School of Artificial Intelligence & Generative AI · Foundation
Python for AI & Machine Learning Foundations Course
Start from Python fundamentals and finish with your first end-to-end machine learning project — built, documented and ready for GitHub.
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Python Setup and Developer WorkflowCore Python ProgrammingData Structures and Pythonic Problem SolvingObject-Oriented Python and Packaging
Python 3VS CodeJupyterGitGitHub
How you will learn
What you will learn, module by module
Build Python, data, and core AI foundations as a beginner. Progress from Python Setup and Developer Workflow to AI Foundations Capstone through guided labs, assessed projects, and portfolio evidence.
01Module 1 · 6 hoursPython Setup and Developer WorkflowCreate a clean Python project with venv, Git repository, notebook, CLI script and README.
Topics you will cover
- Python 3 syntax
- VS Code/Jupyter setup
- Virtual environments
- Packages
- Git basics
- Notebooks versus scripts
- Debugging workflow
- Coding conventions
- Tools and platforms
- Python 3, VS Code, Jupyter, Git, GitHub
- Portfolio evidence
- Public starter repository with documented environment
- Assessment
- Hands-on setup check and quiz
02Module 2 · 8 hoursCore Python ProgrammingBuild a data-cleaning CLI that validates inputs, handles errors and exports clean records.
Topics you will cover
- Variables
- Operators
- Strings
- Control flow
- Loops
- Functions
- Scope
- Exceptions
- File handling
- Comprehensions
- Reusable utility functions
- Tools and platforms
- Python
- Portfolio evidence
- CLI data utility
- Assessment
- Coding exercises and lab review
03Module 3 · 8 hoursData Structures and Pythonic Problem SolvingSolve data transformation challenges using efficient Python collections and generators.
Topics you will cover
- Lists
- Tuples
- Dictionaries
- Sets
- Iterators
- Generators
- Sorting
- Lambda functions
- Complexity intuition
- Clean-code patterns
- Tools and platforms
- Python
- Portfolio evidence
- Problem-solving notebook
- Assessment
- Timed coding checkpoint
04Module 4 · 8 hoursObject-Oriented Python and PackagingRefactor a procedural analytics script into a tested reusable Python package.
Topics you will cover
- Classes
- Objects
- Inheritance
- Composition
- Dataclasses
- Modules
- Packages
- Type hints
- Docstrings
- Logging
- Unit-testable design
- Tools and platforms
- Python, pytest
- Portfolio evidence
- Reusable Python package
- Assessment
- Code review and unit tests
05Module 5 · 10 hoursNumPy, pandas and Data PreparationClean and profile a messy customer dataset and produce an analysis-ready feature table.
Topics you will cover
- Arrays
- Vectorisation
- DataFrames
- Indexing
- Joins
- Groupby
- Missing values
- Dates
- Text cleanup
- Reshaping
- Feature-ready datasets
- Tools and platforms
- NumPy, pandas, Jupyter
- Portfolio evidence
- Data preparation notebook
- Assessment
- Data-wrangling lab
06Module 6 · 8 hoursMath and Statistics for AIImplement matrix operations and statistical diagnostics used in ML preprocessing.
Topics you will cover
- Vectors and matrices
- Dot products
- Distributions
- Mean/variance
- Probability
- Correlation
- Sampling
- Train/test intuition
- Optimization and gradient intuition
- Tools and platforms
- NumPy, SciPy
- Portfolio evidence
- AI math reference notebook
- Assessment
- Concept quiz and notebook
07Module 7 · 10 hoursMachine Learning Workflow FoundationsTrain and evaluate baseline classification and regression models with pipelines.
Topics you will cover
- Problem framing
- Features and labels
- Regression versus classification
- scikit-learn workflow
- Preprocessing
- Baseline models
- Metrics
- Overfitting
- Reproducibility
- Tools and platforms
- scikit-learn, pandas
- Portfolio evidence
- First end-to-end ML notebook
- Assessment
- Mini ML project
08Module 8 · 12 hoursAI Foundations CapstoneBuild a beginner AI solution such as churn, demand or lead-scoring prediction and present results.
