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

Creation Time	Nov. 2, 2023, 3:16 p.m.
Last Access Time	Sept. 19, 2025, 5:04 a.m.
File Size	1004.5 MB
Keywords	FreeCourseSite Python Science - Udemy Machine Supervised com Learning in Data
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Total Files	105
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9. Appendix/3. Windows-Focused Environment Setup 2018.mp4 186.4 MB
3. Naive Bayes and Bayes Classifiers/1. Bayes Classifier Intuition (Continuous).mp4 80.2 MB
9. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
3. Naive Bayes and Bayes Classifiers/2. Bayes Classifier Intuition (Discrete).mp4 50.1 MB
9. Appendix/4. How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn.mp4 43.9 MB
9. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39.0 MB
9. Appendix/12. What order should I take your courses in (part 2).mp4 37.6 MB
2. K-Nearest Neighbor/7. Effect of K.mp4 35.8 MB
4. Decision Trees/6. Decision Tree in Code.mp4 30.3 MB
9. Appendix/11. What order should I take your courses in (part 1).mp4 29.3 MB
9. Appendix/5. How to Code by Yourself (part 1).mp4 24.5 MB
4. Decision Trees/1. Decision Tree Intuition.mp4 20.4 MB
2. K-Nearest Neighbor/3. KNN in Code with MNIST.mp4 18.0 MB
2. K-Nearest Neighbor/1. K-Nearest Neighbor Intuition.mp4 17.6 MB
6. Practical Machine Learning/5. Sci-Kit Learn.mp4 15.8 MB
3. Naive Bayes and Bayes Classifiers/3. Naive Bayes.mp4 15.7 MB
9. Appendix/6. How to Code by Yourself (part 2).mp4 14.8 MB
3. Naive Bayes and Bayes Classifiers/5. Naive Bayes in Code with MNIST.mp4 14.4 MB
4. Decision Trees/4. Maximizing Information Gain.mp4 14.0 MB
5. Perceptrons/2. Perceptron in Code.mp4 13.8 MB
9. Appendix/7. How to Succeed in this Course (Long Version).mp4 13.0 MB
5. Perceptrons/1. Perceptron Concepts.mp4 12.2 MB
7. Building a Machine Learning Web Service/2. Building a Machine Learning Web Service Code.mp4 11.9 MB
6. Practical Machine Learning/6. Regression with Sci-Kit Learn is Easy.mp4 10.8 MB
3. Naive Bayes and Bayes Classifiers/8. Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA).mp4 10.3 MB
5. Perceptrons/3. Perceptron for MNIST and XOR.mp4 8.7 MB
6. Practical Machine Learning/3. Comparison to Deep Learning.mp4 8.7 MB
2. K-Nearest Neighbor/2. K-Nearest Neighbor Concepts.mp4 8.6 MB
4. Decision Trees/2. Decision Tree Basics.mp4 8.3 MB
9. Appendix/10. Python 2 vs Python 3.mp4 7.8 MB
2. K-Nearest Neighbor/4. When KNN Can Fail.mp4 7.7 MB
1. Introduction and Review/1. Introduction and Outline.mp4 7.6 MB
6. Practical Machine Learning/1. Hyperparameters and Cross-Validation.mp4 7.4 MB
3. Naive Bayes and Bayes Classifiers/6. Non-Naive Bayes.mp4 7.3 MB
7. Building a Machine Learning Web Service/1. Building a Machine Learning Web Service Concepts.mp4 7.2 MB
6. Practical Machine Learning/2. Feature Extraction and Feature Selection.mp4 7.1 MB
4. Decision Trees/3. Information Entropy.mp4 7.0 MB
4. Decision Trees/5. Choosing the Best Split.mp4 6.7 MB
5. Perceptrons/4. Perceptron Loss Function.mp4 6.3 MB
8. Conclusion/1. What’s Next Support Vector Machines and Ensemble Methods (e.g. Random Forest).mp4 6.3 MB
1. Introduction and Review/2. Review of Important Concepts.mp4 6.0 MB
3. Naive Bayes and Bayes Classifiers/4. Naive Bayes Handwritten Example.mp4 5.8 MB
6. Practical Machine Learning/4. Multiclass Classification.mp4 5.7 MB
9. Appendix/1. What is the Appendix.mp4 5.5 MB
2. K-Nearest Neighbor/6. KNN for the Donut Problem.mp4 5.4 MB
3. Naive Bayes and Bayes Classifiers/9. Generative vs Discriminative Models.mp4 5.1 MB
3. Naive Bayes and Bayes Classifiers/7. Bayes Classifier in Code with MNIST.mp4 4.4 MB
2. K-Nearest Neighbor/5. KNN for the XOR Problem.mp4 4.3 MB
9. Appendix/2. Where to get Udemy coupons and FREE deep learning material.mp4 4.0 MB
1. Introduction and Review/3. Where to get the Code and Data.mp4 3.9 MB
