What you'll learn

Course 1: Supervised Learning: Regression and Classification

Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn
Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression

Course 2: Supervised Learning: Advanced Learning Algorithms

Build and train a neural network with TensorFlow to perform multi-class classification
Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
Build and use decision trees and tree ensemble methods, including random forests and boosted trees

Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning

Use unsupervised learning techniques including clustering and anomaly detection
Build recommender systems with a collaborative filtering approach and a content-based deep learning method
Build a deep reinforcement learning model

Introduction

Machine Learning is the field of study that gives computers ability to learn without being explicitly programmed ~ Arthur Samuel (1959)

Supervised learning

Supervised learning is simply learnings of A to B or input to output mappings. Learn from data labeled with the “right answers”.

Supervised learning examples with input to output mappings:

Input	Output	Application
Email	Spam? (0/1)	Spam filtering
Audio	Text transcript	Speech recognition
English	Chinese	Machine translation
Ad, User info	Click? (0/1)	Online advertising
Image, Radar info	Position of other cars	Self-driving car
Image of phone	Defect? (0/1)	Visual inspection
Sequence of words	the next word	Chatbot

Regression

To predict a number from infinitely many possible numbers. It tries to fit a line, curve or any other function Eg., Housing price prediction to predict the price of the house based on area (sq. ft) etc