priyam-choksi/Introduction-to-Machine-Learning

This repo will house all our course material and code snippets from the Introduction to Machine Learning Class

Introduction to Machine Learning

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

Course Objectives

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

Who is the target audience?

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

Tutorials

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

Introduction and getting started

• 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

Supervised Machine Learning

• 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.

Unsupervised Learning

• 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.

Reinforcement Learning

• 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.

Course Projects

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.

Want to learn more?

Come learn with us in the Introduction to Machine Learning course at Code Heroku !

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