About this programme
Programme overview
The Artificial Intelligence is a comprehensive, career-oriented program designed to build a strong foundation in Artificial Intelligence while preparing learners for real-world AI roles. This 22-week structured course takes learners from the fundamentals of AI to modern technologies, combining theoretical knowledge with practical implementation and career development.
The course begins with Python programming, mathematical foundations, and core AI concepts before progressing to search algorithms, optimization techniques, knowledge representation, logical reasoning, and expert systems. Learners will then explore Machine Learning, Neural Networks, Deep Learning fundamentals, and modern Generative AI technologies, including Large Language Models (LLMs), Prompt Engineering, Embeddings, and Retrieval-Augmented Generation (RAG). The curriculum also introduces Computer Vision, Reinforcement Learning, and Responsible AI, providing a well-rounded understanding of today's AI landscape.
Throughout the program, learners will gain hands-on experience by completing practical assignments and real-world projects, such as search-based applications, machine learning models, neural networks, AI-powered assistants, computer vision systems, and reinforcement learning agents. These projects are designed to strengthen problem-solving skills while building a professional portfolio that showcases practical AI expertise.
In addition to technical learning, the course emphasizes career readiness. Learners will build a professional portfolio, create an ATS-friendly resume, optimize their GitHub and LinkedIn profiles, prepare for technical and behavioral interviews, and develop effective job search strategies. Industry-standard tools such as ChatGPT, Claude, GitHub Copilot, Hugging Face, LangChain, LlamaIndex, Gymnasium, and Weights & Biases are integrated throughout the learning experience to ensure learners are familiar with modern AI development workflows.
Whether you are a student, software developer, data professional, or career changer, this course provides the knowledge, practical experience, and career preparation needed to confidently begin your journey in Artificial Intelligence and pursue advanced AI specializations or industry roles.
Curriculum
What you'll learn
Python Refresher
Lesson 1: Python RefresherLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson provides a comprehensive refresher on the core Python progr...
Linear Algebra Essentials
Lesson 2: Linear Algebra EssentialsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the fundamental Linear Algebra concepts...
Probability & Bayes Basics
Lesson 3: Probability & Bayes BasicsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson explores the fundamental principles of probab...
Development Environment & Git
Lesson 4: Development Environment & GitLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on setting up a professional AI de...
Landscape of the AI Field
Lesson 5: Landscape of the AI FieldLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson provides a comprehensive introduction to the field...
AI Foundations Notebook & Personal Study Plan
Uninformed search (BFS/DFS)
Lesson 1: Uninformed Search (BFS/DFS)Learning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces uninformed search algorithms, which e...
Informed Search (A*, Heuristics)
Lesson 2: Informed Search (A*, Heuristics)Learning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on informed search techniques that...
Adversarial Search (Minimax)
Lesson 3: Adversarial Search (Minimax)Learning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces adversarial search, where intelligen...
Constraint Satisfaction Problems (CSP)
Lesson 4: Constraint Satisfaction Problems (CSP)Learning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson explores Constraint Satisfaction Prob...
Local Search & Simulated Annealing
Lesson 6: Local Search & Simulated AnnealingLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces optimization algorithms de...
Search & Optimization Algorithms – A Pathfinding and CSP Solver*
Propositional & First-Order Logic
Lesson1:Propositional & First-Order LogicLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the fundamental concepts of...
Inference & Resolution
Lesson 2: Inference & ResolutionLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on inference techniques that enable AI sy...
Semantic Networks & Ontologies
Lesson3:Semantic Networks & OntologiesLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces semantic networks and ontologies...
Expert Systems & Rule Engines
Lesson 4: Expert Systems & Rule EnginesLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson explores expert systems and rule engines t...
Introduction to Knowledge Graphs
Lesson 5: Introduction to Knowledge GraphsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson provides an introduction to knowledge graph...
Rule-Based Expert System
Supervised vs. Unsupervised Learning
Lesson 1: Supervised vs. Unsupervised LearningLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the two primary categories o...
Regression & Classification
Lesson 2: Regression & ClassificationLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on two of the most widely used super...
Decision Trees & Ensemble Learning
Lesson 3: Decision Trees & Ensemble LearningLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson explores Decision Trees and Ensemble...
Model Evaluation Metrics
Lesson 4: Model Evaluation MetricsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the metrics used to evaluate Machine Lea...
Scikit-learn Workflows
Lesson 5: Scikit-learn WorkflowsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the complete Machine Learning workflow usi...
End-to-End Machine Learning Classification
Perceptrons & Activation Functions
Lesson 1: Perceptrons & Activation FunctionsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the perceptron, the fundam...
Backpropagation from Scratch
Lesson 2: Backpropagation from ScratchLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on backpropagation, the learning algori...
Feedforward Networks in PyTorch
Lesson 3: Feedforward Networks in PyTorchLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the implementation of feedforward...
