Introduce you to the fundamentals of Machine Learning. Serve as a launch pad for your career in Machine Learning and Data science.

This course is meant for beginners with a none to a small amount of Machine Learning experience.

You can use Jupyter notebook setup for coding. Anaconda distribution provides us with Jupyter notebook. Download link : https://www.anaconda.com/distribution/. We will use Scikit-Learn library of Python to build our models.

Linear Regression Implement Linear Regression algorithm to predict score of a student based on the number of hours he studies.

Naive Bayes Implement Naive Bayes algorithm to solve Classification problems.

Understanding Gradient Descent Optimization Learn how to use Gradient Descent Optimization to improve our Machine Learning models.

Building a Digit Recognizer using SVM Learn how to use Support Vector Machine (SVM) classifier for building a digit recognition system.

Introduction to Unsupervised Learning using K-means Learn how to use K-Means Clustering algorithm for Machine Learning problems.

Principal Component Analysis (PCA) Learn how to perform PCA for achieving dimensionality reduction.

Face Recognition using PCA Learn how to implement a Face Recognition System in Python using PCA.

Introduction to Reinforcement Learning An introduction on how to implement Reinforcement Learning algorithms and solve the Multi Arm Bandit problem using it.

Reinforcement Learning with OpenAI Gym Learn how to use OpenAI Gym in order to solve Reinforcement Learning problems.

Build an Intelligent Agent with Q-Learning Learn how to use Q-Learning in order to build an intelligent agent.

Movie Recommendation Engine. Build a movie recommendation system using Scikit Learn.

Cartpole Balancing with Q-learning. Build a system to balance a cartpole using Q-Learning.

Object Recognition with Neural Networks. Build a system to recognize objects using Neural Networks.

Mouse Cat Maze with Reinforcement Learning. Use Reinforcement Learning to solve Mouse Cat Maze.