Machine Learning

1. Deep Learning

1.1. Architectures

1.1.1. Convolutional Neural Networks

1.1.2. Recurrent Neural Networks

1.1.2.1. GRU

2. Types of Learning

2.1. Supervised Learning

2.1.1. Classification

2.1.1.1. based-on types

2.1.1.1.1. Binary Classification

2.1.1.1.2. Multi-Class Classification

2.1.1.2. based-on algorithms

2.1.1.2.1. Logistic Regression

2.1.1.2.2. k-Nearest Neighbours

2.1.2. Regression

2.1.2.1. Linear Regression

2.2. Semi-Supervised Learning

2.3. Unsupervised Learning

2.3.1. Clustering

2.3.1.1. K-Means

2.4. Reinforcement Learning

3. How to approach a Problem?

3.1. Define your Problem

3.1.1. Problem Statement

3.1.2. Problem Description

3.1.3. Data Set Description