.6 MB
7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.mp4 191.0 MB
6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.mp4 189.7 MB
5. Recommender Systems/5. [Activity] Making Movie Recommendations to People.mp4 188.5 MB
2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.mp4 182.3 MB
2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.mp4 167.4 MB
2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.mp4 159.6 MB
2. Statistics and Probability Refresher, and Python Practice/4. [Activity] Variation and Standard Deviation.mp4 156.4 MB
6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.mp4 155.8 MB
5. Recommender Systems/3. [Activity] Finding Movie Similarities.mp4 152.0 MB
6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.mp4 150.6 MB
2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.mp4 147.8 MB
4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.mp4 144.9 MB
3. Predictive Models/1. [Activity] Linear Regression.mp4 144.4 MB
9. Experimental Design ML in the Real World/2. AB Testing Concepts.mp4 141.8 MB
10. Deep Learning and Neural Networks/3. [Activity] Deep Learning in the Tensorflow Playground.mp4 141.6 MB
8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).mp4 140.6 MB
9. Experimental Design ML in the Real World/6. AB Test Gotchas.mp4 139.8 MB
5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.mp4 136.5 MB
4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.mp4 134.4 MB
8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.mp4 134.0 MB
5. Recommender Systems/6. [Exercise] Improve the recommender's results.mp4 128.7 MB
10. Deep Learning and Neural Networks/18. The Ethics of Deep Learning.mp4 128.2 MB
8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.mp4 127.5 MB
4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.mp4 125.2 MB
4. Machine Learning with Python/11. Decision Trees Concepts.mp4 125.2 MB
2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.mp4 125.1 MB
5. Recommender Systems/1. User-Based Collaborative Filtering.mp4 124.6 MB
1. Getting Started/11. Introducing the Pandas Library [Optional].mp4 123.1 MB
10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.mp4 118.2 MB
8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.mp4 117.9 MB
10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.mp4 115.3 MB
9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.mp4 113.8 MB
7. Dealing with Real-World Data/3. Data Cleaning and Normalization.mp4 112.4 MB
2. Statistics and Probability Refresher, and Python Practice/1. Types of Data.mp4 112.2 MB
8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.mp4 112.0 MB
5. Recommender Systems/2. Item-Based Collaborative Filtering.mp4 108.6 MB
2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions.mp4 105.8 MB
8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.mp4 105.7 MB
12. You made it!/1. More to Explore.mp4 104.7 MB
10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.mp4 104.6 MB
4. Machine Learning with Python/5. K-Means Clustering.mp4 104.0 MB
8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.mp4 103.0 MB
1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4 102.8 MB
4. Machine Learning with Python/14. [Activity] XGBoost.mp4 102.1 MB
8. Apache Spark Machine Learning on Big Data/10. TF IDF.mp4 100.0 MB
6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis.mp4 98.5 MB
11. Final Project/2. Final project review.mp4 98.5 MB
3. Predictive Models/2. [Activity] Polynomial Regression.mp4 96.6 MB
1. Getting Started/5. [Activity] MAC Installing and Using Anaconda & Course Materials.mp4 96.5 MB
7. Dealing with Real-World Data/1. BiasVariance Tradeoff.mp4 95.8 MB
9. Experimental Design ML in the Real World/3. T-Tests and P-Values.mp4 95.7 MB
4. Machine Learning with Python/13. Ensemble Learning.mp4 95.4 MB
10. Deep Learning and Neural Networks/11. Convolutional Neural Networks (CNN's).mp4 93.1 MB
10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.mp4 92.1 MB
10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.mp4 88.2 MB
2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.mp4 86.1 MB
8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.mp4 83.6 MB
2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.mp4 82.7 MB
4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp4 82.5 MB
4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.mp4 82.4 MB
