Learn the Basics of Machine Learning

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Overview

The “Learn the Basics of Machine Learning” course is designed for beginners eager to understand the fundamental concepts and techniques of machine learning. Ideal for students, professionals, and enthusiasts, this course introduces the core principles behind how machines learn from data to make predictions or decisions without being explicitly programmed. Participants will explore essential topics such as supervised and unsupervised learning, classification, regression, clustering, and evaluation metrics. The course explains key algorithms like linear regression, decision trees, and k-means clustering,...

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