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# Learn the Basics of Machine Learning

### Overview

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
• 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: Breast 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 