[FreeCourseSite.com] Udemy - Machine Learning, Data Science and Deep Learning with Python torrent, magnet, bt

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[FreeCourseSite.com] Udemy - Machine Learning, Data Science and Deep Learning with Python

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2021-03-12

Keywords:

FreeCourseSite com Udemy Machine Learning Data Science and Deep Learning with Python

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File Name

Size

0. Websites you may like/[CourseClub.ME].url

122B

0. Websites you may like/[FCS Forum].url

133B

0. Websites you may like/[FreeCourseSite.com].url

127B

1. Getting Started/1. Introduction.mp4

59.6MB

1. Getting Started/1. Introduction.srt

4.75KB

1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].mp4

21.12MB

1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].srt

6KB

1. Getting Started/11. Introducing the Pandas Library [Optional].mp4

123.1MB

1. Getting Started/11. Introducing the Pandas Library [Optional].srt

18.05KB

1. Getting Started/2. Udemy 101 Getting the Most From This Course.mp4

19.77MB

1. Getting Started/2. Udemy 101 Getting the Most From This Course.srt

4.04KB

1. Getting Started/3. Installation Getting Started.html

1.21KB

1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4

101.15MB

1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.srt

17.03KB

1. Getting Started/5. [Activity] MAC Installing and Using Anaconda & Course Materials.mp4

93.97MB

1. Getting Started/5. [Activity] MAC Installing and Using Anaconda & Course Materials.srt

14.17KB

1. Getting Started/6. [Activity] LINUX Installing and Using Anaconda & Course Materials.mp4

82.69MB

1. Getting Started/6. [Activity] LINUX Installing and Using Anaconda & Course Materials.srt

15.01KB

1. Getting Started/7. Python Basics, Part 1 [Optional].mp4

32.97MB

1. Getting Started/7. Python Basics, Part 1 [Optional].srt

7.76KB

1. Getting Started/8. [Activity] Python Basics, Part 2 [Optional].mp4

20.64MB

1. Getting Started/8. [Activity] Python Basics, Part 2 [Optional].srt

7.63KB

1. Getting Started/9. [Activity] Python Basics, Part 3 [Optional].mp4

10.09MB

1. Getting Started/9. [Activity] Python Basics, Part 3 [Optional].srt

4.24KB

10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.mp4

74.18MB

10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.srt

21.52KB

10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.mp4

88.19MB

10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.srt

21.14KB

10. Deep Learning and Neural Networks/11. Convolutional Neural Networks (CNN's).mp4

93.09MB

10. Deep Learning and Neural Networks/11. Convolutional Neural Networks (CNN's).srt

19.86KB

10. Deep Learning and Neural Networks/12. [Activity] Using CNN's for handwriting recognition.mp4

69.56MB

10. Deep Learning and Neural Networks/12. [Activity] Using CNN's for handwriting recognition.srt

13.76KB

10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).mp4

69.17MB

10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).srt

18.48KB

10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.mp4

81.36MB

10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.srt

16.82KB

10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.mp4

115.26MB

10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.srt

21.53KB

10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp4

18.43MB

10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.srt

8.29KB

10. Deep Learning and Neural Networks/17. Deep Learning Regularization with Dropout and Early Stopping.mp4

33.63MB

10. Deep Learning and Neural Networks/17. Deep Learning Regularization with Dropout and Early Stopping.srt

11.97KB

10. Deep Learning and Neural Networks/18. The Ethics of Deep Learning.mp4

128.24MB

10. Deep Learning and Neural Networks/18. The Ethics of Deep Learning.srt

19.84KB

10. Deep Learning and Neural Networks/19. Learning More about Deep Learning.mp4

38.64MB

10. Deep Learning and Neural Networks/19. Learning More about Deep Learning.srt

3.14KB

10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.mp4

79.98MB

10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.srt

19.07KB

10. Deep Learning and Neural Networks/3. [Activity] Deep Learning in the Tensorflow Playground.mp4

141.58MB

10. Deep Learning and Neural Networks/3. [Activity] Deep Learning in the Tensorflow Playground.srt

19.67KB

10. Deep Learning and Neural Networks/4. Deep Learning Details.mp4

64.21MB

10. Deep Learning and Neural Networks/4. Deep Learning Details.srt

16.81KB

10. Deep Learning and Neural Networks/5. Introducing Tensorflow.mp4

64.15MB

10. Deep Learning and Neural Networks/5. Introducing Tensorflow.srt

19.77KB

10. Deep Learning and Neural Networks/6. Important note about Tensorflow 2.html

644B

10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.mp4

118.22MB

10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.srt

23.06KB

10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.mp4

104.55MB

10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.srt

21.24KB

10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.mp4

92.05MB

10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.srt

23.75KB

11. Final Project/1. Your final project assignment Mammogram Classification.mp4

51.64MB

11. Final Project/1. Your final project assignment Mammogram Classification.srt

11.56KB

11. Final Project/2. Final project review.mp4

98.51MB

11. Final Project/2. Final project review.srt

24.51KB

12. You made it!/1. More to Explore.mp4

64.06MB

12. You made it!/1. More to Explore.srt

7.24KB

12. You made it!/2. Don't Forget to Leave a Rating!.html

564B

12. You made it!/3. Bonus Lecture More courses to explore!.html

7.36KB

2. Statistics and Probability Refresher, and Python Practice/1. Types of Data (Numerical, Categorical, Ordinal).mp4

77.24MB

2. Statistics and Probability Refresher, and Python Practice/1. Types of Data (Numerical, Categorical, Ordinal).srt

16.24KB

2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.mp4

116.74MB

2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.srt

25.91KB

2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.mp4

125.14MB

2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.srt

28.41KB

2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.mp4

22MB

2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.srt

3.99KB

2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.mp4

58.91MB

2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.srt

11.49KB

2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.mp4

56.15MB

2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.srt

12.95KB

2. Statistics and Probability Refresher, and Python Practice/3. [Activity] Using mean, median, and mode in Python.mp4

61.94MB

2. Statistics and Probability Refresher, and Python Practice/3. [Activity] Using mean, median, and mode in Python.srt

15.01KB

2. Statistics and Probability Refresher, and Python Practice/4. [Activity] Variation and Standard Deviation.mp4

110.86MB

2. Statistics and Probability Refresher, and Python Practice/4. [Activity] Variation and Standard Deviation.srt

25.83KB

2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.mp4

30.07MB

2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.srt

7.59KB

2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions (Normal, Binomial, Poisson, etc).mp4

75.36MB

2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions (Normal, Binomial, Poisson, etc).srt

