Introduction to Applied Machine Learning

Introduction to Applied Machine Learning

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

Instructor: Anna Koop

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5 assignments

Taught in English

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

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 4 modules in this course

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.

This week, you will learn about what machine learning (ML) actually is, contrast different problem scenarios, and explore some common misconceptions about ML. You will apply this knowledge by identifying different components essential to a machine learning business solution.

What's included

12 videos6 readings2 assignments3 discussion prompts

12 videosTotal 43 minutes

Introduction to the Applied Machine Learning Specialization3 minutesPreview module
Instructor Introduction1 minute
Introduction to Course 12 minutes
What is Artificial Intelligence and Machine Learning?5 minutes
What about Data Science?3 minutes
The Machine Learning Process4 minutes
The Three Kinds of Machine Learning3 minutes
Classification: What is it and how does it work?3 minutes
Regression: Fitting lines and predicting numbers3 minutes
Unsupervised Learning4 minutes
Reinforcement Learning6 minutes
Weekly Summary1 minute

6 readingsTotal 60 minutes

What about Deep Learning? (supplemental)10 minutes
Fooling Neural Networks (supplemental)10 minutes
How to Curate A Ground Truth For Your Business Dataset (Required)10 minutes
Learning From Multiple Annotators: A Survey (supplemental)10 minutes
Inferring the Ground Truth Through Crowdsourcing (supplemental)10 minutes
Semi Supervised Learning (required)10 minutes

2 assignmentsTotal 30 minutes

Concepts and Definitions20 minutes
Identifying Machine Learning Techniques10 minutes

3 discussion promptsTotal 30 minutes

Artificial Intelligence Viewpoints10 minutes
Meet and Greet!10 minutes
Misunderstandings surrounding AI and ML10 minutes

This week, you will learn how to translate a business need into a machine learning problem. We'll walk through some applied examples so you can get a feel for what makes a well-defined question for your QuAM. Narrowing down your question and making sure you have the data necessary to learn is critical to ML success!

What's included

8 videos4 readings1 assignment2 discussion prompts

8 videosTotal 34 minutes

Generalization and how machines actually learn6 minutesPreview module
Features and transformations of raw data6 minutes
Farmer Betty and Her Precision Agriculture Plans3 minutes
What to consider when using your QuAM2 minutes
Broad Examples Narrowed Down4 minutes
Identify Business Evaluation4 minutes
Everything is a Proxy4 minutes
Weekly Summary2 minutes

4 readingsTotal 40 minutes

A Brief Introduction into Precision Agriculture10 minutes
Farmer Betty Tried Unsupervised Learning (required)10 minutes
Data is Central to Your ML Problem (required)10 minutes
Martin Zinkevich's Rules for ML (supplemental)10 minutes

1 assignment

Machine Learning in the Real World Review0 minutes

2 discussion promptsTotal 20 minutes

Explainability and Accuracy for a QuAM10 minutes
All About Proxies10 minutes

This week is all about data. You will learn about data acquisition and understand the various sources of training data. We'll talk about how much data you need and what pitfalls might arise, including ethical issues.

What's included

9 videos2 readings1 assignment2 discussion prompts

9 videosTotal 34 minutes

Sources of Training Data3 minutesPreview module
How Much Data Do I Need?4 minutes
Ethical Issues4 minutes
Bias in Data Sources3 minutes
Noise and Sources of Randomness5 minutes
Image Classification Example3 minutes
Data Cleaning: Everybody's favourite task4 minutes
Why you need to set up a Data Pipeline4 minutes
Weekly Summary1 minute

2 readingsTotal 20 minutes

Data Protection Laws (required)10 minutes
Government readings on data privacy (supplemental)10 minutes

1 assignment

Understanding Data for ML0 minutes

2 discussion promptsTotal 20 minutes

Sources of Data10 minutes
Bias and Noise10 minutes

This week you will learn about the Machine Learning Process Lifecycle (MLPL). After understanding the definitions and components of the MLPL you will analyze the application of the MLPL on a case study.

What's included

7 videos2 readings1 assignment2 discussion prompts

7 videosTotal 34 minutes

MLPL Overview5 minutesPreview module
MLPL as experienced by Farmer Betty3 minutes
Exploring the process of problem definition7 minutes
Assessing your QuAM for use in your Business6 minutes
Technically Assessing the Strength of your QuAM6 minutes
Different Kinds of Wrong4 minutes
Weekly Summary2 minutes

2 readingsTotal 20 minutes

Machine Learning Process Lifecycle Explained10 minutes
Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)10 minutes

1 assignment

Understanding Machine Learning Projects0 minutes

2 discussion promptsTotal 20 minutes

What task can machine learning help you with?10 minutes
False Positives and False Negatives10 minutes

Instructor

Instructor ratings

4.7 (188 ratings)

Anna Koop

Alberta Machine Intelligence Institute

5 Courses37,581 learners

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Alberta Machine Intelligence Institute

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Reviewed on Nov 12, 2020

An excellent introduction to the mechanics of ML. Not so deep that coding is required, but simultaneously not so high-level as to be abstract. A very nice intro - thanks for this!

Reviewed on Sep 14, 2020

The lectures are very clear and easy to follow. More importantly, it gives me a big picture of how Machine Learning can be applied to the real-world business.

Reviewed on Jan 5, 2020

Course gives a broader understanding of any Machine Learning project, how to approach and what are the important things to keep in mind.

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