Complete Curriculum
3-Month Intensive AI Program
Research-driven curriculum designed to transform you into an industry-ready AI professional
Month 1: Foundations
Building blocks - Python, Statistics, ML Basics, and AI Understanding
🐍
Python Foundation
4 modules • 2 weeks
Modules:
- • Module 1: Python Basics (Variables, Data Types, Operators)
- • Module 2: Control Statements (IF-ELSE, Loops, BREAK/CONTINUE)
- • Module 3: Data Structures (List, Tuple, Sets, Dictionary)
- • Module 4: Functions (Parameters, Lambda, Map/Reduce/Filter)
Learning Outcomes:
- • Master Python syntax and data structures
- • Implement control flow and functions
- • Handle different data types efficiently
- • Apply functional programming concepts
📊
Statistics Essentials
4 modules • 1 week
Modules:
- • Module 1: Overview of Statistics (Descriptive/Inferential, Data Types)
- • Module 2: Harnessing Data (Sampling, Cochran's Formula, Sampling Methods)
- • Module 3: EDA (Central Tendencies, Distribution, CLT, Skewness)
- • Module 4: Hypothesis Testing (P-Value, T-tests, ANOVA)
Learning Outcomes:
- • Understand statistical concepts and sampling
- • Perform exploratory data analysis
- • Conduct hypothesis testing
- • Apply statistical methods to ML problems
🧠
Artificial Intelligence Foundation
6 modules • 2 weeks
Modules:
- • Module 1: AI Overview (Evolution, History, AI vs ML vs DS)
- • Module 2: Deep Learning Introduction (DNN, Feature Learning)
- • Module 3: TensorFlow Foundation (Structure, ML Modeling)
- • Module 4: Computer Vision (CNN, Image Classification)
- • Module 5: NLP (Bag of Words, Word Embedding, BERT)
- • Module 6: AI Ethics (Bias, Trust, Ethical Concerns)
Learning Outcomes:
- • Understand AI fundamentals and applications
- • Implement deep learning models with TensorFlow
- • Build CNN models for image classification
- • Apply NLP techniques and BERT
- • Address AI ethics and bias concerns
🤖
Machine Learning Associate
9 modules • 3 weeks
Modules:
- • Module 1: ML Introduction (Clustering, Classification, Regression)
- • Module 2: NumPy Package (Arrays, Matrix Operations)
- • Module 3: Pandas Package (Series, DataFrame, Data Munging)
- • Module 4: Matplotlib (Visualization, Plots, Sub-plots)
- • Module 5: Seaborn (Advanced Data Visualizations)
- • Module 6: Linear Regression (Best Fit Line, Evaluation)
- • Module 7: Logistic Regression (Sigmoid Curve, Classification)
- • Module 8: K-Means Clustering (Unsupervised Learning)
- • Module 9: KNN (Nearest Neighbor Concept)
Learning Outcomes:
- • Master Python data science libraries (NumPy, Pandas)
- • Create effective data visualizations
- • Implement core ML algorithms
- • Build end-to-end ML pipelines
- • Evaluate and optimize model performance
📝
Git & Version Control
5 modules • 1 week
Modules:
- • Module 1: Git Introduction (Version Control, Workflow, Architecture)
- • Module 2: Repository & GitHub (Init, User Setup, Remote Repos)
- • Module 3: Commits, Pull, Fetch & Push (Conflicts Resolution)
- • Module 4: Tagging, Branching & Merging (Branches, Merge, Reset)
- • Module 5: GitHub & Bitbucket (Collaboration, Remote Workflows)
Learning Outcomes:
- • Master Git version control system
- • Collaborate effectively on projects
- • Manage branches and resolve conflicts
- • Use GitHub for project management
Month 2: Advanced Machine Learning & Deep Learning
Advanced ML algorithms, Feature Engineering, Deep Learning, and Deployment
🎯
Machine Learning Expert
7 modules • 3 weeks
Modules:
- • Module 1: Feature Engineering (Encoding, Scaling, Missing Values)
- • Module 2: Support Vector Machine (SVM, Kernel Trick)
- • Module 3: Principal Component Analysis (PCA)
- • Module 4: Decision Tree & Random Forest
- • Module 5: Ensemble Techniques - Bagging
- • Module 6: Naïve Bayes (Bayes' Theorem, Text Classification)
- • Module 7: Gradient Boosting & XGBoost
Learning Outcomes:
- • Master advanced ML algorithms
- • Implement feature engineering techniques
- • Build ensemble models
- • Optimize model performance
