Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

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.

Syllabus

Start Here: Get Oriented and Check Your Skills

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.

Job Task 1: Plan and Select Your ML Approach

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.

Job Task 2: Build Linear Models and SVMs

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.

Job Task 3: Build Tree-Based and Ensemble Models

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.

Wrap Up: Review Your Skill Achievement and Choose Your Next Path

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.