About this Course
4.9
11 ratings
4 reviews
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 12 hours to complete

Suggested: 4 weeks of study, 2-5 hours/week...
Available languages

English

Subtitles: English...
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 12 hours to complete

Suggested: 4 weeks of study, 2-5 hours/week...
Available languages

English

Subtitles: English...

Syllabus - What you will learn from this course

Week
1
Hours to complete
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. ...
Reading
23 videos (Total 164 min), 1 reading, 14 quizzes
Video23 videos
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 TensorFlow3m
Reading1 reading
Math for Data Science10m
Quiz10 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 Practicem
Week 1 Comprehensivem
Week
2
Hours to complete
3 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....
Reading
6 videos (Total 44 min), 5 quizzes
Video6 videos
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 TensorFlowm
Quiz3 practice exercises
Lesson One10m
Lesson 210m
Week 2 Comprehensivem
Week
3
Hours to complete
3 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....
Reading
8 videos (Total 45 min), 6 quizzes
Video8 videos
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 TensorFlowm
Quiz4 practice exercises
Lesson One10m
Lesson 210m
Lesson 36m
Week 3 Comprehensivem
Week
4
Hours to complete
11 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....
Reading
13 videos (Total 136 min), 5 quizzes
Video13 videos
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 TensorFlowm
Quiz4 practice exercises
Lesson 12m
Lesson 22m
Lesson 32m
Week 4 Comprehensive30m

Instructor

Avatar

Lawrence Carin

James L. Meriam Professor of Electrical and Computer Engineering
Electrical and Computer Engineering

About 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....

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.