About this Course

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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

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Duke University

Syllabus - What you will learn from this course

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Week
1

Week 1

7 hours to complete

Simple Introduction to Machine Learning

7 hours to complete
23 videos (Total 163 min), 1 reading, 10 quizzes
23 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 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
2

Week 2

4 hours to complete

Basics of Model Learning

4 hours to complete
6 videos (Total 44 min)
6 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 PyTorch1m
3 practice exercises
Lesson One30m
Lesson 230m
Week 2 Comprehensive
Week
3

Week 3

4 hours to complete

Image Analysis with Convolutional Neural Networks

4 hours to complete
8 videos (Total 45 min)
8 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 PyTorch45s
4 practice exercises
Lesson One10m
Lesson 230m
Lesson 330m
Week 3 Comprehensive
Week
4

Week 4

5 hours to complete

Recurrent Neural Networks for Natural Language Processing

5 hours to complete
13 videos (Total 136 min)
13 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 PyTorch48s
4 practice exercises
Lesson 12m
Lesson 22m
Lesson 32m
Week 4 Comprehensive30m

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