About this Course

4,973 recent views
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 11 hours to complete
English
Subtitles: English

Skills you will gain

Regression AnalysisSupervised LearningLinear RegressionRidge RegressionMachine Learning (ML) Algorithms
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 11 hours to complete
English
Subtitles: English

Offered by

IBM logo

IBM

Syllabus - What you will learn from this course

Week
1

Week 1

2 hours to complete

Introduction to Supervised Machine Learning and Linear Regression

2 hours to complete
9 videos (Total 77 min), 3 readings, 3 quizzes
9 videos
Introduction to Supervised Machine Learning: What is Machine Learning?4m
Introduction to Supervised Machine Learning: Types of Machine Learning10m
Supervised Machine Learning for Interpretation and Prediction12m
Regression and Classification Examples7m
Introduction to Linear Regression12m
Linear Regression Demo - Part110m
Linear Regression Demo - Part211m
Linear Regression Demo - Part34m
3 readings
Course Prerequisites4m
Linear Regression Demo (Activity)10m
Summary/Review4m
3 practice exercises
Check for Understanding15m
Check for Understanding15m
End of Module Quiz20m
Week
2

Week 2

4 hours to complete

Data Splits and Cross Validation

4 hours to complete
12 videos (Total 116 min), 3 readings, 4 quizzes
12 videos
Training and Test Splits Lab - Part 17m
Training and Test Splits Lab - Part 216m
Training and Test Splits Lab - Part 310m
Training and Test Splits Lab - Part 45m
Cross Validation11m
Cross Validation Demo - Part 110m
Cross Validation Demo - Part 28m
Cross Validation Demo - Part 311m
Cross Validation Demo - Part 410m
Cross Validation Demo - Part 56m
Polynomial Regression7m
3 readings
Training and Test Splits Demo10m
Cross Validation Demo10m
Summary/Review4m
4 practice exercises
Check for Understanding15m
Check for Understanding15m
Check for Understanding30m
End of Module Quiz15m
Week
3

Week 3

5 hours to complete

Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net

5 hours to complete
11 videos (Total 126 min), 3 readings, 3 quizzes
11 videos
Regularization and Model Selection7m
Ridge Regression8m
LASSO Regression12m
Polynomial Features and Regularization Demo - Part 120m
Polynomial Features and Regularization Demo - Part 211m
Polynomial Features and Regularization Demo - Part 310m
Further details of regularization14m
Details of Regularization - Part 18m
Details of Regularization - Part 29m
Details of Regularization - Part 39m
3 readings
Polynomial Features and Regularization Demo10m
Details of Regularization Demo10m
Summary/Review10m
2 practice exercises
Check for Understanding15m
End of Module Quiz15m

About the IBM Introduction to Machine Learning Specialization

This specialization will help you realize the potential of machine learning in a business setting. There will be a focus on helping you gain the skills that will help you succeed in a career in machine learning and data science. You will be able to realize the potential of machine learning and artificial intelligence in different business scenarios. You will also be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You will also learn how to evaluate your machine learning models and to incorporate best practices....
IBM Introduction to Machine Learning

Frequently Asked Questions

  • 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.
  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.