Machine Learning Zoomcamp: Free Course

About this course

Learn machine learning engineering by following a practical route from a dataset to a deployed prediction service. This free DataTalks.Club Machine Learning Zoomcamp preserves all 97 videos in the core playlist, including its orientation sessions, demonstrations, summaries and deployment material. It is intended for learners who already have programming experience.

Course at a glance

What you’ll learn

• Frame a machine learning problem, prepare data and establish a validation approach before selecting a model.
• Develop regression and classification examples and interpret the effect of features and model choices.
• Compare evaluation methods and tree-based or neural approaches in the context of the course projects.
• Follow the deployment sections to understand how trained models can become services with reproducible environments.

Skills you’ll gain

• Data preparation
• Regression and classification
• Model evaluation
• Feature engineering
• Model serving
• ML deployment

Course Overview

The curriculum introduces supervised learning and a project workflow, then develops regression through a car-price example and classification through customer churn. Evaluation follows with accuracy, precision, recall, ROC-related concepts and cross-validation. Subsequent sections cover serving a model, environment management, tree-based learning, deep learning, serverless deployment and Kubernetes. The source combines recordings and updates from different teaching editions; this listing follows that published core sequence. Use the recorded tool versions as context rather than assuming that a current cohort repository and every older demonstration are identical.

How to study the course

• Begin with the orientation and foundational videos, then work through the regression and classification projects. Preserve a clear separation between training and validation data.
• Use the evaluation section before comparing more complex models. Decide what a metric tells you and what it leaves unanswered for the chosen task.
• Finish the deployment, deep-learning and infrastructure material at a practical pace. The opening sessions, homework-solution video and summaries remain in the curriculum.

Use the searchable classroom lesson list to revisit a specific explanation. Pause after a useful demonstration, reproduce a small example and explain the result in your own words. Keep a short note of what worked, what changed and what you still need to understand before moving on.

Who is this for?

The course is for developers, analysts and technical students moving into machine learning engineering. It suits learners who want to build and explain an end-to-end project rather than study only isolated algorithms.

Before you start

Programming experience, basic command-line skills and familiarity with Python are helpful. Cloud services or additional tools used for practice may incur costs. The videos are free; cohort assessment and certificate eligibility are separate from self-paced viewing.

Put the lessons into practice

Choose a small public or invented dataset with a clear prediction target. Describe the problem, prepare the data and establish a validation split before training a baseline. Compare one alternative model using a metric appropriate to the task, and record what changed. Package a simple prediction interface in a practice environment and document how it should be run. Keep the project small enough to explain its assumptions, limitations and reproduction steps. This independent activity supports learning and does not count as a graded cohort submission.

This optional activity is provided by WikiFree for independent practice. It is not a graded assignment from the instructor. Keep your working notes, check the result against the task you defined and return to the relevant lesson when an assumption or technique is unclear.

Certificate / Assessment

Watching the public videos and using WikiFree progress tracking does not award a certificate, exam voucher, academic credit or instructor assessment. The provider has separate cohort assessment criteria; those are not included in this self-paced video listing.

Use your practice output to review what you can now explain and repeat independently. Completion tracking records your study progress; it does not replace an assessed qualification or professional experience.

Frequently asked questions

Provider: DataTalks.Club. Videos play from the DataTalksClub channel on YouTube.