4.5
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102 ratings

Aije Egwaikhide +1 more instructor

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Machine Learning Introduction for Everyone

IBM Skills Network

About this Course

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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. 

In addition to receiving a certificate from Coursera, you'll also earn an IBM Badge to help you share your accomplishments with your network and potential employer. 

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Beginner Level

Approx. 7 hours to complete

English

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What you will 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 will gain

Deep Learning
Artificial Intelligence (AI)
Machine Learning
Reinforcement Learning
Machine Learning Model Lifecycle

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Beginner Level

Approx. 7 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.75/5 (33 Ratings)

Aije Egwaikhide

Senior Data Scientist

IBM

425,341 Learners

6 Courses

Yasmine Hemmati

7,685 Learners

1 Course

Offered by

IBM Skills Network

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

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Syllabus - What you will learn from this course

Week1

Week 1

2 hours to complete

Machine Learning for Everyone

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.

2 hours to complete

6 videos (Total 35 min), 2 readings, 1 quiz

Week2

Week 2

2 hours to complete

Machine Learning Topics

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.

2 hours to complete

6 videos (Total 37 min), 4 readings, 1 quiz

Week3

Week 3

3 hours to complete

(Optional Honors) Final Project

3 hours to complete

1 reading

Reviews

4.5

30 reviews

5 stars
62.72%
4 stars
27.27%
3 stars
7.27%
2 stars
2.72%

TOP REVIEWS FROM MACHINE LEARNING INTRODUCTION FOR EVERYONE

by SGOct 9, 2022

Its very useful in understanding the basics of Machine Learning - Introduction.

by SROct 1, 2022

Very intuitive and easy to understand the basics of Machine Learning concepts. Thank you!

by HOJan 14, 2023

Thanks to the Authors, I learnt a lot from this course

by NAJun 2, 2022

my tool doesn't work!

I can't use it to upgrade my account and do my project!

says tha it's invalid

help

View all reviews

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
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.

More questions? Visit the Learner Help Center.

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Machine Learning Introduction for Everyone

Machine Learning Introduction for Everyone

About this Course

What you will learn

Skills you will gain