- Machine Learning Regression
- Machine Learning Classification
- Machine Learning
- Unsupervised Learning
- Exploratory Data Analysis
- Artificial Intelligence (AI)
- Feature Engineering
- Statistical Hypothesis Testing
- Regression Analysis
- Supervised Learning
- Linear Regression
- Ridge Regression
IBM Introduction to Machine Learning Specialization
Learn machine learning through real use cases. Build the skills for a career in one of the most relevant fields of modern AI through hands-on projects and curriculum from IBM’s experts.
Offered By

What you will learn
Understand the potential applications of machine learning
Gain technical skills like SQL, machine learning modelling, supervised and unsupervised learning, regression, and classification.
Identify opportunities to leverage machine learning in your organization or career
Communicate findings from your machine learning projects to experts and non-experts
Skills you will gain
About this Specialization
Applied Learning Project
In this program, you’ll complete hands-on projects designed to develop your analytical and machine learning skills. You’ll also produce a summary of your insights from each project using data analysis skills, in a similar way as you would in a professional setting, including producing a final presentation to communicate insights to fellow machine learning practitioners, stakeholders, C-suite executives, and chief data officers.
You are highly encouraged to compile your completed projects into an online portfolio that showcases the skills learned in this Specialization.
Some related experience required.
Some related experience required.
How the Specialization Works
Take Courses
A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.
Hands-on Project
Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.
Earn a Certificate
When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

There are 4 Courses in this Specialization
Exploratory Data Analysis for Machine Learning
This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.
Supervised Machine Learning: Regression
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
Supervised Machine Learning: Classification
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
Unsupervised Machine Learning
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
Offered by

IBM
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.
Frequently Asked Questions
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