Supervised Machine Learning

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Supervised Machine Learning

This course is part of multiple programs.

Instructor: Professionals from the Industry

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Choose supervised ML approaches; Build regression, SVM, and tree models; Tune ensembles for better performance

Details to know

Shareable certificate

Add to your LinkedIn profile

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When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

Build practical supervised machine learning skills by working through the kinds of tasks you may see in data science, machine learning, and AI-related roles. In this course, you’ll learn how to turn business problems into clear ML tasks, choose the right modeling approach, and build supervised learning models for classification, regression, forecasting, and tabular prediction problems.

This is not a traditional lecture-by-lecture course. The experience is organized around workplace skills and job tasks, so you can focus on what you need to perform the work. You’ll start by checking your current skills, then personalize your path by reviewing only the lessons that match your goals and prior knowledge. When you already know a skill, you can move ahead. You’ll learn from curated lessons across expert instructors, with each resource selected for the specific skill it teaches best. By completing this course, you can strengthen your readiness for roles such as data analyst, junior data scientist, machine learning associate, or AI practitioner.

Start here to learn how this skill-based course works and find your recommended starting point. You’ll take a short, ungraded diagnostic to check your current skills, then decide whether to go directly to the graded skill assessments or review targeted learning content first.

What's included

1 reading4 assignments

Use this module to build the skills for the job task Plan and Select Your ML Approach. You'll learn how to translate business problems into well-defined ML tasks, set clear objectives and success metrics, evaluate algorithm options against problem requirements, and justify your model choice based on data characteristics and constraints. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

What's included

4 videos6 readings1 assignment3 ungraded labs

4 videosTotal 9 minutes

What Makes a Real-World ML Project Successful?3 minutes
Choosing the Right Model Isn't Just About Accuracy2 minutes
Establishing a Baseline – Part 1: Training Simple Models2 minutes
Establishing a Baseline – Part 2: Evaluation and Model Selection2 minutes

6 readingsTotal 42 minutes

What Makes a Problem Statement Good or Bad?6 minutes
Fixing and Framing ML Problems Across Domains6 minutes
How to Identify and Structure an ML Problem8 minutes
Success Metrics and Real-World Constraints8 minutes
Why Baselines Matter: Measuring Progress with Simple Models7 minutes
Choosing the Right Advanced Model for the Right Task7 minutes

1 assignmentTotal 15 minutes

Knowledge Check: ML Problem Formulation15 minutes

3 ungraded labsTotal 150 minutes

Define Your Own ML Problem30 minutes
Train and Evaluate Your Baseline Models60 minutes
Train an Advanced Model on Your Dataset60 minutes

Use this module to build the skills for the job task Build Linear Models and SVMs. You'll learn how to implement and optimize linear and logistic regression models, check key modeling assumptions, apply regularization techniques, and use Support Vector Machines with linear, RBF, and polynomial kernels for complex classification and regression tasks. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

What's included

8 videos5 readings2 assignments3 ungraded labs

8 videosTotal 44 minutes

Regression in Action: Predicting Sales From Advertising 6 minutes
Understanding Regression Through a Real-World Example6 minutes
Script-Building and Evaluating a Simple Linear Regression Model6 minutes
Getting Started with Logistic Regression for Binary Classification6 minutes
Evaluating Binary Classification Models with Logistic Regression6 minutes
Using SVMs to Recognize Handwritten Digits5 minutes
How SVMs Make Decisions: Margins and Support Vectors4 minutes
Using the RBF Kernel to Improve Classification5 minutes

5 readingsTotal 42 minutes

What Is Linear Regression and How Does It Work? 7 minutes
Evaluating a Linear Regression Model10 minutes
What Is Logistic Regression and Why Do We Use It? 10 minutes
How Support Vector Machines Make Decisions 7 minutes
Understanding the Kernel Trick in SVMs8 minutes

2 assignmentsTotal 30 minutes

Knowledge Check: Linear Regression Key Concepts15 minutes
Knowledge Check: SVM Key Concepts15 minutes

3 ungraded labsTotal 180 minutes

Predicting House Prices Using Linear Regression60 minutes
Predicting Loan Approval Using Logistic Regression60 minutes
Classifying Handwritten Digits Using SVMs60 minutes

Use this module to build the skills for the job task Build Tree-Based and Ensemble Models. You'll learn how to build and tune tree-based models such as CART, Random Forest, XGBoost, and LightGBM, then extend that foundation by designing ensemble methods including bagging, boosting, and stacking to improve robustness and accuracy on tabular datasets. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

What's included

10 videos6 readings1 assignment4 ungraded labs

10 videosTotal 30 minutes

Why Single Decision Trees Can Overfit: A Visual Primer3 minutes
Random Forest for Classification: Iris Dataset Walkthrough4 minutes
Random Forest for Regression: Predicting House Prices3 minutes
Implementing XGBoost and LightGBM for Boosted Classification3 minutes
Hyperparameter Tuning with GridSearchCV: Optimizing XGBoost3 minutes
How Bagging Stabilizes Predictions and Reduces Variance2 minutes
How Boosting Learns from Mistakes — One Model at a Time3 minutes
Implementing XGBoost and LightGBM for Boosted Classification3 minutes
What Is Stacking? A Simple Visual Explanation3 minutes
How to Train a Stacking Model (Without Leaking Data)4 minutes

6 readingsTotal 54 minutes

Understanding Bagging and Random Forests 8 minutes
Understanding Hyperparameters in Random Forests10 minutes
Tuning Boosting Models: Key Hyperparameters Explained10 minutes
Boosting Algorithms Explained: From AdaBoost to XGBoost & LightGBM10 minutes
When and How to Use Stacking Effectively8 minutes
Stacking in Practice: Understanding the StackingClassifier Structure8 minutes

1 assignmentTotal 15 minutes

Knowledge Check: Bagging and Random Forests15 minutes

4 ungraded labsTotal 240 minutes

Bagging in Action: Predicting Customer Churn with Random Forest60 minutes
Using Boosting Models to Predict Heart Disease60 minutes
Using Boosting Models to Predict Heart Disease60 minutes
Building and Evaluating a StackingClassifier on Loan Default Data60 minutes

Review the skills you practiced and demonstrated in this course, then prepare to describe them in career-relevant ways. You’ll also explore recommended skill paths that can help you continue building related job-ready skills.

What's included

2 readings

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Instructor

Professionals from the Industry

489 Courses112,906 learners

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