🧠
Artificial Intelligence Expert
11 modules • 4 weeks
Modules:
- • Module 1: Neural Networks (Weight Init, Optimizers, Activation)
- • Module 2: Deep Neural Networks (Keras, MNIST)
- • Module 3: Computer Vision - Image Recognition (CNN, Transfer Learning)
- • Module 4: Object Detection (RCNN, YOLO, SSD)
- • Module 5: Recurrent Neural Networks (LSTM, Bi-directional)
- • Module 6: NLP (Regex, Word Embedding, BERT, GPT)
- • Module 7: Prompt Engineering
- • Module 8: Reinforcement Learning (MDP, Dynamic Programming)
- • Module 9: Deep Reinforcement Learning (DQN, OpenAI Gym)
- • Module 10: Generative AI - GAN (Core Concepts, Applications)
- • Module 11: Autoencoders (Vanilla, Denoising, Variational)
Learning Outcomes:
- • Build deep learning models with TensorFlow/Keras
- • Implement computer vision and NLP solutions
- • Master prompt engineering techniques
- • Create generative AI models (GANs, Autoencoders)
- • Apply reinforcement learning concepts
Month 3: AI Agents & Agentic Systems
Building autonomous AI agents using modern frameworks
🏗️
Week 1: Foundations
Agentic Workflow, Agent Patterns, Orchestrating LLMs
Topics:
- • Understanding AI Agents & Agent Architecture
- • ReAct Pattern (Reasoning + Acting)
- • Planning and Reasoning in Agents
- • Tool Use and Memory Systems
- • Multi-LLM Orchestration
- • Chain of Thought & Sequential Execution
Project:
Personal Career Agent
- • Resume analysis and job recommendations
- • Interview preparation assistant
- • Career counseling AI agent
🤖
Week 2-3: OpenAI Agents SDK
Assistants API, Threads, Code Interpreter, File Search
Topics:
- • OpenAI Assistants API & Threads
- • Code Interpreter & File Search
- • Tools vs Agents & Guardrails
- • Safety, Rate Limiting & Monitoring
- • Multi-agent Collaboration
- • Roles, Goals & Task Delegation
Skills:
- • Build intelligent assistants
- • Implement multi-agent systems
- • Handle tool integration
- • Manage agent communication
🔗
Week 4-5: LangGraph & AutoGen
Graph-based workflows, Multi-agent conversations
LangGraph:
- • Graph-based agent workflows
- • State management & Conditional edges
- • Cycles and loops in graphs
- • Tools, Memory & Web Searches
- • RAG Integration
AutoGen:
- • Conversational agents
- • Group chat management
- • Human-in-the-loop
- • Agent teaching & specialization
- • Distributed agent systems
🔗
Week 6: MCP (Model Context Protocol)
Server-client architecture, Context sharing, Multiple servers
Topics:
- • Understanding MCP protocol
- • Server-client architecture
- • Context sharing across applications
- • Building MCP servers and clients
- • Multiple local and remote servers
- • Load balancing & failover
Project:
AI Equity Traders
- • Multi-agent trading system
- • Real-time market data integration
- • Risk management & execution
Prerequisites for Each Section
Month 1 Prerequisites
- • Basic computer literacy
- • High school mathematics
- • No programming experience required
- • Dedication to learn
Month 2 Prerequisites
- • Complete Month 1 modules
- • Python programming skills
- • Basic ML understanding
- • Statistics knowledge
Month 3 Prerequisites
- • Complete Month 2 modules
- • Deep learning experience
- • API integration skills
- • System design understanding
Interactive Learning Features
Downloadable Curriculum PDF
Get the complete curriculum in PDF format with detailed module descriptions, learning outcomes, and project requirements.
Download PDFModule-wise Expandable Content
Each module includes expandable sections with detailed explanations, code examples, and hands-on exercises.
Explore ModulesWhat You'll Master
Python programming & data science
Machine learning algorithms
Deep learning & neural networks
Large language models & NLP
Generative AI & LLMs
Prompt engineering & fine-tuning
RAG & vector databases
MLOps & model deployment
Computer vision & image processing
Natural language processing
AI Agents & Multi-agent systems
Production AI systems
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