What you will be able to do
Identify the difference between Artificial Intelligence, Machine Learning, and predictions, and explain how they relate to real business problems
Describe the key ingredients of an ML pipeline, from raw data to a trained model
Distinguish between structured and unstructured data, and understand how feature engineering transforms data into signals a model can use
Select the right learning paradigm for a given problem, choosing between supervised learning, deep learning, and unsupervised learning
Explain regression and classification techniques, including linear regression, logistic regression, decision trees, and ensemble methods such as bagging, boosting, and stacking
Evaluate a model properly using train/test splits, k-fold cross-validation, and temporal split strategies
Recognize and explain overfitting, underfitting, data leakage, and spurious correlations, and understand why they cause models to fail in production
Read an ML pipeline description and assess whether the approach is sound, without writing a single line of code
AI Tutor always available
Questions answered instantly, based on the course content. Ask for examples, ask to be tested, progress at your own pace.
Verifiable certificate
Upon completion, you receive a certificate with a public verification page, ready to add to your LinkedIn profile.
Why this course
Concepts grounded in real techniques
You do not just learn that machine learning exists. You learn what regression, classification, ensembles, clustering, and neural networks actually do, and when each one applies.
Model evaluation that reflects real practice
The course covers train/test splits, k-fold cross-validation, sliding and growing window temporal splits, and the specific failure modes, overfitting, underfitting, and data leakage, that cause real projects to disappoint.
No coding required, no depth sacrificed
The course is designed for learners without a technical background, yet it covers ROC AUC, lift curves, backpropagation, and gradient descent at a conceptual level, giving you vocabulary and intuition that hold up in professional conversations.
AI tutor available throughout
A built-in AI tutor is available at every lesson so you can ask follow-up questions, clarify concepts, and move at your own pace without waiting for a live session.
Compact and self-contained
Around 72 minutes of video across 20 lessons means you can complete the full course in a single focused afternoon, or spread it across a week in short daily sessions.
A shareable credential you can use immediately
A LinkedIn-shareable certificate is included upon completion, giving your profile a concrete signal of ML literacy.
Course content
5 modules · 20 lessonsYour instructor
Kelwin Fernandes
CEO, NILG.AI
Who it's for
Business professionals who work alongside data or engineering teams and want to understand what ML can and cannot do
Managers and consultants evaluating AI vendors, proposals, or internal ML projects
Career changers building foundational ML literacy before moving into a more technical role
Marketers, analysts, or operations leads who encounter model outputs in their daily work and want to interpret them confidently
Anyone who has read about machine learning and wants a structured, honest introduction without being sold hype
Frequently asked questions
The ABCs of Machine Learning
Gratuito