About this Course

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Approx. 9 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.
Approx. 9 hours to complete
English
Subtitles: English

Offered by

Alberta Machine Intelligence Institute logo

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

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

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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