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).

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Intermediate Level

Approx. 25 hours to complete

English

Subtitles: English

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 25 hours to complete

English

Subtitles: English

Instructors

Instructor rating

4.6/5 (163 Ratings)

Offered by

Duke University

Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.

Syllabus - What you will learn from this course

Content Rating96%(3,918 ratings)

Week

Week 1

7 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.

7 hours to complete

23 videos (Total 163 min), 1 reading, 10 quizzes

23 videos

Why Machine Learning Is Exciting5m

What Is Machine Learning?5m

Logistic Regression9m

Interpretation of Logistic Regression9m

Motivation for Multilayer Perceptron4m

Multilayer Perceptron Concepts5m

Multilayer Perceptron Math Model6m

Deep Learning6m

Example: Document Analysis3m

Interpretation of Multilayer Perceptron9m

Transfer Learning5m

Model Selection7m

Early History of Neural Networks14m

Hierarchical Structure of Images6m

Convolution Filters9m

Convolutional Neural Network3m

CNN Math Model6m

How the Model Learns8m

Advantages of Hierarchical Features4m

CNN on Real Images9m

Applications in Use and Practice10m

Deep Learning and Transfer Learning7m

Introduction to PyTorch3m

1 reading

Math for Data Science10m

10 practice exercises

Intro to Machine Learning8m

Logistic Regression8m

Multilayer Perceptron8m

Deep Learning8m

Model Selection8m

History of Neural Networks8m

CNN Concepts10m

CNN Math Model4m

Applications In Use and Practice

Week 1 Comprehensive

Week

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)

6 videos

How Do We Define Learning?9m

How Do We Evaluate Our Networks?12m

How Do We Learn Our Network?7m

How Do We Handle Big Data?10m

Early Stopping2m

Model Learning with PyTorch1m

3 practice exercises

Lesson One30m

Lesson 230m

Week 2 Comprehensive

Week

Week 3

4 hours to complete

Image Analysis with Convolutional Neural Networks

This week will cover model training, as well as transfer learning and fine-tuning. In addition to learning the fundamentals of a CNN and how it is applied, careful discussion is provided on the intuition of the CNN, with the goal of providing a conceptual understanding.

4 hours to complete

8 videos (Total 45 min)

8 videos

Motivation: Diabetic Retinopathy9m

Breakdown of the Convolution (1D and 2D)8m

Core Components of the Convolutional Layer7m

Activation Functions4m

Pooling and Fully Connected Layers4m

Training the Network6m

Transfer Learning and Fine-Tuning4m

CNN with PyTorch45s

4 practice exercises

Lesson One10m

Lesson 230m

Lesson 330m

Week 3 Comprehensive

Week

Week 4

5 hours to complete

Recurrent Neural Networks for 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.

5 hours to complete

13 videos (Total 136 min)

13 videos

Introduction to the Concept of Word Vectors9m

Words to Vectors7m

Example of Word Embeddings11m

Neural Model of Text14m

The Softmax Function7m

Methods for Learning Model Parameters9m

More Details on How to Learn Model Parameters6m

The Recurrent Neural Network11m

Long Short-Term Memory20m

Long Short-Term Memory Review11m

Use of LSTM for Text Synthesis9m

Simple and Effective Alternative Methods for Neural NLP15m

Natural Language Processing with PyTorch48s

4 practice exercises

Lesson 12m

Lesson 22m

Lesson 32m

Week 4 Comprehensive30m

Week

Week 5

2 hours to complete

The Transformer Network for Natural Language Processing

This week we'll cover an Introduction to the Transformer Network, a deep machine learning model designed to be more flexible and robust than Recurrent Neural Network (RNN). We'll start by reviewing several machine learning building blocks of a Transformer Network: the Inner products of word vectors, attention mechanisms, and sequence-to-sequence encoders and decoders. Then, we'll put all of these components together to explore the complete Transformer Network.

2 hours to complete

12 videos (Total 131 min)

12 videos

Word Vectors and Their Interpretation7m

Relationships Between Word Vectors5m

Inner Products Between Word Vectors8m

Intuition Into Meaning of Inner Products of Word Vectors10m

Introduction of Attention Mechanism 10m

Queries, Keys, and Values of Attention Network11m

Self-Attention and Positional Encodings21m

Attention-Based Sequence Encoder11m

Coupling the Sequence Encoder and Decoder 15m

Cross Attention in the Sequence-to-Sequence Model5m

Multi-Head Attention10m

The Complete Transformer Network13m

Week

Week 6

4 hours to complete

Introduction to Reinforcement Learning

This week will cover Reinforcement Learning, a fundamental concept in machine learning that is concerned with taking suitable actions to maximize rewards in a particular situation. After learning the initial steps of Reinforcement Learning, we'll move to Q Learning, as well as Deep Q Learning. We'll discuss the difference between the concepts of Exploration and Exploitation and why they are important.

4 hours to complete

10 videos (Total 105 min)

10 videos

Introduction to Reinforcement Learning9m

Reinforcement Learning Problem Setup8m

Example of Reinforcement Learning in Practice20m

Reinforcement Learning with PyTorch54s

Moving to a Non-Myopic Policy11m

Q Learning11m

Extensions of Q Learning11m

Limitations of Q Learning, and Introduction to Deep Q Learning13m

Deep Q Learning Based on Images8m

Connecting Deep Q Learning with Conventional Q Learning10m

3 practice exercises

Reinforcement Learning Quiz

Q Learning Quiz

Deep Q Learning Quiz

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Introduction to Machine Learning