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Introduction to Machine Learning

Duke University

About this Course

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This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).

Learner Career Outcomes

13%

started a new career after completing these courses

13%

got a tangible career benefit from this course

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Intermediate Level

Approx. 21 hours to complete

English

Skills you will gain

Natural Language Processing
Convolutional Neural Network
Machine Learning
Python Programming
pytorch

Learner Career Outcomes

13%

started a new career after completing these courses

13%

got a tangible career benefit from this course

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Intermediate Level

Approx. 21 hours to complete

English

Instructors

Instructor rating

4.65/5 (1,264 Ratings)

Offered by

Duke University

Syllabus - What you will learn from this course

Content Rating97%(19,268 ratings)

Week1

Week 1

8 hours to complete

Simple Introduction to Machine Learning

The focus of this module is to introduce the concepts of machine learning with as little mathematics as possible. We will introduce basic concepts in machine learning, including logistic regression, a simple but widely employed machine learning (ML) method. Also covered is multilayered perceptron (MLP), a fundamental neural network. The concept of deep learning is discussed, and also related to simpler models.

8 hours to complete

23 videos (Total 164 min), 1 reading, 10 quizzes

23 videos

Why Machine Learning Is Exciting5mWhat Is Machine Learning?5mLogistic Regression9mInterpretation of Logistic Regression9mMotivation for Multilayer Perceptron4mMultilayer Perceptron Concepts5mMultilayer Perceptron Math Model6mDeep Learning6mExample: Document Analysis3mInterpretation of Multilayer Perceptron9mTransfer Learning5mModel Selection7mEarly History of Neural Networks14mHierarchical Structure of Images6mConvolution Filters9mConvolutional Neural Network3mCNN Math Model6mHow the Model Learns8mAdvantages of Hierarchical Features4mCNN on Real Images9mApplications in Use and Practice10mDeep Learning and Transfer Learning7mIntroduction to TensorFlow3m

1 reading

Math for Data Science10m

10 practice exercises

Intro to Machine Learning8mLogistic Regression8mMultilayer Perceptron8mDeep Learning8mModel Selection8mHistory of Neural Networks8mCNN Concepts10mCNN Math Model4mApplications In Use and PracticeWeek 1 Comprehensive

Week2

Week 2

4 hours to complete

Basics of Model Learning

In this module we will be discussing the mathematical basis of learning deep networks. We’ll first work through how we define the issue of learning deep networks as a minimization problem of a mathematical function. After defining our mathematical goal, we will introduce validation methods to estimate real-world performance of the learned deep networks. We will then discuss how gradient descent, a classical technique in optimization, can be used to achieve this mathematical goal. Finally, we will discuss both why and how stochastic gradient descent is used in practice to learn deep networks.

4 hours to complete

6 videos (Total 44 min)

Week3

Week 3

4 hours to complete

Image Analysis with Convolutional Neural Networks

4 hours to complete

8 videos (Total 45 min)

Week4

Week 4

6 hours to complete

Introduction to Natural Language Processing

This week will cover the application of neural networks to natural language processing (NLP), from simple neural models to the more complex. The fundamental concept of word embeddings is discussed, as well as how such methods are employed within model learning and usage for several NLP applications. A wide range of neural NLP models are also discussed, including recurrent neural networks, and specifically long short-term memory (LSTM) models.

6 hours to complete

22 videos (Total 241 min), 1 reading, 4 quizzes

22 videos

Introduction to the Concept of Word Vectors9mWords to Vectors7mExample of Word Embeddings11mNeural Model of Text14mThe Softmax Function7mMethods for Learning Model Parameters9mMore Details on How to Learn Model Parameters6mThe Recurrent Neural Network11mLong Short-Term Memory20mLong Short-Term Memory Review11mUse of LSTM for Text Synthesis9mSimple and Effective Alternative Methods for Neural NLP15mNatural Language Processing with TensorFlow41sIntroduction to Reinforcement Learning9mReinforcement Learning Problem Setup8mExample of Reinforcement Learning in Practice20mMoving to a Non-Myopic Policy11mQ Learning11mExtensions of Q Learning11mLimitations of Q Learning, and Introduction to Deep Q Learning13mDeep Q Learning Based on Images8mConnecting Deep Q Learning with Conventional Q Learning10m

1 reading

Bonus Lesson: Reinforcement Learning10m

4 practice exercises

Lesson 12mLesson 22mLesson 32mWeek 4 Comprehensive30m

Reviews

4.7

765 reviews

5 stars
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3 stars
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1 star
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TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING

by MAJun 27, 2021

This is the best course for the Machine Learning! I liked all the instructors, especially, I loved Lawrence Carin Sir's lectures simplified way of teaching! Thank you Team.

by YZMay 21, 2021

The tutors' instruction are very clear and make it easy to understand for people who has little background. In addition, the examples are useful and applicable in this lesson!

by NNNov 26, 2020

Thanks Coursera and Duke University for this course. It is very insightful to get understood the basics of ML and applied ML in numerous fields. It really made me to move ahead with ML domain.

by GCJul 8, 2019

Very good introductory course, I highly recommend it to anyone looking to get a flavour of the methods behind the recent advances in AI without going into super-technical details.

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Introduction to Machine Learning

Introduction to Machine Learning

About this Course

Learner Career Outcomes

13%

13%

Skills you will gain

Learner Career Outcomes

13%

13%

Instructors

Lawrence Carin

David Carlson

Timothy Dunn

Kevin Liang

Offered by

Duke University

Syllabus - What you will learn from this course

Simple Introduction to Machine Learning

Basics of Model Learning

Image Analysis with Convolutional Neural Networks

Introduction to Natural Language Processing

Reviews

TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING

Frequently Asked Questions

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Introduction to Machine Learning

Introduction to Machine Learning

About this Course

Learner Career Outcomes

13%

13%

Skills you will gain

Learner Career Outcomes

13%

13%

Instructors

Lawrence Carin

David Carlson

Timothy Dunn

Kevin Liang

Offered by

Duke University

Syllabus - What you will learn from this course

Simple Introduction to Machine Learning

Basics of Model Learning

Image Analysis with Convolutional Neural Networks

Introduction to Natural Language Processing

Reviews

TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING

Frequently Asked Questions

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