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文档列表

09 Appendix/035 How to install Numpy Scipy Matplotlib and Sci-Kit Learn.mp4 46.0 MB
04 Decision Trees/021 Decision Tree in Code.mp4 31.8 MB
09 Appendix/036 How to Code by Yourself part 1.mp4 25.7 MB
02 K-Nearest Neighbor/006 KNN in Code with MNIST.mp4 18.8 MB
06 Practical Machine Learning/030 Sci-Kit Learn.mp4 16.6 MB
03 Naive Bayes and Bayes Classifiers/010 Naive Bayes.mp4 16.5 MB
09 Appendix/037 How to Code by Yourself part 2.mp4 15.5 MB
03 Naive Bayes and Bayes Classifiers/012 Naive Bayes in Code with MNIST.mp4 15.1 MB
04 Decision Trees/019 Maximizing Information Gain.mp4 14.6 MB
05 Perceptrons/023 Perceptron in Code.mp4 14.4 MB
05 Perceptrons/022 Perceptron Concepts.mp4 12.8 MB
07 Building a Machine Learning Web Service/033 Building a Machine Learning Web Service Code.mp4 12.4 MB
06 Practical Machine Learning/031 Regression with Sci-Kit Learn is Easy.mp4 11.3 MB
03 Naive Bayes and Bayes Classifiers/015 Linear Discriminant Analysis LDA and Quadratic Discriminant Analysis QDA.mp4 10.9 MB
01 Introduction and Review/004 How to Succeed in this Course.mp4 9.2 MB
05 Perceptrons/024 Perceptron for MNIST and XOR.mp4 9.2 MB
06 Practical Machine Learning/028 Comparison to Deep Learning.mp4 9.1 MB
02 K-Nearest Neighbor/005 K-Nearest Neighbor Concepts.mp4 9.0 MB
04 Decision Trees/017 Decision Tree Basics.mp4 8.7 MB
02 K-Nearest Neighbor/007 When KNN Can Fail.mp4 8.1 MB
01 Introduction and Review/001 Introduction and Outline.mp4 8.0 MB
06 Practical Machine Learning/026 Hyperparameters and Cross-Validation.mp4 7.8 MB
03 Naive Bayes and Bayes Classifiers/013 Non-Naive Bayes.mp4 7.7 MB
07 Building a Machine Learning Web Service/032 Building a Machine Learning Web Service Concepts.mp4 7.6 MB
06 Practical Machine Learning/027 Feature Extraction and Feature Selection.mp4 7.4 MB
04 Decision Trees/018 Information Entropy.mp4 7.3 MB
05 Perceptrons/025 Perceptron Loss Function.mp4 7.2 MB
04 Decision Trees/020 Choosing the Best Split.mp4 7.0 MB
08 Conclusion/034 Whats Next Support Vector Machines and Ensemble Methods e.g. Random Forest.mp4 6.6 MB
01 Introduction and Review/002 Review of Important Concepts.mp4 6.3 MB
03 Naive Bayes and Bayes Classifiers/011 Naive Bayes Handwritten Example.mp4 6.1 MB
06 Practical Machine Learning/029 Multiclass Classification.mp4 5.9 MB
02 K-Nearest Neighbor/009 KNN for the Donut Problem.mp4 5.7 MB
03 Naive Bayes and Bayes Classifiers/016 Generative vs Discriminative Models.mp4 5.4 MB
03 Naive Bayes and Bayes Classifiers/014 Bayes Classifier in Code with MNIST.mp4 4.7 MB
02 K-Nearest Neighbor/008 KNN for the XOR Problem.mp4 4.5 MB
09 Appendix/038 Where to get Udemy coupons and FREE deep learning material.mp4 4.2 MB
01 Introduction and Review/003 Where to get the Code and Data.mp4 4.0 MB
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