About this Course
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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 12 hours to complete

Suggested: 11 hours/week...

English

Subtitles: English

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 12 hours to complete

Suggested: 11 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
4 hours to complete

Classification using Decision Trees and k-NN

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
2 hours to complete

Functions for Fun and Profit

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
3 hours to complete

Regression for Classification: Support Vector Machines

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
1 hour to complete

Contrasting Models

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
4.8
4 ReviewsChevron Right

Top reviews from Machine Learning Algorithms: Supervised Learning Tip to Tail

By MJOct 30th 2019

Great course! I received so much useful information from AMII.

Instructor

Avatar

Anna Koop

Senior Scientific Advisor
Alberta Machine Intelligence Institute, University of Alberta

About Alberta Machine Intelligence Institute

The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning....

About the Machine Learning: Algorithms in the Real World Specialization

This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning....
Machine Learning: Algorithms in the Real 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 enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. 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.