Supervised Machine Learning: Regression and Classification

DeepLearning.AI and Stanford University via Coursera

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Overview

In the first course of the Machine Learning Specialization, you will: Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these...

Syllabus

Week 1: Introduction to Machine Learning

Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and hased millions of other learners, like you, take a look at the exciting world of machine learning!

Week 2: Regression with multiple input variables

This week, you'll extend linear regression to handle multiple input features. You'll also learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code.

Week 3: Classification

This week, you'll learn the other type of supervised learning, classification. You'll learn how to predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization at the end of this week!