OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.

Introduction to Applied Machine Learning

Anna Koop

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.

Read more

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.

Enroll now

What's inside

Syllabus

Introduction to Machine Learning Applications
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.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Explores machine learning applications, which is standard in many industries
Teaches data acquisition and preparation, which are foundational skills for machine learning projects
Presents a case study, which helps learners apply the Machine Learning Process Lifecycle (MLPL)
Introduces learners to machine learning problem definition, which is a key skill for successful machine learning projects
Provides practical examples for translating business needs into machine learning projects, which is valuable for learners in any industry
Taught by Anna Koop, who are recognized for their expertise in machine learning

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Conceptual introduction to applied ml for business

According to students, this course provides a clear introduction to the world of applied machine learning, particularly focusing on how it can be used to solve business problems. Many learners found it excellent for understanding the conceptual framework and the ML project lifecycle, making it suitable for those with little to no prior technical ML experience or those in non-technical roles wanting to understand applications. Reviewers appreciated the focus on translating business needs into ML questions and the insights into data considerations. However, a common point raised is that the course is high-level and lacks deep technical detail or coding exercises, which is a warning for those seeking hands-on implementation skills. While providing a solid foundation, it may require supplemental study for practical application.
Mixed feedback on assignment quality.
"Some assignments were useful for reinforcing the concepts taught."
"I felt the assignments could be more challenging or better aligned with real-world application."
"The quizzes and assignments were helpful for checking understanding, but didn't always build practical skills."
Strong coverage of data and ML lifecycle.
"The sections on data acquisition, sources, and the Machine Learning Process Lifecycle (MLPL) were particularly informative."
"Understanding the ML project lifecycle from problem definition to deployment felt very practical."
"Good insights into preparing data for effective machine learning applications and potential pitfalls."
Provides a clear, accessible introduction.
"A really solid introduction to machine learning concepts and its place in the real world."
"Very clear explanations of potentially complex ideas. Excellent starting point for a beginner."
"I found the lectures easy to follow and the core ideas were well-explained."
"Gave me a great birds-eye view of the ML landscape and common misconceptions."
Helps frame ML in a business context.
"Really helps bridge the gap between business problems and ML solutions. Excellent for understanding *how* ML fits into a strategy."
"The focus on translating business needs into a machine learning problem was very valuable for my work."
"I gained a much clearer understanding of how to define and approach a real-world ML application."
"Practical insights on applying ML to data analysis and automation in different domains."
More theory and concepts, less coding.
"It's a great introduction to the concepts and process, but don't expect hands-on coding or deep dives into algorithms."
"The course is definitely more strategic and conceptual than technical. Good for understanding 'why' and 'what', not 'how' to code it."
"I wished there were more hands-on coding examples or labs to solidify the technical aspects discussed."
"This course is perfect for managers or analysts, less so for practitioners aiming for deep implementation knowledge."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Applied Machine Learning with these activities:
Organize and review your notes, assignments, quizzes, and exams
Organizing and reviewing your notes, assignments, quizzes, and exams will help you retain the information you have learned and improve your overall understanding of the course material.
Show steps
  • Gather your notes, assignments, quizzes, and exams
  • Organize your materials by topic
  • Review your materials regularly
  • Make notes and annotations
  • Create a study guide
Find a mentor who can provide guidance on your machine learning career
Finding a mentor who can provide guidance on your machine learning career will help you accelerate your progress and achieve your goals.
Show steps
  • Identify your goals and objectives
  • Research potential mentors
  • Reach out to potential mentors
  • Establish a mentoring relationship
  • Meet with your mentor regularly
Review Introduction to Machine Learning 4th Edition
Reviewing this book will help you refresh your understanding of the fundamentals of machine learning, which will be essential for success in this course.
View Machine Learning, revised and updated edition on Amazon
Show steps
  • Read the preface and introduction
  • Review the chapters on supervised learning
  • Review the chapters on unsupervised learning
  • Review the chapters on ensemble methods
  • Review the chapters on deep learning
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice data preprocessing and feature engineering
Practicing data preprocessing and feature engineering will help you develop the skills necessary to prepare data for machine learning models.
Browse courses on Data Preprocessing
Show steps
  • Find a dataset to work with
  • Clean the data
  • Preprocess the data
  • Engineer features
  • Train a machine learning model
Volunteer at a local AI organization
Volunteering at a local AI organization will provide you with hands-on experience and help you network with other professionals in the field.
Show steps
  • Find a local AI organization
  • Contact the organization and inquire about volunteer opportunities
  • Attend volunteer orientation
  • Complete volunteer training
  • Participate in volunteer activities
Create a presentation on a machine learning topic
Creating a presentation on a machine learning topic will help you deepen your understanding of the topic and improve your communication skills.
Show steps
  • Choose a topic
  • Research the topic
  • Create an outline
  • Write the presentation
  • Practice the presentation
Mentor a junior machine learning engineer
Mentoring a junior machine learning engineer will help you solidify your own understanding of the field and contribute to the development of the next generation of engineers.
Show steps
  • Find a junior machine learning engineer to mentor
  • Establish a mentoring relationship
  • Meet with your mentee regularly
  • Provide guidance and support
  • Help your mentee set and achieve goals
Contribute to an open-source machine learning project
Contributing to an open-source machine learning project will help you learn about the latest developments in the field and contribute to the community.
Show steps
  • Find an open-source machine learning project to contribute to
  • Read the project documentation
  • Identify an area where you can contribute
  • Make a contribution
  • Submit a pull request

