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种子名称： [FreeCourseSite.com] Udemy - Data Science Supervised Machine Learning in Python
文件类型： 视频
文件数目： 51个文件
文件大小： 1004.15 MB
收录时间： 2020-1-23 12:45
已经下载： 3次
资源热度： 123
最近下载： 2024-6-26 09:23

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[FreeCourseSite.com] Udemy - Data Science Supervised Machine Learning in Python.torrent

1. Introduction and Review/1. Introduction and Outline.mp47.63MB

1. Introduction and Review/2. Review of Important Concepts.mp46.01MB

1. Introduction and Review/3. Where to get the Code and Data.mp43.86MB

1. Introduction and Review/4. How to Succeed in this Course.mp43.3MB

2. K-Nearest Neighbor/1. K-Nearest Neighbor Intuition.mp417.58MB

2. K-Nearest Neighbor/2. K-Nearest Neighbor Concepts.mp48.6MB

2. K-Nearest Neighbor/3. KNN in Code with MNIST.mp417.96MB

2. K-Nearest Neighbor/4. When KNN Can Fail.mp47.7MB

2. K-Nearest Neighbor/5. KNN for the XOR Problem.mp44.26MB

2. K-Nearest Neighbor/6. KNN for the Donut Problem.mp45.43MB

2. K-Nearest Neighbor/7. Effect of K.mp435.8MB

3. Naive Bayes and Bayes Classifiers/1. Bayes Classifier Intuition (Continuous).mp480.16MB

3. Naive Bayes and Bayes Classifiers/2. Bayes Classifier Intuition (Discrete).mp450.1MB

3. Naive Bayes and Bayes Classifiers/3. Naive Bayes.mp415.7MB

3. Naive Bayes and Bayes Classifiers/4. Naive Bayes Handwritten Example.mp45.84MB

3. Naive Bayes and Bayes Classifiers/5. Naive Bayes in Code with MNIST.mp414.43MB

3. Naive Bayes and Bayes Classifiers/6. Non-Naive Bayes.mp47.31MB

3. Naive Bayes and Bayes Classifiers/7. Bayes Classifier in Code with MNIST.mp44.44MB

3. Naive Bayes and Bayes Classifiers/8. Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA).mp410.35MB

3. Naive Bayes and Bayes Classifiers/9. Generative vs Discriminative Models.mp45.12MB

4. Decision Trees/1. Decision Tree Intuition.mp420.37MB

4. Decision Trees/2. Decision Tree Basics.mp48.29MB

4. Decision Trees/3. Information Entropy.mp47MB

4. Decision Trees/4. Maximizing Information Gain.mp413.96MB

4. Decision Trees/5. Choosing the Best Split.mp46.72MB

4. Decision Trees/6. Decision Tree in Code.mp430.34MB

5. Perceptrons/1. Perceptron Concepts.mp412.22MB

5. Perceptrons/2. Perceptron in Code.mp413.76MB

5. Perceptrons/3. Perceptron for MNIST and XOR.mp48.74MB

5. Perceptrons/4. Perceptron Loss Function.mp46.28MB

6. Practical Machine Learning/1. Hyperparameters and Cross-Validation.mp47.43MB

6. Practical Machine Learning/2. Feature Extraction and Feature Selection.mp47.1MB

6. Practical Machine Learning/3. Comparison to Deep Learning.mp48.7MB

6. Practical Machine Learning/4. Multiclass Classification.mp45.65MB

6. Practical Machine Learning/5. Sci-Kit Learn.mp415.81MB

6. Practical Machine Learning/6. Regression with Sci-Kit Learn is Easy.mp410.76MB

7. Building a Machine Learning Web Service/1. Building a Machine Learning Web Service Concepts.mp47.24MB

7. Building a Machine Learning Web Service/2. Building a Machine Learning Web Service Code.mp411.87MB

8. Conclusion/1. What’s Next Support Vector Machines and Ensemble Methods (e.g. Random Forest).mp46.27MB

9. Appendix/1. What is the Appendix.mp45.45MB

9. Appendix/10. Python 2 vs Python 3.mp47.83MB

9. Appendix/11. What order should I take your courses in (part 1).mp429.32MB

9. Appendix/12. What order should I take your courses in (part 2).mp437.62MB

9. Appendix/2. Where to get Udemy coupons and FREE deep learning material.mp44.02MB

9. Appendix/3. Windows-Focused Environment Setup 2018.mp4186.38MB

9. Appendix/4. How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn.mp443.92MB

9. Appendix/5. How to Code by Yourself (part 1).mp424.53MB

9. Appendix/6. How to Code by Yourself (part 2).mp414.8MB

9. Appendix/7. How to Succeed in this Course (Long Version).mp412.99MB

9. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.96MB

9. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp478.25MB