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
- 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
- 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
- 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
- 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
- Vectorization
- DataFrames
- Indexing
- Joins
- Groupby
- Missing values
- 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
- 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
- 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 relevant to roles such as Junior Python/AI learner, AI/ML trainee, and data/AI intern pathway. 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
Ranjeet Kumar
Advisor, Brightnest AI Academy · Innovation & Growth LeaderA technologist and data leader with 15+ years of experience applying data, artificial intelligence and machine learning to complex problems, scalable products and business growth.
Learner experience
I progressed from AI fundamentals to building GenAI applications and a source-aware RAG assistant I could confidently demonstrate.
Industry and technology ecosystem
Microsoft
Clear answers before you enrol
Start from Python fundamentals and finish with your first end-to-end machine learning project - built, documented and ready for GitHub.
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 relevant to roles such as Junior Python/AI learner, AI/ML trainee, and data/AI intern pathway. 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, resume 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.