Click to download-Python machine learning training camp (2020 version)
extraction code: ndvx
Python machine learning video tutorial, recorded in 2020
The Python Fun Machine Learning (Pure Manual Code Implementation) course aims to help students lay a solid foundation in the field of machine learning.
The course focuses on the explanation of algorithm principles and the derivation of mathematical formulas, and provides a complete code implementation based on the Python language. The function of each module is realized from scratch (non-calling toolkit) and the algorithm workflow and implementation method are demonstrated through code examples.
Chapter 1: Derivation of linear regression principle
Chapter 2: Linear regression code implementation
Chapter 3: Model evaluation method
Chapter 4: Linear regression experimental analysis
Chapter 5: Logistic regression principle derivation
Chapter 6: Logistic regression code implementation
Chapter 7: Logistic regression experimental analysis
Chapter 8 : Clustering Algorithm-Kmeans&Dbscan Principle
Chapter 9: Kmeans Code Implementation
Chapter 10: Clustering Algorithm Experimental Analysis
Chapter 11: Decision Tree Principle
Chapter 12: Decision Tree Code Implementation
Chapter 13: Decision Tree Experimental Analysis
Chapter 14: Integrated Algorithm Principle
Chapter 15: Integrated Algorithm Experiment Analysis
Chapter 16: Support Vector Machine Principle Derivation
Chapter 17: Support Vector Machine Experimental Analysis
Chapter 18: Neural Network Algorithm Principle
Chapter 19: Neural Network Code Implementation
Chapter 20: Bayesian Algorithm Principle
Chapter 21: Bayesian Code Implementation
Chapter 22: Association Rules Actual Combat Analysis
Chapter 23: Association Rules Code Implementation
Chapter 24: Word Vector Word2vec Popular Interpretation
Chapter 25: Code Implementation Word2vec Word Vector Model
Chapter 26: Principle Analysis of Recommendation System
Chapter 27: Building a Music Recommendation System
Chapter 28: Linear Discrimination Interpretation of Principles of Analyzing Dimensionality Reduction Algorithm
Chapter 29: Interpretation of Principles of Principal Component Analysis Dimensionality Reduction Algorithm