This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more. Our labs give you hands-on experience with these machine learning and data science concepts. You will develop concrete machine learning skills as well as create a final project demonstrating your proficiency.
After completing this program, you’ll be able to realize the potential of machine learning algorithms and artificial intelligence in different business scenarios. You’ll be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You’ll also learn how to evaluate your machine learning models and to incorporate best practices.
This Course Is Part of Multiple Programs
You can also leverage the learning from the program to complete the remaining two courses of the six-course IBM Machine Learning Professional Certificate and power a new career in the field of machine learning.
Welcome to the world of machine learning.
Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Machine learning is an important component in the growing field of data science. Using statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision-making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them.
In this module, you will explore some of the fundamental concepts behind machine learning. You will learn to differentiate between AI, machine, and deep learning. Further, you will also explore the importance and requirements of each process in the lifecycle of a machine learning product.
What's included
6 videos2 readings1 assignment2 plugins
Show info about module content
6 videos•Total 35 minutes
Introduction to Machine Learning for Everyone•7 minutes
Machine Learning History•7 minutes
Interesting Applications of Machine Learning•4 minutes
Machine Learning Model Lifecycle•2 minutes
A Day in the life of a Machine Learning Engineer•8 minutes
Tools for Machine Learning•7 minutes
2 readings•Total 9 minutes
Course Overview•5 minutes
Module Summary•4 minutes
1 assignment•Total 30 minutes
Graded Quiz•30 minutes
2 plugins•Total 40 minutes
Machine Learning History•15 minutes
Hands-on Lab: Watson Text to Speech Voices•25 minutes
Machine Learning Topics
Module 2•3 hours to complete
Module details
Machine learning is a hot topic, and everyone is trying to understand what it is about. With the amount of information that is out there about machine learning, you can get quickly overwhelmed.
In this module, you will explore the most important topics in machine learning that you need to know. You will dive into supervised and unsupervised learning, classification, deep and reinforcement learning, as well as regression. Further, you will learn how to evaluate a machine learning model.
What's included
8 videos1 reading1 assignment1 app item2 plugins
Show info about module content
8 videos•Total 48 minutes
Supervised vs Unsupervised Learning•7 minutes
Classification•6 minutes
Regression•6 minutes
Evaluating Machine Learning Models•8 minutes
Introduction to Deep Learning•5 minutes
Reinforcement Learning•6 minutes
Generative AI Overview and Use Cases•5 minutes
Generative AI Applications•6 minutes
1 reading•Total 2 minutes
Module Summary•2 minutes
1 assignment•Total 30 minutes
Graded Quiz•30 minutes
1 app item•Total 60 minutes
Hands-on Demo: Exploring Machine Learning Classification with the Iris Dataset•60 minutes
In this assignment, we will investigate insurance charges using a Machine Learning Regression Application, exploring how different features influence these charges. Using an interactive regression app, you will analyze how a machine learning model predicts insurance costs based on different user inputs.
What's included
4 readings1 assignment1 plugin
Show info about module content
4 readings•Total 10 minutes
Final Project Overview•3 minutes
Course Summary•3 minutes
Congrats and Next Steps•2 minutes
Course Team and Acknowledgements•2 minutes
1 assignment•Total 30 minutes
Final Quiz•30 minutes
1 plugin•Total 45 minutes
Hands-on Lab: Investigating Insurance Charges with an Machine Learning Regression Application•45 minutes
Instructors
Instructor ratings
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
At IBM, we know how rapidly tech evolves and recognize the crucial need for businesses and professionals to build job-ready, hands-on skills quickly. As a market-leading tech innovator, we’re committed to helping you thrive in this dynamic landscape. Through IBM Skills Network, our expertly designed training programs in AI, software development, cybersecurity, data science, business management, and more, provide the essential skills you need to secure your first job, advance your career, or drive business success. Whether you’re upskilling yourself or your team, our courses, Specializations, and Professional Certificates build the technical expertise that ensures you, and your organization, excel in a competitive world.
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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.