Loss Functions & Optimizers
Lesson 4: Loss Functions & OptimizersLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson explores the role of loss functions and opti...
Overfitting & Regularization
Lesson 5: Overfitting & RegularizationLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on one of the most important challe...
From-Scratch Neural Network Backpropagation
Transformer Architecture Basics
Lesson 1: Transformer Architecture BasicsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the Transformer architecture, the...
Prompt Engineering
Lesson 2: Prompt EngineeringLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on Prompt Engineering, the practice of designing...
Embeddings & Vector Search
Lesson3: Embeddings & Vector SearchLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces embeddings and vector search, two e...
Retrieval-Augmented Generation (RAG)
Lesson 4: Retrieval-Augmented Generation (RAG)Learning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces Retrieval-Augmented Generati...
Claude / OpenAI APIs
Lesson 5: Claude / OpenAI APIsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces learners to integrating Large Language Model...
Responsible Prompting
Lesson 6: Responsible PromptingLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on responsible prompting practices for Generat...
RAG Document Q&A Assistant
CNNs for Image Classification
Lesson 1: CNNs for Image ClassificationLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces Convolutional Neural Networks (CNNs...
Object Detection Basics
Lesson 2: Object Detection BasicsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the fundamentals of object detection, an...
Markov Decision Processes
Lesson 3: Markov Decision ProcessesLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces Markov Decision Processes (MDPs) as a m...
Q-Learning
Lesson 4: Q-LearningLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces Q-learning, a value-based Reinforcement Learning algor...
Policy vs. Value Methods
Lesson 5: Policy vs. Value MethodsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson explores two fundamental approaches to Reinforcemen...
Gymnasium Environments
Lesson 6: Gymnasium EnvironmentsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces Gymnasium, a standard framework for develo...
Computer Vision Classifier & Q-Learning Agent
Bias & Fairness in Models
Lesson 1: Bias & Fairness in ModelsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson explores bias and fairness in Artificial Intel...
Explainability – SHAP & LIME
Lesson 2: Explainability – SHAP & LIMELearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces Explainable AI (XAI) and techniq...
AI Safety & Alignment Basics
Lesson 3: AI Safety & Alignment BasicsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the fundamentals of AI safety an...
Data Privacy & Governance
Lesson 4: Data Privacy & GovernanceLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on data privacy and AI governance, hel...
Regulatory Landscape – EU AI Act & NIST
Lesson 5: Regulatory Landscape – EU AI Act & NISTLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the AI regulatory and...
AI Model Risk Assessment & Bias Audit
End-to-End Problem Framing
Lesson 1: End-to-End Problem FramingLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on transforming a real-world problem into...
Combining Search, Machine Learning & Generative AI
Lesson 2: Combining Search, Machine Learning & Generative AILearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on combining...
Documentation & Reproducibility
Lesson 3: Documentation & ReproducibilityLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson teaches learners how to professionally d...
AI Capstone Projects
capstone project 1
capstone project2
capstone project 3
Site Structure
Lesson 1: Site StructureLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson teaches learners how to organize an effective professional po...
Case-Study Format
Lesson 2: Case-Study FormatLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the recommended format for presenting AI projec...
GitHub Hygiene
Lesson 3: GitHub HygieneLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on maintaining clean and professional GitHub reposito...
AI Portfolio Creation
ATS Formatting
Lesson 1: ATS FormattingLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces Applicant Tracking System (ATS) friendly resume fo...
Quantified Bullets
Lesson 2: Quantified BulletsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson teaches learners how to transform generic resume statemen...
LinkedIn Optimization
Lesson 3: LinkedIn OptimizationLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on optimizing LinkedIn to support the learner'...
AI Engineer / AI Generalist Resume
Algorithms & Search Theory
Lesson 1: Algorithms & Search TheoryLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on revising important algorithms and...
Machine Learning & Neural Network Fundamentals
Lesson 2: Machine Learning & Neural Network FundamentalsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson prepares learners for Mac...
Generative AI & Prompting
Lesson 3: Generative AI & PromptingLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson prepares learners for interviews focused on mo...
Behavioral / STAR Interviews
Lesson 4: Behavioral / STAR InterviewsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on preparing learners for behavioral in...
Mock Interviews
Lesson 5: Mock InterviewsLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson provides practical interview simulation experience through r...
50+ Solved Interview Questions
Target Company List
Lesson 1: Target Company ListLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson teaches learners how to identify and organize companies...
Referrals & Cold Outreach
Lesson 2: Referrals & Cold OutreachLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces professional networking strategies...
Application Tracking
Lesson 3: Application TrackingLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson focuses on systematically tracking job applications and...
Offer Negotiation
Lesson 4: Offer NegotiationLearning Format: Self-Paced CurriculumRecommended Learning Time: 15–18 Hours per WeekLesson DescriptionThis lesson introduces the fundamentals of evaluating and negotiating...
AI Job Search & Application Tracking
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