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Machine Learning


What Is Machine learning
Why Use Machine Learning
Types of Machine Learning Systems
Main Challenges of Machine Learning 
Testing and Validating.
Prepare the Data for Machine Learning Algorithms
SCIKIT-LEARN
Select and Train a Model
Fine-Tune Your Model
Linear Regression
Gradient Descent
Polynomial Regression 
Logistic Regression  
MNIST
Training a Binary Classifier
Performance Measures
Multiclass Classification
Error Analysis
Multilabel Classification
Multioutput Classification
Linear SVM Classification
Nonlinear SVM Classification
SVM Regression
Training and Visualizing a Decision Tree, Making Predictions 
Estimating Class Probabilities 
The CART Training Algorithm 
Computational Complexity 
Gini Impurity or Entropy
Regularization Hyperparameters
Voting Classifiers
Bagging and Pasting
Random Patches and Random Subspaces
Random Forests 
Boosting 
Stacking.
The Curse of Dimensionality
Main Approaches for Dimensionality Reduction
PCA
Kernel PCA
LLE
Other Dimensionality Reduction Techniques
Clustering
K-Means
Clustering for image segmentation
Clustering for Pre-processing
Clustering for Semi-Supervised Learning
DBSCAN
Gaussian Mixtures. 
 
Sci-Kit-Learn

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