C1 - Supervised Machine Learning : Regression and Classification

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

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

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 has helped millions of other learners, like you, take a look at the exciting world of machine learning!

Learning Objectives

• Define machine learning
• Define supervised learning
• Define unsupervised learning
• Write and run Python code in Jupyter Notebooks
• Define a regression model
• Implement and visualize a cost function
• Implement gradient descent
• Optimize a regression model using gradient descent

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.

Learning Objectives

• Use vectorization to implement multiple linear regression
• Use feature scaling, feature engineering, and polynomial regression to improve model training
• Implement 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!

Learning Objectives

• Use logistic regression for binary classification
• Implement logistic regression for binary classification
• Address overfitting using regularization, to improve model performance