Machine Learning is the study of computer algorithms to predictions from data without the computer being explicitly programmed to do so. In this course we take a look at the concepts and implementation of various machine learning algorithms in Python.

### Requirements

- Knowledge of python libraies such as Pandas, Matplotlib and Seaborn
- Fundamental knowledge of mathematics and statistics
- Laptop
- Analytic and Logical reasoning.

### Who is this course for:

Those who have undergone my Python for Data Analysis track and want to go deeper into machine learning.

### Course Outcome:

- Trainees at the end of the course should be able to use machine learning and deep learning algorithms to make decisions from both structured and unstructured data.

### Course Outline:

#### Linear Regression

#### Logistic Regression

#### K-Nearest Neighbours

#### Decision Trees and Random Forests

#### Support vector Machines

#### K-Means Clustering

#### Principal Component Analysis

#### Recommender Systems

#### Natural Language Processing

#### Big Data and Spark

#### Deep Learning

#### Introduction to SciPy

#### Capstone Project

- Case Study: Titanic Dataset