Topics you will cover
- End-to-end workflow from raw data to model-ready dataset
- Model selection
- Evaluation
- Documentation
- Responsible-use notes
- GitHub presentation
- Tools and platforms
- Python, pandas, scikit-learn, GitHub
- Portfolio evidence
- Portfolio-ready AI foundations project
- Assessment
- Capstone rubric and presentation
Projects you will build
2 portfolio projects plus module evidence
Customer Churn Starter AI
Clean customer data, engineer basic features, train a baseline classifier and explain results.
Data cleaning notebook · Model notebook · README · Metric summary · 5-minute demoDemand Forecasting Foundations
Prepare historical sales data and build a baseline predictive model with error analysis.
Feature table · Model comparison · Error analysis · Business recommendations Course value
Why this course
Beginner Python knowledge becomes valuable when learners can clean data, write reusable code, build a first model, and explain the result.
The curriculum progresses from Python Setup and Developer Workflow to AI Foundations Capstone, with guided labs, assessments, and two portfolio projects: Customer Churn Starter AI and Demand Forecasting Foundations.
Who this course is for
Aspiring AI/ML learners, students, analysts moving into Python and technical professionals starting AI.
FoundationAI & Generative AI Engineer
PrerequisitesNo prior programming experience is required. Basic computer literacy and a willingness to practise coding are recommended.
What you will be able to do
- Create a clean Python project with venv, Git repository, notebook, CLI script and README.
- Build a data-cleaning CLI that validates inputs, handles errors and exports clean records.
- Solve data transformation challenges using efficient Python collections and generators.
- Refactor a procedural analytics script into a tested reusable Python package.
- Clean and profile a messy customer dataset and produce an analysis-ready feature table.
- Train and evaluate baseline classification and regression models with pipelines.
- Build a beginner AI solution such as churn, demand or lead-scoring prediction and present results.
Technology you will use in this course
Python 3VS CodeJupyterGitGitHubpytestNumPypandasSciPyscikit-learn
Career relevance
AI & Generative AI Engineer
This course supports the development of skills for Python and AI learning, AI/ML training and data or AI internship preparation. The strongest learner outcome is a portfolio that shows the problem, implementation, testing or evaluation, documentation and a clear explanation of decisions—not a certificate alone.
Course evidence and instruction
Discuss your learning pathway
Review prerequisites, learning format and project expectations with admissions before enrolment.
Get course guidanceExplore the course projects
Review the project briefs and deliverables to understand the work expected during the course.
Review project expectationsTechnology references
Technology names identify learning tools and do not imply an employer partnership or endorsement.
Microsoft
Clear answers before you enrol
Is the Python for AI course suitable for beginners?
This is a foundation-level course. No prior programming experience is required. Basic computer literacy and a willingness to practise coding are recommended.
What will I build during the course?
You will complete guided labs in every module and build two portfolio projects: Customer Churn Starter AI and Demand Forecasting Foundations. Deliverables include working files or code, documentation, testing or evaluation evidence, and a final presentation.
Which tools and platforms are covered?
Key tools include Python 3, VS Code, Jupyter, Git, GitHub, Python, pytest, and NumPy. Additional platforms are introduced in relevant modules through practical tasks, and the toolset may evolve as industry practice changes.
How long does the course take?
The course includes approximately 70 guided learning hours across 8 modules, normally delivered over 9–11 weeks depending on batch intensity and learner practice time.
Which career paths can this course support?
The curriculum supports the development of skills for Python and AI learning, AI/ML training and data or AI internship preparation. Career outcomes depend on prior experience, project quality, interview readiness and market conditions; employment is not guaranteed.
Will I receive mentor and career support?
The course includes live instruction, lab support, assignment feedback, project reviews and career preparation covering portfolio development, CV writing, LinkedIn profile improvement, and interview guidance.
Ready to start?
A-56, Sector-64, Noida, Uttar Pradesh – 201301Ready to start your Python for AI & Machine Learning Foundations journey?
Review the full curriculum, experience a live class and confirm the right starting point before enrolling.