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

Access provided by Toyota North America

27,492 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

747 reviews

Intermediate level

Some related experience required

Flexible schedule

7 hours to complete

Learn at your own pace

98%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

747 reviews

Intermediate level

Some related experience required

Flexible schedule

7 hours to complete

Learn at your own pace

98%

Most learners liked this course

Skills you'll gain

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Assessments

5 assignments

Taught in English

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This course is part of the Machine Learning: Algorithms in the Real World 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 videos Total 44 minutes

Introduction to the Applied Machine Learning Specialization 3 minutes
Instructor Introduction 1 minute
Introduction to Course 1 2 minutes
What is Artificial Intelligence and Machine Learning? 5 minutes
What about Data Science? 4 minutes
The Machine Learning Process 5 minutes
The Three Kinds of Machine Learning 3 minutes
Classification: What is it and how does it work? 4 minutes
Regression: Fitting lines and predicting numbers 4 minutes
Unsupervised Learning 4 minutes
Reinforcement Learning 6 minutes
Weekly Summary 2 minutes

6 readings Total 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 assignments Total 30 minutes

Concepts and Definitions 20 minutes
Identifying Machine Learning Techniques 10 minutes

3 discussion prompts Total 30 minutes

Artificial Intelligence Viewpoints 10 minutes
Meet and Greet! 10 minutes
Misunderstandings surrounding AI and ML 10 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 videos Total 34 minutes

Generalization and how machines actually learn 6 minutes
Features and transformations of raw data 6 minutes
Farmer Betty and Her Precision Agriculture Plans 4 minutes
What to consider when using your QuAM 3 minutes
Broad Examples Narrowed Down 5 minutes
Identify Business Evaluation 4 minutes
Everything is a Proxy 4 minutes
Weekly Summary 2 minutes

4 readings Total 40 minutes

A Brief Introduction into Precision Agriculture 10 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 Review 0 minutes

2 discussion prompts Total 20 minutes

Explainability and Accuracy for a QuAM 10 minutes
All About Proxies 10 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 videos Total 34 minutes

Sources of Training Data 3 minutes
How Much Data Do I Need? 4 minutes
Ethical Issues 4 minutes
Bias in Data Sources 3 minutes
Noise and Sources of Randomness 5 minutes
Image Classification Example 3 minutes
Data Cleaning: Everybody's favourite task 5 minutes
Why you need to set up a Data Pipeline 5 minutes
Weekly Summary 2 minutes

2 readings Total 20 minutes

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

1 assignment

Understanding Data for ML 0 minutes

2 discussion prompts Total 20 minutes

Sources of Data 10 minutes
Bias and Noise 10 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 videos Total 35 minutes

MLPL Overview 5 minutes
MLPL as experienced by Farmer Betty 3 minutes
Exploring the process of problem definition 7 minutes
Assessing your QuAM for use in your Business 6 minutes
Technically Assessing the Strength of your QuAM 6 minutes
Different Kinds of Wrong 4 minutes
Weekly Summary 3 minutes

2 readings Total 20 minutes

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

1 assignment

Understanding Machine Learning Projects 0 minutes

2 discussion prompts Total 20 minutes

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

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Instructor

Instructor ratings

(191 ratings)

Anna Koop

Alberta Machine Intelligence Institute

5 Courses 40,733 learners

Offered by

Alberta Machine Intelligence Institute

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Reviewed on Sep 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.

Reviewed on Oct 8, 2020

I have been teaching Machine Learning for some time now. This course is really good and comprehensively presents the working of machine learning process in a nutshell. Very good introductory course.

Reviewed on Jun 21, 2020

An excellent introduction to the fascinating world of machine learning and its endless applications. Loved the emphasis on the evaluation of the business prospect of ML as well.

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