16.08KB

2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.mp4

114.04MB

2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.srt

28.33KB

2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.mp4

129.35MB

2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.srt

28.57KB

2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.mp4

147.81MB

2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.srt

29.96KB

3. Predictive Models/1. [Activity] Linear Regression.mp4

100.46MB

3. Predictive Models/1. [Activity] Linear Regression.srt

25.7KB

3. Predictive Models/2. [Activity] Polynomial Regression.mp4

66.77MB

3. Predictive Models/2. [Activity] Polynomial Regression.srt

17.59KB

3. Predictive Models/3. [Activity] Multiple Regression, and Predicting Car Prices.mp4

73.85MB

3. Predictive Models/3. [Activity] Multiple Regression, and Predicting Car Prices.srt

21.13KB

3. Predictive Models/4. Multi-Level Models.mp4

47.46MB

3. Predictive Models/4. Multi-Level Models.srt

10.66KB

4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.mp4

98.61MB

4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.srt

20.9KB

4. Machine Learning with Python/10. [Activity] LINUX Installing Graphviz.mp4

7.05MB

4. Machine Learning with Python/10. [Activity] LINUX Installing Graphviz.srt

1.11KB

4. Machine Learning with Python/11. Decision Trees Concepts.mp4

86.53MB

4. Machine Learning with Python/11. Decision Trees Concepts.srt

21.1KB

4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.mp4

95.95MB

4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.srt

22.45KB

4. Machine Learning with Python/13. Ensemble Learning.mp4

65.21MB

4. Machine Learning with Python/13. Ensemble Learning.srt

14.55KB

4. Machine Learning with Python/14. [Activity] XGBoost.mp4

102.08MB

4. Machine Learning with Python/14. [Activity] XGBoost.srt

27.05KB

4. Machine Learning with Python/15. Support Vector Machines (SVM) Overview.mp4

44.75MB

4. Machine Learning with Python/15. Support Vector Machines (SVM) Overview.srt

9.88KB

4. Machine Learning with Python/16. [Activity] Using SVM to cluster people using scikit-learn.mp4

46.7MB

4. Machine Learning with Python/16. [Activity] Using SVM to cluster people using scikit-learn.srt

16.61KB

4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp4

58.14MB

4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.srt

13.11KB

4. Machine Learning with Python/3. Bayesian Methods Concepts.mp4

40.72MB

4. Machine Learning with Python/3. Bayesian Methods Concepts.srt

8.83KB

4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.mp4

89.08MB

4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.srt

17.42KB

4. Machine Learning with Python/5. K-Means Clustering.mp4

71.95MB

4. Machine Learning with Python/5. K-Means Clustering.srt

17.2KB

4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.mp4

57.29MB

4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.srt

11.55KB

4. Machine Learning with Python/7. Measuring Entropy.mp4

34.97MB

4. Machine Learning with Python/7. Measuring Entropy.srt

6.9KB

4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.mp4

2.06MB

4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.srt

689B

4. Machine Learning with Python/9. [Activity] MAC Installing Graphviz.mp4

14.83MB

4. Machine Learning with Python/9. [Activity] MAC Installing Graphviz.srt

1.26KB

5. Recommender Systems/1. User-Based Collaborative Filtering.mp4

86.37MB

5. Recommender Systems/1. User-Based Collaborative Filtering.srt

19.38KB

5. Recommender Systems/2. Item-Based Collaborative Filtering.mp4

75MB

5. Recommender Systems/2. Item-Based Collaborative Filtering.srt

19.99KB

5. Recommender Systems/3. [Activity] Finding Movie Similarities using Cosine Similarity.mp4

107.83MB

5. Recommender Systems/3. [Activity] Finding Movie Similarities using Cosine Similarity.srt

20.08KB

5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.mp4

94.86MB

5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.srt

16.78KB

5. Recommender Systems/5. [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering.mp4

132.55MB

5. Recommender Systems/5. [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering.srt

22.61KB

5. Recommender Systems/6. [Exercise] Improve the recommender's results.mp4

84.24MB

5. Recommender Systems/6. [Exercise] Improve the recommender's results.srt

13.2KB

6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.mp4

40.28MB

6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.srt

8.95KB

6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.mp4

142.06MB

6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.srt

28.48KB

6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis (PCA).mp4

67.74MB

6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis (PCA).srt

12.32KB

6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.mp4

109.74MB

6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.srt

21.2KB

6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.mp4

103.33MB

6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.srt

19.74KB

6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.mp4

132.26MB

