Python machine learning Chinese version, python machine Chinese Version

Python machine learning Chinese version, python machine Chinese Version

• Introduction to Python Machine Learning
• Chapter 1 Let computers learn from data
  • Convert data into knowledge
  • Three types of machine learning algorithms

• Chapter 2 Training Machine Learning Classification Algorithms
  • A glimpse of early machine learning history through artificial neural networks
  • Use Python to implement the sensor Algorithm
  • Training the sensor model based on Iris Dataset
  • Adaptive Linear neuron and Convergence
  • Python implements Adaptive Linear neurons
  • Large-scale machine learning and random gradient descent
• Chapter 3 using Scikit-learn for Classifier
  • How to select an appropriate classifier Algorithm
  • Scikit-learn tour
  • Logistic regression modeling of class Probability
  • Use regularization to solve overfitting
  • SVM
  • Use relaxation variables to solve non-linear Differentiation
  • Use kernel SVM to solve non-linear Problems
  • Decision Tree Learning
  • Maximum information gain
  • Construct a decision tree
  • Random Forest
  • K Nearest Neighbor-a lazy Learning Algorithm
  • Summary
• Chapter 4 build a good training set-data preprocessing
  • Process Missing Values
  • Remove features or samples with missing values
  • Rewrite Missing Value
  • Understanding the estimator API in sklearn
  • Process classified data
  • Splits a dataset into a training set and a test set.
  • Unified feature value range
  • Select meaningful features
  • Evaluate feature importance using random Forest
  • Summary
• Chapter 5 compressing data by Dimensionality Reduction
  • Unsupervised Dimensionality Reduction Using PCA
  • Chat variance
  • Feature conversion
  • LDA for supervised Data Compression
  • Map original data to new feature space
  • Nonlinear ing Using Kernel PCA
  • Use Python to implement Kernel PCA
  • Map new data points
  • Kernel PCA in sklearn
  • Summary
• Chapter 6 model evaluation and Parameter Adjustment
  • Create a workflow through MPs queue
  • K-fold crossover verification to evaluate model performance
  • Debug algorithms using learning curves and verification Curves
  • Search for parameters through Grid
  • Selection algorithm through nested cross-validation
  • Different Performance Evaluation Indicators
• Chapter 7 integrated learning
  • Integrated Learning
  • Vote with different classification algorithms
• Chapter 8 deep learning-PyTorch