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

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Anna Koop

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

About this Course

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This course 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 course will introduce you to problem definition and data preparation in a machine learning project.
By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications. You will learn to take a business need and turn it into a machine learning application. You will prepare data for effective machine learning applications.

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

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Course 1 of 4 in the

Machine Learning: Algorithms in the Real World Specialization

Intermediate Level

Approx. 6 hours to complete

English

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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.

Course 1 of 4 in the

Machine Learning: Algorithms in the Real World Specialization

Intermediate Level

Approx. 6 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.71/5 (184 Ratings)

Anna Koop

Senior Scientific Advisor

Alberta Machine Intelligence Institute, University of Alberta

32,980 Learners

5 Courses

Offered by

Alberta Machine Intelligence Institute

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Syllabus - What you will learn from this course

Content Rating99%(1,778 ratings)

Week1

Week 1

3 hours to complete

Introduction to Machine Learning Applications

3 hours to complete

12 videos (Total 44 min), 6 readings, 2 quizzes

12 videos

Introduction to the Applied Machine Learning Specialization3mInstructor Introduction1mIntroduction to Course 12mWhat is Artificial Intelligence and Machine Learning?5mWhat about Data Science?3mThe Machine Learning Process4mThe Three Kinds of Machine Learning3mClassification: What is it and how does it work?3mRegression: Fitting lines and predicting numbers3mUnsupervised Learning4mReinforcement Learning6mWeekly Summary1m

6 readings

What about Deep Learning? (supplemental)10mFooling Neural Networks (supplemental)10mHow to Curate A Ground Truth For Your Business Dataset (Required)10mLearning From Multiple Annotators: A Survey (supplemental)10mInferring the Ground Truth Through Crowdsourcing (supplemental)10mSemi Supervised Learning (required)10m

2 practice exercises

Concepts and Definitions20mIdentifying Machine Learning Techniques10m

Week2

Week 2

2 hours to complete

Machine Learning in the Real World

2 hours to complete

8 videos (Total 34 min), 4 readings, 1 quiz

8 videos

Generalization and how machines actually learn6mFeatures and transformations of raw data6mFarmer Betty and Her Precision Agriculture Plans3mWhat to consider when using your QuAM2mBroad Examples Narrowed Down4mIdentify Business Evaluation4mEverything is a Proxy4mWeekly Summary2m

4 readings

A Brief Introduction into Precision Agriculture10mFarmer Betty Tried Unsupervised Learning (required)10mData is Central to Your ML Problem (required)10mMartin Zinkevich's Rules for ML (supplemental)10m

1 practice exercise

Machine Learning in the Real World Review

Week3

Week 3

1 hour to complete

Learning Data

1 hour to complete

9 videos (Total 34 min), 2 readings, 1 quiz

9 videos

Sources of Training Data3mHow Much Data Do I Need?4mEthical Issues4mBias in Data Sources3mNoise and Sources of Randomness5mImage Classification Example3mData Cleaning: Everybody's favourite task4mWhy you need to set up a Data Pipeline4mWeekly Summary1m

2 readings

Data Protection Laws (required)10mGovernment readings on data privacy (supplemental)10m

1 practice exercise

Understanding Data for ML

Week4

Week 4

1 hour to complete

Machine Learning Projects

1 hour to complete

7 videos (Total 35 min), 2 readings, 1 quiz

7 videos

MLPL Overview5mMLPL as experienced by Farmer Betty3mExploring the process of problem definition7mAssessing your QuAM for use in your Business6mTechnically Assessing the Strength of your QuAM6mDifferent Kinds of Wrong4mWeekly Summary2m

2 readings

Machine Learning Process Lifecycle Explained10mDeep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)10m

1 practice exercise

Understanding Machine Learning Projects

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TOP REVIEWS FROM INTRODUCTION TO APPLIED MACHINE LEARNING

by VESep 21, 2019

I have taken a great deal of knowledge in this course and have applied it to what I have seen in the ML and AI world. This was a great introduction!

by AJJun 18, 2020

Very nice , informative introduction.It's very broad and generalized introduction of the Machine Learning.

I am definitely going for the next curse.

by AASep 11, 2019

very comprehensive course on applied machine learning. the most interesting information in this course is business needs for ML and what it's requirement to have a good QuAM.

by ASJul 11, 2021

This is course is truly amazing for the people who want to know about the things that need to be considered when making a QUAM or an ML system. Looking forward to knowing more about ML!

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.

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Introduction to Applied Machine Learning

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