Learn the Basics of Machine Learning

Overview

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Learn the basics of Machine Learning in this introductory course.

### Why Learn Machine Learning?
Machine learning, the field of computer science that gives computer systems the ability to learn from data, is one of the hottest topics in computer science.

Machine learning is transforming the world: from spam filtering in social networks to computer vision for self-driving cars, the potential applications of machine learning are vast.

### Take-Away Skills:
This course covers the foundational machine learning algorithms that will help you advance in your career. Whether you’re trying to analyze a dataset using machine learning, or you’re a data analyst trying to upgrade your skills, this course is the best place to start.

### Note on Prerequisites:
You should be comfortable with Python, including functions, control flow, lists, and loops.

Syllabus

Introduction to Machine Learning: What is Machine Learning and how do we use it?

Lesson: Why Use Machine Learning?
Article: Supervised vs. Unsupervised
Article: Scikit-Learn Cheatsheet

Linear Regression: Given a set of points, find a line that fits the data best! Even this simple form of regression allows us to predict future points.

Lesson: Linear Regression
Quiz: Linear Regression
Project: Honey Production

Multiple Linear Regression: **Multiple Linear Regression** uses two or more independent variables to predict the value of the dependent variable.

Article: StreetEasy Dataset
Lesson: Multiple Linear Regression
Quiz: Multiple Linear Regression

Yelp Regression Project: Practice your regression skills on a real-world dataset provided by Yelp!

Informational: Yelp Rating Predictor Cumulative Project
Article: Yelp Dataset Terms of Use

Classification Vs Regression: Learn about the two types of Supervised Learning algorithms, for predicting different kinds of output.

Article: Regression vs. Classification

Classification: K-Nearest Neighbors: K-Nearest Neighbors is a supervised machine learning algorithm for classification. You will implement and test this algorithm on several datasets.

Lesson: Distance Formula
Article: Normalization
Article: Training Set vs Validation Set vs Test Set
Lesson: K-Nearest Neighbors
Quiz: K-Nearest Neighbors
Project: Cancer Classifier

Logistic Regression: Find the probability of data samples belonging to a specific class with one of the most popular classification algorithms.

Lesson: Logistic Regression
Quiz: Logistic Regression
Project: Predict Titanic Survival

Decision Trees: In this course, you will learn how to build and use decision trees and random forests - two powerful supervised machine learning models.

Lesson: Decision Trees
Quiz: Decision Trees
Project: Find the Flag
Lesson: Random Forests
Quiz: Random Forests
Project: Predicting Income with Random Forests

Clustering: K-Means: Clustering is the most well-known unsupervised learning technique. It finds structure in unlabeled data by identifying similar groups.

Lesson: K-Means Clustering
Quiz: K-Means Clustering
Lesson: K-Means++ Clustering
Project: Handwriting Recognition using K-Means

Perceptron: Learn about the most basic type of neural net, the single neuron perceptron! You will use it to divide linearly-separable data.

Lesson: Perceptron
Quiz: Perceptron Quiz
Project: Perceptron Logic Gates

Artificial Intelligence Decision Making: Minimax: In this course, you'll learn how to create a game playing AI that can play Tic Tac Toe and Connect Four.

Lesson: Minimax
Lesson: Advanced Minimax
Quiz: Minimax
Project: Build Your Own Connect Four AI