GetFreeCourses.Co-Udemy-Machine Learning, Data Science and Deep Learning with Python

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文档列表

2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.mp4 155.0 MB
6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.mp4 149.0 MB
10. Deep Learning and Neural Networks/3. [Activity] Deep Learning in the Tensorflow Playground.srt 148.5 MB
10. Deep Learning and Neural Networks/3. [Activity] Deep Learning in the Tensorflow Playground.mp4 148.5 MB
8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.mp4 140.5 MB
5. Recommender Systems/5. [Activity] Making Movie Recommendations to People.mp4 139.0 MB
6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.mp4 138.7 MB
7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.mp4 135.7 MB
2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.mp4 135.6 MB
10. Deep Learning and Neural Networks/18. The Ethics of Deep Learning.mp4 134.5 MB
2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.mp4 131.2 MB
1. Getting Started/11. Introducing the Pandas Library [Optional].mp4 129.1 MB
8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.mp4 123.6 MB
2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.mp4 122.4 MB
10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.mp4 120.9 MB
2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.mp4 119.6 MB
8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.mp4 117.4 MB
2. Statistics and Probability Refresher, and Python Practice/4. [Activity] Variation and Standard Deviation.mp4 116.2 MB
6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.mp4 115.1 MB
10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.mp4 113.9 MB
5. Recommender Systems/3. [Activity] Finding Movie Similarities.mp4 113.1 MB
8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.mp4 110.8 MB
6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.mp4 108.4 MB
8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.mp4 108.0 MB
1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4 107.8 MB
7. Dealing with Real-World Data/2. [Activity] K-Fold Cross-Validation to avoid overfitting.mp4 107.3 MB
3. Predictive Models/1. [Activity] Linear Regression.mp4 105.3 MB
4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.mp4 103.4 MB
8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).mp4 103.3 MB
11. Final Project/2. Final project review.mp4 103.3 MB
9. Experimental Design ML in the Real World/2. AB Testing Concepts.srt 102.2 MB
9. Experimental Design ML in the Real World/2. AB Testing Concepts.mp4 102.2 MB
1. Getting Started/5. [Activity] MAC Installing and Using Anaconda & Course Materials.mp4 101.2 MB
9. Experimental Design ML in the Real World/6. AB Test Gotchas.mp4 100.8 MB
4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.mp4 100.6 MB
5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.mp4 99.5 MB
10. Deep Learning and Neural Networks/11. Convolutional Neural Networks (CNN's).mp4 97.6 MB
10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.mp4 96.5 MB
8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.mp4 94.2 MB
4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.mp4 93.4 MB
10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.mp4 92.5 MB
4. Machine Learning with Python/11. Decision Trees Concepts.mp4 90.7 MB
5. Recommender Systems/1. User-Based Collaborative Filtering.mp4 90.6 MB
10. Deep Learning and Neural Networks/5. Introducing Tensorflow.mp4 90.5 MB
5. Recommender Systems/6. [Exercise] Improve the recommender's results.mp4 88.3 MB
8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.mp4 87.7 MB
9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.srt 85.6 MB
9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.mp4 85.6 MB
10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.mp4 85.3 MB
1. Getting Started/6. [Activity] LINUX Installing and Using Anaconda & Course Materials.mp4 84.1 MB
10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.mp4 83.9 MB
7. Dealing with Real-World Data/3. Data Cleaning and Normalization.mp4 82.6 MB
6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.mp4 81.7 MB
2. Statistics and Probability Refresher, and Python Practice/1. Types of Data.mp4 81.0 MB
2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions.mp4 79.0 MB
5. Recommender Systems/2. Item-Based Collaborative Filtering.mp4 78.6 MB
10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.mp4 77.8 MB
3. Predictive Models/3. [Activity] Multiple Regression, and Predicting Car Prices.mp4 77.4 MB
10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.mp4 76.2 MB
4. Machine Learning with Python/5. K-Means Clustering.mp4 75.4 MB
10. Deep Learning and Neural Networks/12. [Activity] Using CNN's for handwriting recognition.mp4 72.9 MB
10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).mp4 72.5 MB
8. Apache Spark Machine Learning on Big Data/10. TF IDF.mp4 72.2 MB
6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis.mp4 71.0 MB
3. Predictive Models/2. [Activity] Polynomial Regression.mp4 70.0 MB
7. Dealing with Real-World Data/1. BiasVariance Tradeoff.mp4 69.5 MB
4. Machine Learning with Python/13. Ensemble Learning.mp4 68.4 MB
9. Experimental Design ML in the Real World/3. T-Tests and P-Values.mp4 68.1 MB
10. Deep Learning and Neural Networks/4. Deep Learning Details.srt 67.4 MB
10. Deep Learning and Neural Networks/4. Deep Learning Details.mp4 67.3 MB
12. You made it!/1. More to Explore.mp4 67.2 MB
