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
