Machine Learning Fundamentals
Develop the skills to train and evaluate a tabular prediction model through a learning path centred on Machine Learning Fundamentals. Explore concepts, guided practice and an applied project.
Build practical Machine Learning Fundamentals skills.
Skills and knowledge developed in this course.
- Data preparation
- Supervised models
- Model selection
- Interpretation and delivery
- Applied project: Train and evaluate a tabular prediction model
- Machine Learning Fundamentals — validation and review
About this course
This course develops practical capability in Machine Learning Fundamentals. Its course-specific content covers Data preparation, Supervised models, Model selection, Interpretation and delivery, followed by an applied project focused on train and evaluate a tabular prediction model. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Learners starting in artificial intelligence & generative ai and professionals who want an organized foundation before more advanced work.
Before you start
Basic computer literacy and comfort using the relevant operating system. No prior production experience is assumed; any lab accounts or software are confirmed before training.
Train and evaluate a tabular prediction model
Deliver train and evaluate a tabular prediction model. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Data preparation
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- Missing values and categorical encoding
- Train-test separation and leakage prevention
Practical exerciseApply data preparation to the train and evaluate a tabular prediction model and capture the validation evidence.
02Supervised models
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- Linear models and decision trees
- Classification and regression losses
Practical exerciseApply supervised models to the train and evaluate a tabular prediction model and capture the validation evidence.
03Model selection
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- Cross-validation and hyperparameters
- Precision, recall and baseline comparisons
Practical exerciseApply model selection to the train and evaluate a tabular prediction model and capture the validation evidence.
04Interpretation and delivery
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- Feature importance and error slices
- Save a model and document its limits
Practical exerciseApply interpretation and delivery to the train and evaluate a tabular prediction model and capture the validation evidence.
05Applied project: Train and evaluate a tabular prediction model
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- Define inputs, assumptions and acceptance criteria for: Train and evaluate a tabular prediction model.
- Implement the project using data preparation, supervised models, model selection, interpretation and delivery.
Practical exerciseTrain and evaluate a tabular prediction model.
06Machine Learning Fundamentals — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Data preparation · Supervised models
Establish the Machine Learning Fundamentals scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Model selection · Interpretation and delivery
Apply the core Machine Learning Fundamentals techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for train and evaluate a tabular prediction model. - 04Learning milestone 4 In progress
Deliver the applied project
Complete train and evaluate a tabular prediction model, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