2. Statistics and Probability Refresher, and Python Practice/3. [Activity] Using mean, median, and mode in Python.mp4 64.9 MB
1. Getting Started/1. Introduction.mp4 62.5 MB
2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.mp4 61.8 MB
4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp4 61.0 MB
4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.mp4 60.1 MB
2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.mp4 58.9 MB
8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.mp4 57.4 MB
11. Final Project/1. Your final project assignment.mp4 54.1 MB
7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.mp4 51.4 MB
7. Dealing with Real-World Data/10. Binning, Transforming, Encoding, Scaling, and Shuffling.mp4 50.2 MB
3. Predictive Models/4. Multi-Level Models.mp4 49.8 MB
4. Machine Learning with Python/14. Support Vector Machines (SVM) Overview.mp4 46.9 MB
4. Machine Learning with Python/15. [Activity] Using SVM to cluster people using scikit-learn.mp4 46.1 MB
7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.mp4 43.7 MB
4. Machine Learning with Python/3. Bayesian Methods Concepts.mp4 42.7 MB
6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.mp4 42.2 MB
10. Deep Learning and Neural Networks/19. Learning More about Deep Learning.mp4 40.5 MB
7. Dealing with Real-World Data/5. Normalizing numerical data.mp4 40.1 MB
7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp4 38.1 MB
7. Dealing with Real-World Data/6. [Activity] Detecting outliers.mp4 38.1 MB
4. Machine Learning with Python/7. Measuring Entropy.mp4 36.7 MB
9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.mp4 36.5 MB
10. Deep Learning and Neural Networks/17. Deep Learning Regularization with Dropout and Early Stopping.mp4 35.3 MB
9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.mp4 34.6 MB
1. Getting Started/7. Python Basics, Part 1 [Optional].mp4 34.6 MB
2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.mp4 31.5 MB
6. More Data Mining and Machine Learning Techniques/9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp4 27.0 MB
2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.mp4 23.1 MB
1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].mp4 22.1 MB
1. Getting Started/8. [Activity] Python Basics, Part 2 [Optional].mp4 21.6 MB
1. Getting Started/2. Udemy 101 Getting the Most From This Course.mp4 20.7 MB
10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp4 19.3 MB
6. More Data Mining and Machine Learning Techniques/8. Understanding a Confusion Matrix.mp4 15.6 MB
4. Machine Learning with Python/9. [Activity] MAC Installing Graphviz.mp4 15.5 MB
1. Getting Started/9. [Activity] Python Basics, Part 3 [Optional].mp4 10.6 MB
4. Machine Learning with Python/10. [Activity] LINUX Installing Graphviz.mp4 7.4 MB
4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.mp4 2.2 MB
2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.srt 30.7 kB
2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.srt 29.3 kB
6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.srt 29.2 kB
6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.srt 29.2 kB
2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.srt 29.1 kB
2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.srt 29.0 kB
8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.srt 28.8 kB
2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.srt 26.5 kB
2. Statistics and Probability Refresher, and Python Practice/4. [Activity] Variation and Standard Deviation.srt 26.5 kB
3. Predictive Models/1. [Activity] Linear Regression.srt 26.3 kB
7. Dealing with Real-World Data/2. [Activity] K-Fold Cross-Validation to avoid overfitting.srt 25.1 kB
11. Final Project/2. Final project review.srt 25.1 kB
8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).srt 25.0 kB
7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.srt 24.4 kB
10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.srt 24.3 kB
10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.srt 23.9 kB
5. Recommender Systems/5. [Activity] Making Movie Recommendations to People.srt 23.2 kB
10. Deep Learning and Neural Networks/5. Introducing Tensorflow.srt 23.0 kB
6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.srt 23.0 kB
4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.srt 23.0 kB
9. Experimental Design ML in the Real World/6. AB Test Gotchas.srt 22.4 kB
10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.srt 22.0 kB
10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.srt 22.0 kB
8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.srt 21.7 kB
6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.srt 21.7 kB
10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.srt 21.6 kB
3. Predictive Models/3. [Activity] Multiple Regression, and Predicting Car Prices.srt 21.6 kB
4. Machine Learning with Python/11. Decision Trees Concepts.srt 21.6 kB
4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.srt 21.4 kB
5. Recommender Systems/3. [Activity] Finding Movie Similarities.srt 20.6 kB
5. Recommender Systems/2. Item-Based Collaborative Filtering.srt 20.5 kB
10. Deep Learning and Neural Networks/11. Convolutional Neural Networks (CNN's).srt 20.3 kB
10. Deep Learning and Neural Networks/18. The Ethics of Deep Learning.srt 20.3 kB
6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.srt 20.2 kB
5. Recommender Systems/1. User-Based Collaborative Filtering.srt 19.8 kB
10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.srt 19.5 kB