6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.srt

28.5KB

6. More Data Mining and Machine Learning Techniques/6.1 Pac-Man Example.html

145B

6. More Data Mining and Machine Learning Techniques/6.2 Python Markov Decision Process Toolbox.html

119B

6. More Data Mining and Machine Learning Techniques/6.3 Cat and Mouse Example.html

140B

6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.mp4

77.96MB

6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.srt

22.49KB

6. More Data Mining and Machine Learning Techniques/8. Understanding a Confusion Matrix.mp4

14.83MB

6. More Data Mining and Machine Learning Techniques/8. Understanding a Confusion Matrix.srt

9.71KB

6. More Data Mining and Machine Learning Techniques/9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp4

25.11MB

6. More Data Mining and Machine Learning Techniques/9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).srt

10.39KB

7. Dealing with Real-World Data/1. BiasVariance Tradeoff.mp4

66.31MB

7. Dealing with Real-World Data/1. BiasVariance Tradeoff.srt

14.4KB

7. Dealing with Real-World Data/10. Binning, Transforming, Encoding, Scaling, and Shuffling.mp4

47.92MB

7. Dealing with Real-World Data/10. Binning, Transforming, Encoding, Scaling, and Shuffling.srt

14.21KB

7. Dealing with Real-World Data/2. [Activity] K-Fold Cross-Validation to avoid overfitting.mp4

76.1MB

7. Dealing with Real-World Data/2. [Activity] K-Fold Cross-Validation to avoid overfitting.srt

16.41KB

7. Dealing with Real-World Data/3. Data Cleaning and Normalization.mp4

78.76MB

7. Dealing with Real-World Data/3. Data Cleaning and Normalization.srt

17.08KB

7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.mp4

129.38MB

7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.srt

23.78KB

7. Dealing with Real-World Data/5. Normalizing numerical data.mp4

38.2MB

7. Dealing with Real-World Data/5. Normalizing numerical data.srt

7.65KB

7. Dealing with Real-World Data/6. [Activity] Detecting outliers.mp4

36.32MB

7. Dealing with Real-World Data/6. [Activity] Detecting outliers.srt

11.44KB

7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.mp4

41.71MB

7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.srt

11.83KB

7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.mp4

49.02MB

7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.srt

14.31KB

7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp4

36.34MB

7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.srt

9.88KB

8. Apache Spark Machine Learning on Big Data/1. Warning about Java 11 and Spark 3!.html

602B

8. Apache Spark Machine Learning on Big Data/10. TF IDF.mp4

68.86MB

8. Apache Spark Machine Learning on Big Data/10. TF IDF.srt

14.03KB

8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.mp4

102.99MB

8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.srt

12.85KB

8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.mp4

105.68MB

8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.srt

13.91KB

8. Apache Spark Machine Learning on Big Data/2. Spark installation notes for MacOS and Linux users.html

3.57KB

8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.mp4

83.63MB

8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.srt

12.04KB

8. Apache Spark Machine Learning on Big Data/3.1 winutils.exe.html

108B

8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.mp4

111.98MB

8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.srt

10.59KB

8. Apache Spark Machine Learning on Big Data/4.1 winutils.exe.html

108B

8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.mp4

89.86MB

8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.srt

21.21KB

8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).mp4

98.52MB

8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).srt

24.41KB

8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.mp4

54.74MB

8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.srt

11.46KB

8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.mp4

134.01MB

8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.srt

28.1KB

8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.mp4

117.86MB

8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.srt

17.73KB

9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.mp4

33.04MB

9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.srt

15.42KB

9. Experimental Design ML in the Real World/2. AB Testing Concepts.mp4

97.48MB

9. Experimental Design ML in the Real World/2. AB Testing Concepts.srt

20.18KB

9. Experimental Design ML in the Real World/3. T-Tests and P-Values.mp4

64.91MB

9. Experimental Design ML in the Real World/3. T-Tests and P-Values.srt

13.16KB

9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.mp4

81.62MB

9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.srt

13.68KB

9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.mp4

34.84MB

9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.srt

8.34KB

9. Experimental Design ML in the Real World/6. AB Test Gotchas.mp4

96.1MB

9. Experimental Design ML in the Real World/6. AB Test Gotchas.srt

21.88KB

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