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Supervised Machine Learning: Regression and Classification
  • Level Foundation
  • Duration 33 hours
  • Course by DeepLearning.AI
  • Offered by

About

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 techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. 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.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Modules

Overview of Machine Learning

    • 2 Videos
    • 1 Readings
    • 1 ExternalTool
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1 External Tool

  • Intake Survey

2 Videos

  • Welcome to machine learning!
  • Applications of machine learning

1 Readings

  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!

Supervised vs. Unsupervised Machine Learning

    • 6 Videos
    • 1 Lab
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1 Labs

  • Python and Jupyter Notebooks

6 Videos

  • What is machine learning?
  • Supervised learning part 1
  • Supervised learning part 2
  • Unsupervised learning part 1
  • Unsupervised learning part 2
  • Jupyter Notebooks

Practice Quiz: Supervised vs unsupervised learning

    • 1 Assignment
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1 Assignment

  • Practice quiz: Supervised vs unsupervised learning

Regression Model

    • 6 Videos
    • 2 Lab
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2 Labs

  • Optional lab: Model representation
  • Optional lab: Cost function

6 Videos

  • Linear regression model part 1
  • Linear regression model part 2
  • Cost function formula
  • Cost function intuition
  • Visualizing the cost function
  • Visualization examples

Practice Quiz: Regression Model

    • 1 Assignment
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1 Assignment

  • Practice quiz: Regression

Train the model with gradient descent

    • 6 Videos
    • 1 Lab
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1 Labs

  • Optional lab: Gradient descent

6 Videos

  • Gradient descent
  • Implementing gradient descent
  • Gradient descent intuition
  • Learning rate
  • Gradient descent for linear regression
  • Running gradient descent

Practice quiz: Train the model with gradient descent

    • 1 Assignment
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1 Assignment

  • Practice quiz: Train the model with gradient descent

Multiple linear regression

    • 4 Videos
    • 2 Lab
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2 Labs

  • Optional lab: Python, NumPy and vectorization
  • Optional Lab: Multiple linear regression

4 Videos

  • Multiple features
  • Vectorization part 1
  • Vectorization part 2
  • Gradient descent for multiple linear regression

Practice quiz: Multiple linear regression

    • 1 Assignment
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1 Assignment

  • Practice quiz: Multiple linear regression

Gradient descent in practice

    • 6 Videos
    • 3 Lab
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3 Labs

  • Optional Lab: Feature scaling and learning rate
  • Optional lab: Feature engineering and Polynomial regression
  • Optional lab: Linear regression with scikit-learn

6 Videos

  • Feature scaling part 1
  • Feature scaling part 2
  • Checking gradient descent for convergence
  • Choosing the learning rate
  • Feature engineering
  • Polynomial regression

Practice quiz: Gradient descent in practice

    • 1 Assignment
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1 Assignment

  • Practice quiz: Gradient descent in practice

Week 2 practice lab: Linear regression

    • 1 Programming
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1 Programming

  • Week 2 practice lab: Linear regression

Classification with logistic regression

    • 3 Videos
    • 3 Lab
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3 Labs

  • Optional lab: Classification
  • Optional lab: Sigmoid function and logistic regression
  • Optional lab: Decision boundary

3 Videos

  • Motivations
  • Logistic regression
  • Decision boundary

Practice quiz: Classification with logistic regression

    • 1 Assignment
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1 Assignment

  • Practice quiz: Classification with logistic regression

Cost function for logistic regression

    • 2 Videos
    • 2 Lab
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2 Labs

  • Optional lab: Logistic loss
  • Optional lab: Cost function for logistic regression

2 Videos

  • Cost function for logistic regression
  • Simplified Cost Function for Logistic Regression

Practice quiz: Cost function for logistic regression

    • 1 Assignment
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1 Assignment

  • Practice quiz: Cost function for logistic regression

Gradient descent for logistic regression

    • 1 Videos
    • 2 Lab
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2 Labs

  • Optional lab: Gradient descent for logistic regression
  • Optional lab: Logistic regression with scikit-learn

1 Videos

  • Gradient Descent Implementation

Practice quiz: Gradient descent for logistic regression

    • 1 Assignment
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1 Assignment

  • Practice quiz: Gradient descent for logistic regression

The problem of overfitting

    • 5 Videos
    • 2 Lab
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2 Labs

  • Optional lab: Overfitting
  • Optional lab: Regularization

5 Videos

  • The problem of overfitting
  • Addressing overfitting
  • Cost function with regularization
  • Regularized linear regression
  • Regularized logistic regression

Practice quiz: The problem of overfitting

    • 1 Assignment
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1 Assignment

  • Practice quiz: The problem of overfitting

End of Access to Lab Notebooks

    • 1 Readings
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1 Readings

  • [IMPORTANT] Reminder about end of access to Lab Notebooks

Week 3 practice lab: logistic regression

    • 1 Programming
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1 Programming

  • Week 3 practice lab: logistic regression

Conversations with Andrew (Optional)

    • 1 Videos
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1 Videos

  • Andrew Ng and Fei-Fei Li on Human-Centered AI

Acknowledgments

    • 1 Readings
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1 Readings

  • Acknowledgments

Auto Summary

"Supervised Machine Learning: Regression and Classification" focuses on building and training machine learning models using Python, NumPy, and scikit-learn. This beginner-friendly course is part of a 3-course specialization created by DeepLearning.AI and Stanford Online, taught by AI expert Andrew Ng. It covers supervised learning techniques like linear and logistic regression and prepares you for real-world AI applications. With a duration of 1980 minutes, it offers subscription options for both Starter and Professional levels, making it ideal for those looking to break into AI or advance their machine learning careers.

Andrew Ng
Instructors

Andrew Ng

Aarti Bagul
Instructors

Aarti Bagul

Geoff Ladwig
Instructors

Geoff Ladwig