IBM
Machine Learning Introduction for Everyone
IBM

Machine Learning Introduction for Everyone

Aije Egwaikhide
Yasmine Hemmati

Instructors: Aije Egwaikhide

21,303 already enrolled

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
4.5

(235 reviews)

Beginner level
No prior experience required
Flexible schedule
Approx. 7 hours
Learn at your own pace
96%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.5

(235 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

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

Instructors

Instructor ratings
4.8 (67 ratings)
Aije Egwaikhide
IBM
6 Courses644,668 learners

Offered by

IBM

Recommended if you're interested in Machine Learning

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4.5

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