1. Introduction and Review/4. How to Succeed in this Course.mp4 3.3 MB
9. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 27.8 KB
9. Appendix/12. What order should I take your courses in (part 2).vtt 20.2 KB
3. Naive Bayes and Bayes Classifiers/1. Bayes Classifier Intuition (Continuous).vtt 20.0 KB
9. Appendix/5. How to Code by Yourself (part 1).vtt 19.8 KB
9. Appendix/3. Windows-Focused Environment Setup 2018.vtt 17.4 KB
9. Appendix/11. What order should I take your courses in (part 1).vtt 14.1 KB
9. Appendix/7. How to Succeed in this Course (Long Version).vtt 12.9 KB
9. Appendix/4. How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn.vtt 12.4 KB
9. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt 12.2 KB
9. Appendix/6. How to Code by Yourself (part 2).vtt 11.6 KB
3. Naive Bayes and Bayes Classifiers/2. Bayes Classifier Intuition (Discrete).vtt 11.3 KB
3. Naive Bayes and Bayes Classifiers/3. Naive Bayes.vtt 10.1 KB
6. Practical Machine Learning/5. Sci-Kit Learn.vtt 9.8 KB
4. Decision Trees/6. Decision Tree in Code.vtt 9.6 KB
4. Decision Trees/4. Maximizing Information Gain.vtt 8.5 KB
5. Perceptrons/1. Perceptron Concepts.vtt 7.7 KB
7. Building a Machine Learning Web Service/2. Building a Machine Learning Web Service Code.vtt 6.5 KB
3. Naive Bayes and Bayes Classifiers/8. Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA).vtt 6.5 KB
2. K-Nearest Neighbor/3. KNN in Code with MNIST.vtt 6.4 KB
2. K-Nearest Neighbor/7. Effect of K.vtt 6.2 KB
2. K-Nearest Neighbor/2. K-Nearest Neighbor Concepts.vtt 5.5 KB
4. Decision Trees/2. Decision Tree Basics.vtt 5.4 KB
6. Practical Machine Learning/3. Comparison to Deep Learning.vtt 5.4 KB
9. Appendix/10. Python 2 vs Python 3.vtt 5.4 KB
6. Practical Machine Learning/6. Regression with Sci-Kit Learn is Easy.vtt 5.3 KB
1. Introduction and Review/1. Introduction and Outline.vtt 5.0 KB
4. Decision Trees/1. Decision Tree Intuition.vtt 4.9 KB
7. Building a Machine Learning Web Service/1. Building a Machine Learning Web Service Concepts.vtt 4.7 KB
6. Practical Machine Learning/1. Hyperparameters and Cross-Validation.vtt 4.5 KB
2. K-Nearest Neighbor/1. K-Nearest Neighbor Intuition.vtt 4.3 KB
3. Naive Bayes and Bayes Classifiers/6. Non-Naive Bayes.vtt 4.3 KB
5. Perceptrons/4. Perceptron Loss Function.vtt 4.3 KB
6. Practical Machine Learning/2. Feature Extraction and Feature Selection.vtt 4.3 KB
4. Decision Trees/5. Choosing the Best Split.vtt 4.2 KB
3. Naive Bayes and Bayes Classifiers/5. Naive Bayes in Code with MNIST.vtt 4.2 KB
5. Perceptrons/2. Perceptron in Code.vtt 4.1 KB
2. K-Nearest Neighbor/4. When KNN Can Fail.vtt 4.0 KB
1. Introduction and Review/2. Review of Important Concepts.vtt 3.9 KB
4. Decision Trees/3. Information Entropy.vtt 3.8 KB
6. Practical Machine Learning/4. Multiclass Classification.vtt 3.6 KB
1. Introduction and Review/4. How to Succeed in this Course.vtt 3.5 KB
9. Appendix/1. What is the Appendix.vtt 3.3 KB
3. Naive Bayes and Bayes Classifiers/4. Naive Bayes Handwritten Example.vtt 3.2 KB
8. Conclusion/1. What’s Next Support Vector Machines and Ensemble Methods (e.g. Random Forest).vtt 3.1 KB
9. Appendix/2. Where to get Udemy coupons and FREE deep learning material.vtt 3.0 KB
3. Naive Bayes and Bayes Classifiers/9. Generative vs Discriminative Models.vtt 2.8 KB
1. Introduction and Review/3. Where to get the Code and Data.vtt 2.3 KB
2. K-Nearest Neighbor/6. KNN for the Donut Problem.vtt 2.2 KB
5. Perceptrons/3. Perceptron for MNIST and XOR.vtt 2.0 KB
2. K-Nearest Neighbor/5. KNN for the XOR Problem.vtt 1.9 KB
3. Naive Bayes and Bayes Classifiers/7. Bayes Classifier in Code with MNIST.vtt 1.4 KB
[FCS Forum].url 133 bytes
[FreeCourseSite.com].url 127 bytes
[CourseClub.NET].url 123 bytes

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

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