10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.mp4 81.4 MB
8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.mp4 81.2 MB
1. Getting Started/6. [Activity] LINUX Installing and Using Anaconda & Course Materials.mp4 80.2 MB
10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.mp4 80.0 MB
6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.mp4 78.0 MB
7. Dealing with Real-World Data/2. [Activity] K-Fold Cross-Validation to avoid overfitting.mp4 76.1 MB
10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.mp4 74.2 MB
3. Predictive Models/3. [Activity] Multiple Regression, and Predicting Car Prices.mp4 73.9 MB
10. Deep Learning and Neural Networks/12. [Activity] Using CNN's for handwriting recognition.mp4 69.6 MB
3. Predictive Models/4. Multi-Level Models.mp4 69.5 MB
10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).mp4 69.2 MB
4. Machine Learning with Python/15. Support Vector Machines (SVM) Overview.mp4 65.7 MB
10. Deep Learning and Neural Networks/4. Deep Learning Details.mp4 64.2 MB
10. Deep Learning and Neural Networks/5. Introducing Tensorflow.mp4 64.2 MB
2. Statistics and Probability Refresher, and Python Practice/3. [Activity] Using mean, median, and mode in Python.mp4 61.9 MB
1. Getting Started/1. Introduction.mp4 59.6 MB
6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.mp4 59.2 MB
4. Machine Learning with Python/3. Bayesian Methods Concepts.mp4 58.1 MB
7. Dealing with Real-World Data/5. Normalizing numerical data.mp4 56.9 MB
4. Machine Learning with Python/7. Measuring Entropy.mp4 52.1 MB
11. Final Project/1. Your final project assignment.mp4 51.6 MB
9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.mp4 51.3 MB
7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.mp4 49.0 MB
7. Dealing with Real-World Data/10. Binning, Transforming, Encoding, Scaling, and Shuffling.mp4 47.9 MB
4. Machine Learning with Python/16. [Activity] Using SVM to cluster people using scikit-learn.mp4 46.7 MB
2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.mp4 42.5 MB
7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.mp4 41.7 MB
10. Deep Learning and Neural Networks/19. Learning More about Deep Learning.mp4 38.6 MB
7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp4 36.3 MB
7. Dealing with Real-World Data/6. [Activity] Detecting outliers.mp4 36.3 MB
10. Deep Learning and Neural Networks/17. Deep Learning Regularization with Dropout and Early Stopping.mp4 33.6 MB
9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.mp4 33.1 MB
1. Getting Started/7. Python Basics, Part 1 [Optional].mp4 33.0 MB
6. More Data Mining and Machine Learning Techniques/9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp4 25.1 MB
2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.mp4 22.0 MB
1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].mp4 21.1 MB
1. Getting Started/8. [Activity] Python Basics, Part 2 [Optional].mp4 20.6 MB
1. Getting Started/2. Udemy 101 Getting the Most From This Course.mp4 19.8 MB
10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp4 18.4 MB
4. Machine Learning with Python/9. [Activity] MAC Installing Graphviz.mp4 14.8 MB
6. More Data Mining and Machine Learning Techniques/8. Understanding a Confusion Matrix.mp4 14.8 MB
1. Getting Started/9. [Activity] Python Basics, Part 3 [Optional].mp4 10.1 MB
4. Machine Learning with Python/10. [Activity] LINUX Installing Graphviz.mp4 7.0 MB
4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.mp4 2.1 MB
2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.srt 30.0 KB
4. Machine Learning with Python/14. [Activity] XGBoost.srt 28.7 KB
2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.srt 28.6 KB
6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.srt 28.5 KB
6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.srt 28.5 KB
2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.srt 28.4 KB
2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.srt 28.3 KB
8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.srt 28.1 KB
2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.srt 25.9 KB
2. Statistics and Probability Refresher, and Python Practice/4. [Activity] Variation and Standard Deviation.srt 25.8 KB
3. Predictive Models/1. [Activity] Linear Regression.srt 25.7 KB
11. Final Project/2. Final project review.srt 24.5 KB
