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Machine Learning

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PART-1: MACHINE LEARNING (ML)

1. The Machine Learning landscape

What is Machine Learning?

Why use Machine Learning?

Types of Machine Learning systems

Supervised Learning
Unsupervised Learning
Reinforcement Learning

2. Important Element of Machine Learning

Multiclass Classification

One-vs-All

Learnability

Overfitting and Underfitting
Error Measures
PCA learning
Statistical learning approaches

3. Feature Selection and Feature Engineering

Multiclass Classification

Scikit-learn
Creating training and test sets
Managing missing features
Feature selection
Principal Component analysis
- Scikit-learn
- Sparse PCA
- Kernal PCA

4. Linear Regression

Linear Models
A bidimensional example
Linear regression with scikit-learn and higher dimensionality
- Ridge Regression
- Lasso Regression
- Elasticnet Regression
Robust regression with random sample consensus
Polynomial regression
Isotonic regression

5. Logistic Regression

Linear Classification
Logistic regression
Implementation and optimizations
Stochastic gradient descent algorithms
Finding the optimal hyperparameters through grid search
Classification metrics
ROC curve
AUC curve

6. Naive Bayes

Bayes’Therorem
Naive Bayes classifiers
Naive Bayes in scikit-learn
- Bernoulli naive Bayes
- Multinomial naive Bayes
- Gausssiannaive Bayes

7. Support Vector Machines

Linear support vector machines
Scikit-learn implementation
- Linear classification
- Kernel-based classification
  - Radial Basis Function
  - Polynomial Kernel
  - Sigmoid Kernel
  - Custom Kernels
- Non-linear examples
Controlled support vector machines
Support vector Regression

8. Decision Trees and Ensemble Learning

Binary Decision Trees
Binary decisions
Impurity measures
- Gini impurity index
- Cross-entropy impurity index
- Misclassification impurity index
Feature importance
Decision tree classification with scikit-learn
Ensemble Learning
- Random Forests and Features importance in Random Forest
- AdaBoost
- Gradient tree boosting
- Voting classifier

9. Clustering Fundamentals

Clustering Basics
K-MEANS
- Finding the optimal number of clusters
  - Optimizing the inertia
  - Cluster instability
- DBSCAN
- Spectral Clustering
Evaluation methods based on the ground truth
- Homogeneity
- Completeness
- Adjusted rand index

10. Hierarchical Clustering

Hierarchical strategies
Agglomerative clustering
- Dendrograms
- Agglomerative clustering in scikit-learn
- Connectivity constraints

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