Machine learning Combat Machines learning in Action code video project case

Machinelearning

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The first part of the classification

• 1.) Machine Learning Basics
• 2.) K-Nearest neighbor algorithm
• 3.) Decision Tree
• 4.) Classification method based on probability theory: Naive Bayes
• 5.) Logistic regression
• 6.) Support Vector Machine
• 7.) Integration method-random forest and AdaBoost

The second part uses the regression to predict the numerical data

• 8.) Predicting numerical data: regression
• 9.) Tree regression

Part III unsupervised Learning

• 10.) Use the K-means clustering algorithm to group unlabeled data: K-means Clustering
• 11.) Correlation analysis using the Apriori algorithm
• 12.) Use the FP-GROWTH algorithm to efficiently discover frequent itemsets

Part IV Other tools

• 13.) Use PCA to simplify data
• 14.) Simplify data with SVD
• 15.) Big Data and MapReduce

Part V Project Combat (non-textbook content)

• 16.) Recommendation System

Periodic summary

• Summary of the first phase of 2017-04-08_

• Appendix A, getting Started with Python
• Appendix B Linear Algebra
• Appendix C Review of probability theory
• Appendix D Resources
• Index
• Copyright notice
• APACHECN (Apache Chinese web) Maintenance update
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Machine learning Combat Machines learning in Action code video project case