About this Course

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Approx. 9 hours to complete
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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.
Approx. 9 hours to complete
English

Offered by

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Alberta Machine Intelligence Institute

Syllabus - What you will learn from this course

Week
1

Week 1

4 hours to complete

Classification using Decision Trees and k-NN

4 hours to complete
8 videos (Total 46 min), 4 readings, 2 quizzes
8 videos
What does a classifier actually do?5m
Classification in scikit-learn3m
What are decision trees?6m
Generalization and overfitting8m
Classification using k-nearest neighbours8m
Distance measures8m
Weekly summary2m
4 readings
Math Review10m
Scikitlearn documentation for decision trees (Optional)10m
Scikitlearn documentation for random forests (Optional)10m
Scikitlearn documentation for k-nearest neighbours (Optional)10m
2 practice exercises
Supervised Learning Basics
Understanding Classification with Decision Trees and k-NN20m
Week
2

Week 2

2 hours to complete

Functions for Fun and Profit

2 hours to complete
9 videos (Total 62 min), 1 reading, 4 quizzes
9 videos
Optimal line-fitting8m
Loss and Convexity7m
Gradient Descent9m
Nonlinear features and model complexity6m
Bias and variance tradeoff6m
Regularizers5m
Loss for Classification7m
Weekly summary4m
1 reading
Scikitlearn documentation for linear regression (Optional)10m
4 practice exercises
Regression Basics
Understanding Model Complexity
From Regression to Classification2m
The Regression side of Supervised Learning20m
Week
3

Week 3

3 hours to complete

Regression for Classification: Support Vector Machines

3 hours to complete
6 videos (Total 34 min), 1 reading, 2 quizzes
6 videos
Neural Networks9m
Hinge Loss6m
Basics of Support Vector Machines6m
Kernels6m
Weekly Summary1m
1 reading
Scikitlearn documentation for SVMs (Optional)10m
2 practice exercises
Understanding Support Vector Machines
Regression-based Classification10m
Week
4

Week 4

1 hour to complete

Contrasting Models

1 hour to complete
8 videos (Total 46 min), 1 reading, 1 quiz
8 videos
Classification assessment6m
Learning Curves6m
Testing your models7m
Cross validation5m
Parameter tuning and grid search5m
Model Parameters6m
Weekly Summary1m
1 reading
Some resources on model assessment (Optional)10m
1 practice exercise
Contrasting Models

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About the Machine Learning: Algorithms in the Real World Specialization

Machine Learning: Algorithms in the Real World

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