MACHINE LEARNING BEGINNER TO ADVANCED LEVEL

Introduction to Machine learning

Supervised learning, Unsupervised learning

Reinforcement learning, Deep learning

Feature Selection: Filter, Wrapper , Embedded methods

Feature Normalization: min-max normalization, z-score normalization, constant factor

normalization

Introduction to Dimensionality Reduction: Principal Component Analysis(PCA)

Linear Discriminant Analysis(LDA)

Supervised Learning – I Introduction

Regression models: Simple LinearRegression, multiple linear Regression

Cost Function, Gradient Descent

Performance Metrics: MeanAbsolute Error(MAE)

Mean Squared Error(MSE) R-Squared error,Adjusted R Square.

Classification models: Decision Trees-ID3,CART

Naive Bayes,K-Nearest-Neighbours (KNN)

Multinomial Logistic Regression Support Vector Machines (SVM)

Nonlinearity and Kernel Methods

Supervised Learning – II (NeuralNetworks)

Introduction

Neural NetworkRepresentation – Problems – Perceptrons , Activation Functions

Artificial Neural Networks (ANN),Back Propagation Algorithm.

Convolutional NeuralNetworks - Convolution and Pooling layers

Recurrent NeuralNetworks (RNN).

Classification Metrics: Confusion matrix

Recall, Accuracy, F-Score, ROC curves

Model Validation in Classification : Cross Validation - Holdout Method, K-Fold

Stratified K-Fold,Leave-One-Out Cross Validation.

Bias-Variance tradeoff, Regularization

Overfitting, Underfitting

Ensemble Methods: Boosting, Bagging, Random Forest.

Unsupervised Learning: Clustering-K-means, K-Modes

K-Prototypes, Gaussian Mixture Models

Expectation-Maximization.

Reinforcement Learning: Exploration and exploitation trade-offs

Non-associative learning

Markov decision processes, Q-learning.