8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).srt 24.4 KB
7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.srt 23.8 KB
10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.srt 23.7 KB
10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.srt 23.1 KB
5. Recommender Systems/5. [Activity] Making Movie Recommendations to People.srt 22.6 KB
6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.srt 22.5 KB
4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.srt 22.4 KB
9. Experimental Design ML in the Real World/6. AB Test Gotchas.srt 21.9 KB
10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.srt 21.5 KB
10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.srt 21.5 KB
10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.srt 21.2 KB
8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.srt 21.2 KB
6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.srt 21.2 KB
10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.srt 21.1 KB
3. Predictive Models/3. [Activity] Multiple Regression, and Predicting Car Prices.srt 21.1 KB
4. Machine Learning with Python/11. Decision Trees Concepts.srt 21.1 KB
4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.srt 20.9 KB
9. Experimental Design ML in the Real World/2. AB Testing Concepts.srt 20.2 KB
5. Recommender Systems/3. [Activity] Finding Movie Similarities.srt 20.1 KB
5. Recommender Systems/2. Item-Based Collaborative Filtering.srt 20.0 KB
10. Deep Learning and Neural Networks/11. Convolutional Neural Networks (CNN's).srt 19.9 KB
10. Deep Learning and Neural Networks/18. The Ethics of Deep Learning.srt 19.8 KB
10. Deep Learning and Neural Networks/5. Introducing Tensorflow.srt 19.8 KB
6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.srt 19.7 KB
10. Deep Learning and Neural Networks/3. [Activity] Deep Learning in the Tensorflow Playground.srt 19.7 KB
5. Recommender Systems/1. User-Based Collaborative Filtering.srt 19.4 KB
10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.srt 19.1 KB
1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.srt 18.9 KB
10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).srt 18.5 KB
1. Getting Started/11. Introducing the Pandas Library [Optional].srt 18.0 KB
7. Dealing with Real-World Data/2. [Activity] K-Fold Cross-Validation to avoid overfitting.srt 18.0 KB
8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.srt 17.7 KB
3. Predictive Models/2. [Activity] Polynomial Regression.srt 17.6 KB
4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.srt 17.4 KB
4. Machine Learning with Python/5. K-Means Clustering.srt 17.2 KB
7. Dealing with Real-World Data/3. Data Cleaning and Normalization.srt 17.1 KB
10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.srt 16.8 KB
10. Deep Learning and Neural Networks/4. Deep Learning Details.srt 16.8 KB
5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.srt 16.8 KB
4. Machine Learning with Python/16. [Activity] Using SVM to cluster people using scikit-learn.srt 16.6 KB
2. Statistics and Probability Refresher, and Python Practice/1. Types of Data.srt 16.2 KB
2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions.srt 16.1 KB
9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.srt 15.4 KB
2. Statistics and Probability Refresher, and Python Practice/3. [Activity] Using mean, median, and mode in Python.srt 15.0 KB
1. Getting Started/6. [Activity] LINUX Installing and Using Anaconda & Course Materials.srt 14.7 KB
4. Machine Learning with Python/13. Ensemble Learning.srt 14.5 KB
1. Getting Started/5. [Activity] MAC Installing and Using Anaconda & Course Materials.srt 14.5 KB
7. Dealing with Real-World Data/1. BiasVariance Tradeoff.srt 14.4 KB
7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.srt 14.3 KB
7. Dealing with Real-World Data/10. Binning, Transforming, Encoding, Scaling, and Shuffling.srt 14.2 KB
8. Apache Spark Machine Learning on Big Data/10. TF IDF.srt 14.0 KB
8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.srt 13.9 KB
10. Deep Learning and Neural Networks/12. [Activity] Using CNN's for handwriting recognition.srt 13.8 KB
9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.srt 13.7 KB
5. Recommender Systems/6. [Exercise] Improve the recommender's results.srt 13.2 KB
9. Experimental Design ML in the Real World/3. T-Tests and P-Values.srt 13.2 KB
4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.srt 13.1 KB
