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.
Machine Learning Introduction for Everyone
Machine Learning Introduction for Everyone
Instructors: Aije Egwaikhide
22,013 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.5
(244 reviews)
Beginner level
No prior experience required
Flexible schedule
Approx. 7 hours
Learn at your own pace
96%
Most learners liked this course
What you'll learn
Compare and contrast artificial intelligence, machine learning, and deep learning
Explain the machine learning models development lifecycle
Differentiate between supervised and unsupervised machine learning
Evaluate classification models using metrics such as accuracy, confusion matrices, precision, and recall
Skills you'll gain
Details to know
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Assessments
2 assignments
Taught in English
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There are 3 modules in this course
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
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 videos4 readings1 assignment3 plugins
What's included
1 reading1 peer review1 app item2 plugins
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4.5
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Showing 3 of 244
HO
5
Reviewed on Jan 14, 2023
Thanks to the Authors, I learnt a lot from this course
BS
5
Reviewed on Jun 12, 2022
Very informative and for someone who just knows the word "Machine Learning", this was a good learning curve in getting to know about it
AM
5
Reviewed on Jun 22, 2023
An essential introduction to the world of Machine Learning with a very insightful Honors project at the end!
New to Machine Learning? Start here.
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