Career center

Learners who complete Introduction to Applied Machine Learning will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models for a variety of applications. They typically have a strong understanding of machine learning algorithms, as well as the software engineering skills necessary to implement and deploy these models. Taking Introduction to Applied Machine Learning can help Machine Learning Engineers to gain a deeper understanding of machine learning theory and how to apply it to real-world problems. This can help them to become more effective in their roles and to develop more innovative and effective machine learning solutions.
See salaries and explore the career path for Machine Learning Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and deploy artificial intelligence systems. They typically have a strong understanding of machine learning, computer science, and mathematics, allowing them to develop and implement intelligent systems that can solve complex problems. Taking Introduction to Applied Machine Learning can help Artificial Intelligence Engineers to gain a deeper understanding of machine learning algorithms and how to use them to develop intelligent systems. This can help them to become more effective in their roles and to develop more innovative and effective artificial intelligence solutions.
See salaries and explore the career path for Artificial Intelligence Engineer
Data Scientist
Data Scientists use data to help businesses make better decisions. They typically have a strong understanding of machine learning, statistics, and computer science, allowing them to collect, clean, analyze, and visualize data to identify trends and patterns. Taking Introduction to Applied Machine Learning can help Data Scientists to gain a deeper understanding of machine learning algorithms and how to use them to analyze data. This can help them to become more effective in their roles and to provide more valuable insights to their businesses.
See salaries and explore the career path for Data Scientist
Operations Research Analyst
Operations Research Analysts develop and apply analytical techniques to help businesses make better decisions. They typically have a strong understanding of mathematics, statistics, and computer science, allowing them to analyze data and identify solutions to complex problems. Taking Introduction to Applied Machine Learning can help Operations Research Analysts to gain a deeper understanding of machine learning algorithms and how to use them to solve complex problems. This can help them to become more effective in their roles and to provide more valuable insights to their businesses.
See salaries and explore the career path for Operations Research Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. They typically have a strong understanding of statistics and machine learning, allowing them to identify trends and patterns that can help businesses make better decisions. Taking Introduction to Applied Machine Learning can help Data Analysts to gain a deeper understanding of machine learning algorithms and how to use them to analyze data. This can help them to become more effective in their roles and to provide more valuable insights to their businesses.
See salaries and explore the career path for Data Analyst
Business Intelligence Analyst
Business Intelligence Analysts analyze data to help businesses make better decisions. They typically have a strong understanding of business intelligence tools and techniques, as well as the skills necessary to collect, clean, and analyze data. Taking Introduction to Applied Machine Learning can help Business Intelligence Analysts to gain a deeper understanding of machine learning algorithms and how to use them to analyze data. This can help them to become more effective in their roles and to provide more valuable insights to their businesses.
See salaries and explore the career path for Business Intelligence Analyst
Computer Scientist
Computer Scientists research and develop new computing technologies. They typically have a strong understanding of computer science, as well as the skills necessary to design and implement new computing solutions. Taking Introduction to Applied Machine Learning can help Computer Scientists to gain a deeper understanding of machine learning algorithms and how to use them to develop new computing technologies. This can help them to become more effective in their roles and to contribute to the development of new and innovative computing solutions.
See salaries and explore the career path for Computer Scientist
Statistician
Statisticians work with data to help businesses and organizations make better decisions. They typically have a strong understanding of probability and statistics, allowing them to analyze data and identify trends and patterns. Taking Introduction to Applied Machine Learning can help Statisticians to gain a deeper understanding of machine learning algorithms and how to use them to analyze data. This can help them to become more effective in their roles and to provide more valuable insights to their organizations.
