CODE HEROKU

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.

In this course, you will learn about the fundamentals of machine learning. We will see examples of supervised, unsupervised and reinforcement learning. We will use a few machine learning API's to build a movie recommendation engine.

Course Objectives

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

Requirements

A computer and broadband internet connection
No software required, we'll install everything as we go.

Who is the target audience?

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

Course Syllabus

Module 0: Getting Started

Python and Libraries Setup

Module 1: Introduction to Python and Statistics

Introduction to Python Programming
Intermediate Python Concepts
Python Packages For Data Science Numpy & Matplotlib
Stats for Machine Learning
Exploratory Data Analysis - 1
Exploratory Data Analysis - 2
Inferential Statistics

Module 2: Data Extraction and Cleaning

Introduction to SQL
SQL For Data Science - Part 2
Web Scraping with Python
Web Scraping with Python - Part 2
Working with APIs
Text Data Cleaning
Module 2 Revision and Test

Module 3: Supervised Machine Learning

Introduction to Machine Learning
Naive Bayes Classifier
Content Based Recommendation Systems
Recommendation Systems - Collaborative Filtering
Gradient Descent For Machine Learning
Logistic Regression

Module 4: Intermediate ML Concepts

SVM - Introduction
Digit Recognition with SVM
Ensemble Methods, Bagging & Boosting

Neural Networks

Introduction to Neural Networks
Neural Networks - Part 2
Introduction to TensorFlow
Convolution Neural Networks (CNN)
Image Classification with CNN

Unsupervised Learning

Introduction to Unsupervised Learning using K-means
Principal Component Analysis (PCA)
Face Recognition using PCA

Reinforcement Learning

Inroduction to Reinforcement Learning
Reinforcement Learning with OpenAI Gym
Building a Crawling Robot with Q-Learning

Capstone Project

Part 1 - Research

Mihir Thakkar

About the Instructor

Mihir has earned his Masters degree in Computer Engineering from University of Maryland, College Park. Prior to starting Code Heroku, he has worked in the US as a software engineer for a healthcare IT company and taught summer courses at Columbia University, New York.

COURSE PROJECTS

You will be building these projects in your course

Ham or Spam Email Classification

Student Exam Score Prediction

Building a Movie Recommendation Engine

Cartpole Balancing with Q-learning

Object Recognition with Neural Networks

Mouse Cat Maze with Reinforcement Learning

Our Reviews

Attended the Machine Learning class yesterday and I must say that it is on of the best platforms which really thinks about its students and is dedicated to provide a better learning environment for the advancement of Indian education system. The class was interactive and I enjoyed taking it ! I would love to join future sessions on machine learning.

- Suman Adhikari

I've been to a lot of online sessions but this one stands out from the rest. The online class I've attended is Supervised Machine Learning, It is super easy to understand and follow up with the video

- Akhil Leventis

Session on web2py was good. More hands-on session was useful for understanding. Waiting for still more sessions like this one. Thank u!

- Ritu Koneri

I had my first session with Code Heroku and I found it to be quite insightful. I am a beginner at coding, so i was looking for some ways to learn new skills and i found this Building apps with Python workshop ad. The session was delivered in a manner such that a beginner could comprehend easily and could get hands-on learning experience. Thanks for the great session! Looking forward to other sessions as well.

- Preeti Manchanda

Machine Learning Career Track