Machine Learning Introduction for Everyone
Learn machine learning basics, from AI concepts to model evaluation. Hands-on labs with IBM tools for practical skills in ML applications.
Learn machine learning basics, from AI concepts to model evaluation. Hands-on labs with IBM tools for practical skills in ML applications.
This beginner-friendly course provides a comprehensive introduction to machine learning for everyone. Over three modules, learners explore fundamental concepts of AI, machine learning, and deep learning. The course covers the machine learning model lifecycle, supervised and unsupervised learning, classification, regression, and model evaluation techniques. With hands-on labs using IBM tools, participants gain practical experience in applying machine learning concepts. The curriculum includes an overview of generative AI and its applications. By the end of the course, learners will be able to understand the potential of machine learning in various business scenarios, differentiate between AI techniques, and evaluate machine learning models using metrics like accuracy and precision.
4.5
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What you'll learn
Understand the differences between artificial intelligence, machine learning, and deep learning
Explain the machine learning model development lifecycle
Differentiate between supervised and unsupervised machine learning
Understand classification and regression in machine learning
Learn to evaluate machine learning models using metrics like accuracy and confusion matrices
Gain hands-on experience with IBM Watson and AutoAI tools
Skills you'll gain
This course includes:
35 Minutes PreRecorded video
2 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 3 modules in this course
This course offers a comprehensive introduction to machine learning concepts and applications. Starting with the fundamentals of artificial intelligence and machine learning history, it progresses to cover the machine learning model lifecycle and essential tools. Learners explore key topics such as supervised vs. unsupervised learning, classification, regression, and model evaluation techniques. The course also introduces deep learning, reinforcement learning, and provides an overview of generative AI. Through hands-on labs and demos using IBM tools like Watson and AutoAI, participants gain practical experience in applying machine learning concepts. The curriculum balances theoretical knowledge with real-world applications, preparing learners to understand and leverage machine learning in various business scenarios.
Machine Learning for Everyone
Module 1 · 1 Hours to complete
Machine Learning Topics
Module 2 · 2 Hours to complete
Machine Learning Topics
Module 3 · 2 Hours to complete
Fee Structure
Payment options
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Instructors
Data Scientist Aije Egwaikhide: Empowering Women in STEM and Innovating AI Solutions at IBM
Aije Egwaikhide is a fantastic example of how dedication and passion can lead to a successful career in tech! With her background in Economics and Statistics, paired with advanced qualifications in Business and Management Analytics, she’s truly paving the way in the field of data science. Her work at IBM, particularly in creating innovative machine learning solutions for the Oil and Gas sector, is an inspiring achievement.
IBM Instructor Specializing in Machine Learning Fundamentals
Yasmine Hemmati is a professional at IBM, where she teaches the course Machine Learning Introduction for Everyone. This course is designed to provide learners with a foundational understanding of machine learning concepts and techniques. It covers essential topics such as the history of machine learning, the machine learning model lifecycle, and the differences between supervised and unsupervised learning. Participants will also learn how to evaluate classification models using metrics like accuracy and precision. The course aims to equip students with practical skills in data science and machine learning, making it accessible for individuals from various backgrounds interested in entering this rapidly evolving field.
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4.5 course rating
225 ratings
Frequently asked questions
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