1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.srt 19.3 kB
10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).srt 18.9 kB
1. Getting Started/11. Introducing the Pandas Library [Optional].srt 18.5 kB
8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.srt 18.2 kB
3. Predictive Models/2. [Activity] Polynomial Regression.srt 18.0 kB
4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.srt 17.8 kB
4. Machine Learning with Python/5. K-Means Clustering.srt 17.6 kB
7. Dealing with Real-World Data/3. Data Cleaning and Normalization.srt 17.5 kB
10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.srt 17.2 kB
5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.srt 17.2 kB
2. Statistics and Probability Refresher, and Python Practice/1. Types of Data.srt 16.6 kB
2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions.srt 16.5 kB
9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.srt 15.8 kB
2. Statistics and Probability Refresher, and Python Practice/3. [Activity] Using mean, median, and mode in Python.srt 15.4 kB
4. Machine Learning with Python/15. [Activity] Using SVM to cluster people using scikit-learn.srt 15.2 kB
1. Getting Started/6. [Activity] LINUX Installing and Using Anaconda & Course Materials.srt 15.0 kB
4. Machine Learning with Python/13. Ensemble Learning.srt 14.9 kB
1. Getting Started/5. [Activity] MAC Installing and Using Anaconda & Course Materials.srt 14.8 kB
7. Dealing with Real-World Data/1. BiasVariance Tradeoff.srt 14.7 kB
7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.srt 14.7 kB
7. Dealing with Real-World Data/10. Binning, Transforming, Encoding, Scaling, and Shuffling.srt 14.6 kB
8. Apache Spark Machine Learning on Big Data/10. TF IDF.srt 14.4 kB
8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.srt 14.2 kB
10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.srt 14.2 kB
10. Deep Learning and Neural Networks/12. [Activity] Using CNN's for handwriting recognition.srt 14.1 kB
5. Recommender Systems/6. [Exercise] Improve the recommender's results.srt 13.5 kB
9. Experimental Design ML in the Real World/3. T-Tests and P-Values.srt 13.5 kB
4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.srt 13.4 kB
2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.srt 13.3 kB
8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.srt 13.2 kB
6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis.srt 12.6 kB
8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.srt 12.3 kB
10. Deep Learning and Neural Networks/17. Deep Learning Regularization with Dropout and Early Stopping.srt 12.3 kB
7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.srt 12.1 kB
11. Final Project/1. Your final project assignment.srt 11.8 kB
4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.srt 11.8 kB
2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.srt 11.8 kB
8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.srt 11.7 kB
7. Dealing with Real-World Data/6. [Activity] Detecting outliers.srt 11.7 kB
6. More Data Mining and Machine Learning Techniques/9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).srt 11.1 kB
3. Predictive Models/4. Multi-Level Models.srt 10.9 kB
8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.srt 10.8 kB
4. Machine Learning with Python/14. Support Vector Machines (SVM) Overview.srt 10.1 kB
7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.srt 10.1 kB
6. More Data Mining and Machine Learning Techniques/8. Understanding a Confusion Matrix.srt 9.9 kB
6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.srt 9.2 kB
4. Machine Learning with Python/3. Bayesian Methods Concepts.srt 9.0 kB
9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.srt 8.5 kB
10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.srt 8.5 kB
1. Getting Started/7. Python Basics, Part 1 [Optional].srt 7.9 kB
7. Dealing with Real-World Data/5. Normalizing numerical data.srt 7.8 kB
1. Getting Started/8. [Activity] Python Basics, Part 2 [Optional].srt 7.8 kB
2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.srt 7.8 kB
12. You made it!/3. Bonus Lecture More courses to explore!.html 7.5 kB
12. You made it!/1. More to Explore.srt 7.4 kB
4. Machine Learning with Python/7. Measuring Entropy.srt 7.1 kB
1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].srt 6.1 kB
1. Getting Started/1. Introduction.srt 4.9 kB
1. Getting Started/9. [Activity] Python Basics, Part 3 [Optional].srt 4.3 kB
1. Getting Started/2. Udemy 101 Getting the Most From This Course.srt 4.1 kB
2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.srt 4.1 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 3.2 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
10. Deep Learning and Neural Networks/6. Important note about Tensorflow 2.html 1.0 kB
4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.srt 689 Bytes
8. Apache Spark Machine Learning on Big Data/1. Warning about Java 11 and Spark 2.4!.html 650 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
How you can help GetFreeCourses.Co.txt 182 Bytes
6. More Data Mining and Machine Learning Techniques/6.2 Pac-Man Example.html 145 Bytes
6. More Data Mining and Machine Learning Techniques/6.1 Cat and Mouse Example.html 140 Bytes
6. More Data Mining and Machine Learning Techniques/6.3 Python Markov Decision Process Toolbox.html 119 Bytes
GetFreeCourses.Co.url 116 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

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