This repository contains material related to Introduction to Machine Learning Course by Code Heroku.

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

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

The tutorials lead you through implementing various algorithms in machine learning. All of the code is written in Python.

• Introduction to machine learning

  A brief introduction on the fundamentals of machine learning.

• Setup Python

  A guide for installing Python on your system.

• Scikit-Learn and other libraries installation

  A guide for installing Scikit-Learn and other libraries required for this course.

• Python Numpy Tutorial

  A brief walkthrough on Python, Numpy, Scipy and Matplotlib

• Linear Regression using Scikit-Learn

  Implement linear regression to predict score of a student based on the number of hours he studies.

• Naive Bayes using Scikit Learn

  Implement Naive Bayes algorithm to solve classification problems using Scikit Learn.

• Building a Movie Recommendation Engine

  Build a Movie Recommendation Engine in Python using Scikit Learn.

• Understanding Gradient Descent Optimization

  Learn how to use Gradient Descent optimization for solving Machine Learning problems.

• 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.

• Inroduction 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.

The following projects are included as a part of this course.

• Ham or Spam Email Classification

  Build a spam classifier system.

• Student Exam Score Prediction

  Predict the score obtained by a student in the examination based on how many hours he has studied.

• 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.

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