**Introduction to Machine Learning**

- What is Machine Learning?
- Applications of Machine Learning
- Why Machine Learning is the Future
- Installing R and R Studio (MAC & Windows)
- Installing Python and Anaconda (MAC & Windows)

**Data Pre-processing**

- Data Preprocessing
- Importing the Libraries
- Importing the Dataset
- For Python learners, summary of Object-oriented programming: classes & objects
- Missing Data
- Categorical Data
- Splitting the Dataset into the Training set and Test set
- Feature Scaling

**Regression**

- Simple Linear Regression
- Dataset + Business Problem Description
- Simple Linear Regression in Python
- Simple Linear Regression in R
- Multiple Linear Regression
- Multiple Linear Regression in Python
- Multiple Linear Regression in R
- Polynomial Regression
- Polynomial Regression in Python
- Polynomial Regression in R
- Support Vector Regression (SVR)
- SVR in Python
- SVR in R
- Decision Tree Regression in Python
- Decision Tree Regression in R
- Random Forest Regression in Python
- Random Forest Regression in R

**Classification**

- Logistic Regression in Python and R
- K-Nearest Neighbors (K-NN)
- Support Vector Machine (SVM)
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
- Confusion Matrix
- CAP Curve

**Clustering**

- K-Means Clustering in Python and R
- Hierarchical Clustering in Python and R

**Association Rule Learning**

- Association Rule Learning in Python and R
- Apriori

**Reinforcement Learning**

- Upper Confidence Bound (UCB)
- Thompson Sampling

**Natural Language Processing**

- Natural Language Processing in R
- Natural Language Processing in Python

**Deep Learning**

- Artificial Neural Networks in Python and R
- Convolution Neural Networks in Python and R