[FreeTutorials.Us] data-science-supervised-machine-learning-in-python
| 文件类型 | 收录时间 | 最后活跃 | 资源热度 | 文件大小 | 文件数量 |
|---|---|---|---|---|---|
| 视频 | 2018-10-8 13:37 | 2024-10-25 21:24 | 115 | 409.3 MB | 38 |
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文件列表
- 01 Introduction and Review/001 Introduction and Outline.mp47.62MB
- 01 Introduction and Review/002 Review of Important Concepts.mp46.01MB
- 01 Introduction and Review/003 Where to get the Code and Data.mp43.86MB
- 01 Introduction and Review/004 How to Succeed in this Course.mp48.78MB
- 02 K-Nearest Neighbor/005 K-Nearest Neighbor Concepts.mp48.6MB
- 02 K-Nearest Neighbor/006 KNN in Code with MNIST.mp417.96MB
- 02 K-Nearest Neighbor/007 When KNN Can Fail.mp47.7MB
- 02 K-Nearest Neighbor/008 KNN for the XOR Problem.mp44.26MB
- 02 K-Nearest Neighbor/009 KNN for the Donut Problem.mp45.42MB
- 03 Naive Bayes and Bayes Classifiers/010 Naive Bayes.mp415.69MB
- 03 Naive Bayes and Bayes Classifiers/011 Naive Bayes Handwritten Example.mp45.83MB
- 03 Naive Bayes and Bayes Classifiers/012 Naive Bayes in Code with MNIST.mp414.43MB
- 03 Naive Bayes and Bayes Classifiers/013 Non-Naive Bayes.mp47.3MB
- 03 Naive Bayes and Bayes Classifiers/014 Bayes Classifier in Code with MNIST.mp44.44MB
- 03 Naive Bayes and Bayes Classifiers/015 Linear Discriminant Analysis LDA and Quadratic Discriminant Analysis QDA.mp410.35MB
- 03 Naive Bayes and Bayes Classifiers/016 Generative vs Discriminative Models.mp45.12MB
- 04 Decision Trees/017 Decision Tree Basics.mp48.28MB
- 04 Decision Trees/018 Information Entropy.mp46.99MB
- 04 Decision Trees/019 Maximizing Information Gain.mp413.96MB
- 04 Decision Trees/020 Choosing the Best Split.mp46.72MB
- 04 Decision Trees/021 Decision Tree in Code.mp430.33MB
- 05 Perceptrons/022 Perceptron Concepts.mp412.22MB
- 05 Perceptrons/023 Perceptron in Code.mp413.76MB
- 05 Perceptrons/024 Perceptron for MNIST and XOR.mp48.74MB
- 05 Perceptrons/025 Perceptron Loss Function.mp46.87MB
- 06 Practical Machine Learning/026 Hyperparameters and Cross-Validation.mp47.43MB
- 06 Practical Machine Learning/027 Feature Extraction and Feature Selection.mp47.09MB
- 06 Practical Machine Learning/028 Comparison to Deep Learning.mp48.7MB
- 06 Practical Machine Learning/029 Multiclass Classification.mp45.65MB
- 06 Practical Machine Learning/030 Sci-Kit Learn.mp415.81MB
- 06 Practical Machine Learning/031 Regression with Sci-Kit Learn is Easy.mp410.75MB
- 07 Building a Machine Learning Web Service/032 Building a Machine Learning Web Service Concepts.mp47.24MB
- 07 Building a Machine Learning Web Service/033 Building a Machine Learning Web Service Code.mp411.87MB
- 08 Conclusion/034 Whats Next Support Vector Machines and Ensemble Methods e.g. Random Forest.mp46.27MB
- 09 Appendix/035 How to install Numpy Scipy Matplotlib and Sci-Kit Learn.mp443.92MB
- 09 Appendix/036 How to Code by Yourself part 1.mp424.53MB
- 09 Appendix/037 How to Code by Yourself part 2.mp414.8MB
- 09 Appendix/038 Where to get Udemy coupons and FREE deep learning material.mp44.02MB
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