This course is part of the Machine Learning: Algorithms in the Real World Specialization

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406 ratings

Anna Koop

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Offered By

Machine Learning: Algorithms in the Real World SpecializationAlberta Machine Intelligence Institute

About this Course

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This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML.
To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).

This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

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Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 2 of 4 in the

Machine Learning: Algorithms in the Real World Specialization

Approx. 9 hours to complete

English

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 4 in the

Machine Learning: Algorithms in the Real World Specialization

Approx. 9 hours to complete

English

Instructor

Instructor rating

4.7/5 (74 Ratings)

Anna Koop

Senior Scientific Advisor

Alberta Machine Intelligence Institute, University of Alberta

32,919 Learners

4 Courses

Offered by

Alberta Machine Intelligence Institute

Syllabus - What you will learn from this course

Content Rating96%(1,004 ratings)

Week1

Week 1

4 hours to complete

Classification using Decision Trees and k-NN

Welcome to Supervised Learning, Tip to Tail! This week we'll go over the basics of supervised learning, particularly classification, as well as teach you about two classification algorithms: decision trees and k-NN. You'll get started programming on the platform through Jupyter notebooks and start to familiarize yourself with all the issues that arise when using machine learning for classification.

4 hours to complete

8 videos (Total 46 min), 4 readings, 2 quizzes

8 videos

Introduction to the Course1mWhat does a classifier actually do?5mClassification in scikit-learn3mWhat are decision trees?6mGeneralization and overfitting8mClassification using k-nearest neighbours8mDistance measures8mWeekly summary2m

4 readings

Math Review10mScikitlearn documentation for decision trees (Optional)10mScikitlearn documentation for random forests (Optional)10mScikitlearn documentation for k-nearest neighbours (Optional)10m

2 practice exercises

Supervised Learning BasicsUnderstanding Classification with Decision Trees and k-NN20m

Week2

Week 2

2 hours to complete

Functions for Fun and Profit

Welcome to the second week of the course! In this week you'll learn all about regression algorithms, the other side of supervised learning. We'll introduce you to the idea of finding lines, optimization criteria, and all the associated issues. Through regression we'll see the interactions between model complexity and accuracy, and you'll get a first taste of how regression and classification might relate.

2 hours to complete

9 videos (Total 62 min), 1 reading, 4 quizzes

9 videos

Line-fitting6mOptimal line-fitting8mLoss and Convexity7mGradient Descent9mNonlinear features and model complexity6mBias and variance tradeoff6mRegularizers5mLoss for Classification7mWeekly summary4m

1 reading

Scikitlearn documentation for linear regression (Optional)10m

4 practice exercises

Regression BasicsUnderstanding Model ComplexityFrom Regression to Classification2mThe Regression side of Supervised Learning20m

Week3

Week 3

3 hours to complete

Regression for Classification: Support Vector Machines

This week we'll be diving straight in to using regression for classification. We'll describe all the fundamental pieces that make up the support vector machine algorithms, so that you can understand how many seemingly unrelated machine learning algorithms tie together. We'll introduce you to logistic regression, neural networks, and support vector machines, and show you how to implement two of those.

3 hours to complete

6 videos (Total 34 min), 1 reading, 2 quizzes

Week4

Week 4

1 hour to complete

Contrasting Models

Now at the tail end of the course, we're going to go over how to know how well your model is actually performing and what you can do to get even better performance from it. We'll review assessment questions particular to regression and classification, and introduce some other tools that really help you analyze your model performance. The topics covered this week aim to give you confidence in your models, so you're ready to unlock the power of machine learning for your business goals.

1 hour to complete

8 videos (Total 46 min), 1 reading, 1 quiz

Reviews

4.7

66 reviews

5 stars
76.16%
4 stars
18.42%
3 stars
3.19%
2 stars
0.98%
1 star
1.22%

TOP REVIEWS FROM MACHINE LEARNING ALGORITHMS: SUPERVISED LEARNING TIP TO TAIL

by KGMay 9, 2020

The explanation of the topics are easy to understand due to the dynamics of theory, practical exercises and quizzes.

by RMDec 8, 2020

I found the course to be enough detailed to get clarity on the basic concepts of Supervised learning algorithms. I hope to apply the learning from the course in work!

by EGJan 8, 2020

The whole specialization is extremely useful for people starting in ML. Highly recommended!

by SKApr 11, 2020

Excellent course. In which I had in-depth knowledge of all algorithms and the way she explained attracts to listen except for her spontaneity and speed in progressing.

View all reviews

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Machine Learning Algorithms: Supervised Learning Tip to Tail

About this Course