See salaries and explore the career path for Statistician
Business Analyst
Business Analysts look at an organization's business processes and help to improve them. They typically have a strong understanding of data and technology, allowing them to make better decisions that can lead to improved efficiency and productivity within the business. Taking Introduction to Applied Machine Learning can help Business Analysts to gain a deeper understanding of machine learning techniques and how to apply them to business problems. This can help them to make more informed decisions about how to use data and technology to improve business processes.
See salaries and explore the career path for Business Analyst
Software Engineer
Software Engineers design, develop, and deploy software applications. They typically have a strong understanding of computer science, as well as the skills necessary to implement and deploy software solutions. Taking Introduction to Applied Machine Learning can help Software Engineers to gain a deeper understanding of machine learning algorithms and how to use them to develop innovative software solutions. This can help them to become more effective in their roles and to develop more valuable products for their businesses.
See salaries and explore the career path for Software Engineer
Product Manager
Product Managers are responsible for the development and launch of new products. They typically have a strong understanding of product management methodologies, as well as the skills necessary to market and sell new products. Taking Introduction to Applied Machine Learning can help Product Managers to gain a deeper understanding of machine learning algorithms and how to use them to develop new products. This can help them to become more effective in their roles and to develop more innovative and successful products.
See salaries and explore the career path for Product Manager
Financial Analyst
Financial Analysts analyze financial data to help businesses make better decisions. They typically have a strong understanding of financial analysis tools and techniques, as well as the skills necessary to collect, clean, and analyze financial data. Taking Introduction to Applied Machine Learning can help Financial Analysts to gain a deeper understanding of machine learning algorithms and how to use them to analyze financial data. This can help them to become more effective in their roles and to provide more valuable insights to their businesses.
See salaries and explore the career path for Financial Analyst
Sales Manager
Sales Managers are responsible for the development and execution of sales strategies. They typically have a strong understanding of sales principles and techniques, as well as the skills necessary to develop and execute effective sales strategies. Taking Introduction to Applied Machine Learning can help Sales Managers to gain a deeper understanding of machine learning algorithms and how to use them to target and engage customers. This can help them to become more effective in their roles and to develop more successful sales strategies.
See salaries and explore the career path for Sales Manager
Management Consultant
Management Consultants help businesses improve their performance. They typically have a strong understanding of business principles and techniques, as well as the skills necessary to analyze businesses and develop and implement improvement plans. Taking Introduction to Applied Machine Learning can help Management Consultants to gain a deeper understanding of machine learning algorithms and how to use them to analyze businesses and develop improvement plans. This can help them to become more effective in their roles and to help their clients achieve their business goals.
See salaries and explore the career path for Management Consultant
Marketing Manager
Marketing Managers are responsible for the development and execution of marketing campaigns. They typically have a strong understanding of marketing principles and techniques, as well as the skills necessary to develop and execute effective marketing campaigns. Taking Introduction to Applied Machine Learning can help Marketing Managers to gain a deeper understanding of machine learning algorithms and how to use them to target and engage customers. This can help them to become more effective in their roles and to develop more successful marketing campaigns.
See salaries and explore the career path for Marketing Manager

Featured in The Course Notes

This course is mentioned in our blog, The Course Notes. Read one article that features Introduction to Applied Machine Learning:
Learn AI: Machine Learning and Deep Learning Online Courses
18 minute read

