About this Course

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Flexible deadlines
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Intermediate Level
Approx. 12 hours to complete
English

Skills you will gain

Statistical AnalysisMachine LearningPython ProgrammingComputer ProgrammingLinear Algebra
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.
Intermediate Level
Approx. 12 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

2 hours to complete

What Does Good Data look like?

2 hours to complete
11 videos (Total 65 min), 2 readings, 3 quizzes
11 videos
Business Understanding and Problem Discovery9m
No Free Lunch Theorem5m
Exploring the process of problem definition7m
Data Acquisition and Understanding8m
Metadata Matters5m
Dealing with Multimodal Data2m
Features and transformations of raw data6m
Identifying Data from Problem5m
Case Study: Problem from Data6m
Weekly Summary What does good data look like?4m
2 readings
Machine Learning Process Lifecycle Review10m
Match Data to the needs of the learning Algorithm10m
3 practice exercises
Business Understanding and Problem Discovery (BUPD) Review10m
Data Acquisition and Understanding Review10m
Module 1 Quiz30m
Week
2

Week 2

2 hours to complete

Preparing your Data for Machine Learning Success

2 hours to complete
11 videos (Total 61 min)
11 videos
Converting to Useful Forms7m
Data Quality5m
How Much Data Do I Need?4m
Everything has to be Numbers6m
Types of Data5m
Aligning Similar Data4m
Imputing Missing Values7m
Data Transformations7m
Weekly Summary: Preparing your Data for Machine Learning Success1m
Data Cleaning: Everybody's favourite task4m
4 practice exercises
Data Warehousing Review10m
Everything has to be Numbers Review10m
Types of Data Review10m
Module 2 Quiz30m
Week
3

Week 3

6 hours to complete

Feature Engineering for MORE Fun & Profit

6 hours to complete
8 videos (Total 45 min), 2 readings, 4 quizzes
8 videos
Useful/Useless Features6m
How Many Features?5m
What is Unsupervised Learning6m
Feature Selection7m
Feature Extraction2m
Transfer Learning7m
Weekly Summary: Feature Engineering for MORE Fun & Profit1m
2 readings
Possibilities for Text Features10m
Word Embeddings10m
3 practice exercises
Understanding Features30m
Building Good Features30m
Understanding Transfer Learning30m
Week
4

Week 4

2 hours to complete

Bad Data

2 hours to complete
9 videos (Total 48 min)
9 videos
Generalization and how machines actually learn6m
Bias in Data Sources3m
Bias and variance tradeoff6m
Outliers5m
Skewed Distributions7m
Badness Multipliers4m
Live Data Danger6m
Weekly Summary: Bad Data1m
4 practice exercises
Mistakes Computers Make10m
Data: Skewed Distributions10m
Live Data Dangers10m
Module 4 Quiz30m

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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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