2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.srt 13.0 KB
8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.srt 12.9 KB
6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis.srt 12.3 KB
8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.srt 12.0 KB
10. Deep Learning and Neural Networks/17. Deep Learning Regularization with Dropout and Early Stopping.srt 12.0 KB
7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.srt 11.8 KB
11. Final Project/1. Your final project assignment.srt 11.6 KB
4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.srt 11.5 KB
2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.srt 11.5 KB
8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.srt 11.5 KB
7. Dealing with Real-World Data/6. [Activity] Detecting outliers.srt 11.4 KB
6. More Data Mining and Machine Learning Techniques/9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).srt 10.9 KB
3. Predictive Models/4. Multi-Level Models.srt 10.7 KB
8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.srt 10.6 KB
4. Machine Learning with Python/15. Support Vector Machines (SVM) Overview.srt 9.9 KB
7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.srt 9.9 KB
6. More Data Mining and Machine Learning Techniques/8. Understanding a Confusion Matrix.srt 9.7 KB
6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.srt 8.9 KB
4. Machine Learning with Python/3. Bayesian Methods Concepts.srt 8.8 KB
9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.srt 8.3 KB
10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.srt 8.3 KB
1. Getting Started/7. Python Basics, Part 1 [Optional].srt 7.8 KB
7. Dealing with Real-World Data/5. Normalizing numerical data.srt 7.7 KB
1. Getting Started/8. [Activity] Python Basics, Part 2 [Optional].srt 7.6 KB
2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.srt 7.6 KB
12. You made it!/3. Bonus Lecture More courses to explore!.html 7.4 KB
12. You made it!/1. More to Explore.srt 7.2 KB
4. Machine Learning with Python/7. Measuring Entropy.srt 6.9 KB
1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].srt 6.0 KB
1. Getting Started/1. Introduction.srt 4.7 KB
1. Getting Started/9. [Activity] Python Basics, Part 3 [Optional].srt 4.2 KB
1. Getting Started/2. Udemy 101 Getting the Most From This Course.srt 4.0 KB
2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.srt 4.0 KB
8. Apache Spark Machine Learning on Big Data/2. Spark installation notes for MacOS and Linux users.html 3.6 KB
10. Deep Learning and Neural Networks/19. Learning More about Deep Learning.srt 3.1 KB
4. Machine Learning with Python/9. [Activity] MAC Installing Graphviz.srt 1.3 KB
4. Machine Learning with Python/10. [Activity] LINUX Installing Graphviz.srt 1.1 KB
4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.srt 689 bytes
10. Deep Learning and Neural Networks/6. Important note about Tensorflow 2.html 644 bytes
8. Apache Spark Machine Learning on Big Data/1. Warning about Java 11 and Spark 3!.html 602 bytes
12. You made it!/2. Don't Forget to Leave a Rating!.html 564 bytes
1. Getting Started/3. Installation Getting Started.html 265 bytes
6. More Data Mining and Machine Learning Techniques/6.1 Pac-Man Example.html 145 bytes
6. More Data Mining and Machine Learning Techniques/6.3 Cat and Mouse Example.html 140 bytes
6. More Data Mining and Machine Learning Techniques/6.2 Python Markov Decision Process Toolbox.html 119 bytes
8. Apache Spark Machine Learning on Big Data/3.1 winutils.exe.html 108 bytes
8. Apache Spark Machine Learning on Big Data/4.1 winutils.exe.html 108 bytes
0. Websites you may like/[DesireCourse.Net].url 51 bytes
1. Getting Started/[DesireCourse.Net].url 51 bytes
12. You made it!/[DesireCourse.Net].url 51 bytes
5. Recommender Systems/[DesireCourse.Net].url 51 bytes
9. Experimental Design ML in the Real World/[DesireCourse.Net].url 51 bytes
[DesireCourse.Net].url 51 bytes
0. Websites you may like/[CourseClub.Me].url 48 bytes
1. Getting Started/[CourseClub.Me].url 48 bytes
12. You made it!/[CourseClub.Me].url 48 bytes
5. Recommender Systems/[CourseClub.Me].url 48 bytes
9. Experimental Design ML in the Real World/[CourseClub.Me].url 48 bytes
[CourseClub.Me].url 48 bytes

[DesireCourse.Net] Udemy - Machine Learning, Data Science and Deep Learning with Python

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