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to Applied Machine Learning.
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a practical guide to machine learning using Python. It valuable resource for anyone looking to apply machine learning techniques to real-world problems.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Machine Learning, revised and updated edition
Save
Provides a comprehensive overview of machine learning concepts and algorithms. It valuable resource for anyone looking to gain a deeper understanding of the field.
Machine Learning, revised and updated edition (The...
Paperback
$$
Machine Learning, revised and updated edition (The...
Kindle Edition
$$
Cover image
Probabilistic Machine Learning
Save
Provides a probabilistic perspective on machine learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Probabilistic Machine Learning: An Introduction...
Hardcover
$$$$
Machine Learning: A Probabilistic Perspective...
Hardcover
$$$
Probabilistic Machine Learning: An Introduction...
Kindle Edition
$$$
Probabilistic Machine Learning: Advanced Topics...
Kindle Edition
$$$
Machine Learning: A Probabilistic Perspective...
Kindle Edition
$$$
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Deep Learning
Save
Provides a comprehensive overview of pattern recognition and machine learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Deep Learning: Foundations and Concepts
Hardcover
$$$
Cover image
Artificial Intelligence in the 21st Century
Save
Provides an algorithmic perspective on machine learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Artificial Intelligence in the 21st Century
Hardcover
$$$
Cover image
Machine Learning in Action
Save
Provides a practical guide to machine learning. It valuable resource for anyone looking to apply machine learning techniques to real-world problems.
Machine Learning in Action
Paperback
$$
(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)
Kindle Edition
$
Cover image
Machine Learning
Save
Provides a comprehensive overview of machine learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Machine Learning: The Art and Science of Algorithms...
Kindle Edition
$$
Machine Learning: ECML 2001: 12th European...
Paperback
$$$
Machine Learning: The Art and Science of Algorithms...
Paperback
$$
Machine Learning and Knowledge Discovery in...
Paperback
$$$
Machine Learning and Knowledge Discovery in...
Paperback
$$$
Cover image
Introduction to Machine Learning, fourth edition
Save
Provides a case study approach to machine learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Introduction to Machine Learning, fourth edition
Paperback
Check
Introduction to Machine Learning, fourth edition
Kindle Edition
Check
Cover image
Machine Learning with Python Cookbook
Save
Provides a cookbook of machine learning recipes in Python. It valuable resource for anyone looking to apply machine learning techniques to real-world problems.
Machine Learning with Python Cookbook: Practical...
Paperback
$$
Machine Learning with Python Cookbook
Kindle Edition
$$
Cover image
Causal Factor Investing
Save
Provides a comprehensive overview of machine learning for finance. It valuable resource for anyone looking to apply machine learning techniques to financial data.
Causal Factor Investing (Elements in Quantitative...
Paperback
$$
Causal Factor Investing: Can Factor Investing...
Kindle Edition
$$
Cover image
Speech and Audio Processing
Save
Provides a comprehensive overview of machine learning for signal processing. It valuable resource for anyone looking to apply machine learning techniques to signal processing data.
Speech and Audio Processing: A MATLAB®-based...
Hardcover
$$$$
Speech and Audio Processing: A MATLAB®-based...
Kindle Edition
$$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Similar courses are unavailable at this time. Please try again later.
Level
Intermediate
Via
Coursera
Institution
Alberta Machine Intelligence Institute
Instructor
Anna Koop
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی
This course belongs to a collection called Machine Learning: Algorithms in the Real World. It's comprised of four courses:
  1. Data for Machine Learning
  2. Machine Learning Algorithms: Supervised Learning Tip to Tail
  3. Introduction to Applied Machine Learning (viewing)
  4. Optimizing Machine Learning Performance
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Explores machine learning applications, which is standard in many industries
Teaches data acquisition and preparation, which are foundational skills for machine learning projects
Presents a case study, which helps learners apply the Machine Learning Process Lifecycle (MLPL)
Introduces learners to machine learning problem definition, which is a key skill for successful machine learning projects
Provides practical examples for translating business needs into machine learning projects, which is valuable for learners in any industry
Taught by Anna Koop, who are recognized for their expertise in machine learning
Featured in The Course Notes
Read one article that features this course, Introduction to Applied Machine Learning from our blog, The Course Notes:
Learn AI: Machine Learning and Deep Learning Online Courses
18 minute read
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Introduction to Applied Machine Learning.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser