AI & Machine Learning Fundamentals
Build a strong, practical foundation in artificial intelligence, machine learning, data and responsible AI before specializing further.
What you'll learn
Understand AI, machine learning, deep learning and generative AI at a practical level.
Learn how machines learn from data and where different approaches are useful.
Understand datasets, features, labels, training, validation and inference.
Explore core supervised, unsupervised and neural-network concepts.
Use simple Python-based examples to understand ML workflows.
Recognize common AI risks, limitations and responsible-use considerations.
Build a small introductory ML project and a roadmap for further specialization.
Career opportunities
This course includes
Live instructor-led classes
Beginner-friendly demonstrations
Guided coding exercises
AI/ML mini-projects
Career roadmap
Assessment and certificate
Who it's for
- Absolute beginners
- Students exploring AI careers
- Non-technical professionals who want AI literacy
- Managers and entrepreneurs
- Career switchers deciding between AI pathways
Curriculum
12 modules · Live classes · Hands-on practice · Practical assignments
- AI definition and applications
- Machine learning as a subset of AI
- Deep learning
- Generative AI overview
Practical / Lab: Classify real-world products into AI, ML and non-ML use cases.
- Data, features and labels
- Training and inference
- Patterns and generalization
- Overfitting concept
Practical / Lab: Walk through a simple learning problem step by step.
- Structured vs unstructured data
- Data quality
- Bias and leakage
- Train/validation/test concepts
Practical / Lab: Inspect a dataset and identify quality issues.
- Variables and data structures
- Functions
- Libraries
- Notebook workflow
Practical / Lab: Write a small Python program that prepares data.
- Regression
- Classification
- Examples and use cases
- Basic evaluation
Practical / Lab: Train a simple model and interpret the result.
- Clustering
- Anomaly detection
- When labels are unavailable
Practical / Lab: Group a small dataset and explain the clusters.
- Neurons and layers
- Training concept
- Images and text
- Where deep learning fits
Practical / Lab: Build a simple neural-network demonstration.
- LLMs at a high level
- Tokens and context
- Prompting fundamentals
- Limitations and hallucinations
Practical / Lab: Design prompts and evaluate responses critically.
- Accuracy and reliability
- Bias and fairness
- Privacy
- Security
- Human oversight
Practical / Lab: Review an AI use case for risks and safeguards.
- AI/ML roles
- Data roles
- Learning pathways
- Portfolio strategy
Practical / Lab: Create a personal 6–12 month learning roadmap.
- Problem selection
- Data preparation
- Simple model
- Results
Practical / Lab: Build a small practical AI/ML project.
- Problem framing
- Data
- Model
- Evaluation
- Presentation
Practical / Lab: Present a beginner-friendly AI/ML solution and explain its limitations.
Requirements
- No programming experience is required.
- Basic computer skills are sufficient.
- Laptop and stable internet connection are required.
- A willingness to practice is more important than prior AI knowledge.
Program description
This is the entry point to the AI/ML category. It is designed for learners who want to understand the field before committing to a deeper engineering or data-science pathway.
Rather than teaching a long list of algorithms, the program focuses on mental models: what AI is, how ML learns from data, how models are evaluated, where AI fails and how to choose the next specialization.
Capstone / final project
Final project: choose a simple real-world problem, prepare a small dataset, build an introductory model or AI workflow, evaluate the result and explain where human judgment is still required.
Recommended tools & platforms
Batches
New batches forming · limited seats
Mode
Live instructor-led training
Duration
12 weeks
24/7 support