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3-MONTH COMPREHENSIVE AI PROGRAM

Name: 3-MONTH COMPREHENSIVE AI PROGRAM
Availability: InStock

12 weeks

Enroll Now ₹59,990

Highlights

Industry-grade curriculum updated quarterly
Real production-style projects
1-on-1 mentor support
Career counselling & resume reviews
Mock interviews with hiring partners
Lifetime access to course materials

Learning Outcomes

Build real projects you can showcase in interviews
Apply industry tools and workflows confidently
Understand core concepts through hands-on practice
Earn a certificate on successful completion

AI Concepts, Career Landscape & Data Foundations20h
- AI landscape 2025 — real applications, career roles (Data Analyst → AI Engineer → AI Architect)
- What is data? Structured, unstructured, semi-structured; data quality vs. quantity
- Types of AI models — text-to-text, image-to-text, multimodal, audio
- Dataset formats: JSONL, CSV, text files; train/validation/test splits
- Types of datasets: labelled, unlabelled, instruction-output
- AI ethics overview — bias, fairness, accountability, transparency
- Lab / Hands-on: Map 10 real-world AI applications. Build a mind-map of 'AI Ecosystem & Career Paths'. Create a custom dataset using a dataset generator tool for a chosen domain.
Python for Data Science + Math & Statistics for AI20h
- Python syntax, data structures, file handling, OOP
- NumPy arrays, Pandas DataFrames, data transformations
- Statistics: mean, median, variance, distributions, correlation vs. causation
- Linear algebra: vectors, matrices — intuition and Python implementation
- Probability basics, hypothesis testing, A/B testing
- Data visualization with Matplotlib and Seaborn
- Lab / Hands-on: Work through the Titanic and a custom domain dataset — compute statistics, handle missing values, visualize distributions, and export a clean dataset.
Data Preprocessing, Feature Engineering & ML Foundations16h
- Data quality: missing values, outliers, noise handling
- Encoding categorical data, scaling, normalization, standardization
- Feature extraction from images, text, and signals
- Supervised vs. Unsupervised Learning — ML workflow overview
- Key algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost, SVM, KNN, K-Means, PCA, Naive Bayes
- Model evaluation: Accuracy, Precision, Recall, F1, AUC, RMSE, Confusion Matrix
- Overfitting, underfitting, cross-validation, hyperparameter tuning (GridSearchCV)
- Ensemble methods: Bagging, Boosting, Stacking
- Lab / Hands-on: Full preprocessing and ML pipeline: clean a raw dataset, engineer features, train 3 different models, evaluate and compare. Deploy best model as a simple prediction script.
PROJECT 1 — End-to-End Machine Learning Solution4h
- Build a complete ML solution for a real-world problem: customer churn prediction, loan default classification, medical diagnosis, or crop yield prediction. The solution must include full data preprocessing, feature engineering, model training, evaluation, and a simple inference interface.

Deep Learning — Neural Networks, CNN, GAN20h
- Neural network fundamentals: perceptron, hidden layers, activation functions
- Forward propagation, backpropagation, gradient descent
- Regularization: dropout, batch normalization, L1/L2
- PyTorch workflow: tensors, datasets, dataloaders, training loop
- CNN architecture: filters, pooling, flattening — image classification
- Transfer Learning: ResNet, VGG, MobileNet — fine-tune for custom image sets
- Generative Adversarial Networks (GANs) — architecture, training dynamics, use cases
- Lab / Hands-on: Image classification with CNN + Transfer Learning on a custom image dataset. Train a simple GAN for synthetic data generation.
NLP, Sequence Models & Transformer Architecture20h
- NLP pipeline: tokenization, stop words, stemming, lemmatization
- Vectorization: Bag-of-Words, TF-IDF, Word2Vec
- Sentiment analysis with classical NLP
- Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
- Transformer architecture: self-attention, multi-head attention, positional encoding
- BERT, GPT, T5 — architecture overview and use cases
- Pre-training vs. fine-tuning — transfer learning for NLP
- Lab / Hands-on: Text classification with LSTM. Sentiment analysis with BERT (Hugging Face). Explore attention visualization on a classification task.
LLM vs SLM — Understanding Scale & the SLM Ecosystem16h
- Why SLMs matter — efficiency, on-premise deployment, domain specialization
- SLM ecosystem: Phi-3 Mini, Mistral 7B, Gemma 2B, TinyLlama, Qwen-2
- Tokenization, embeddings, and context windows in depth
- Benchmarking SLMs: MMLU, HellaSwag, domain-specific evaluation
- Hugging Face Hub: model discovery, versioning, and deployment
- Responsible AI in LLMs: hallucinations, bias, safety red-teaming
- Lab / Hands-on: Set up Hugging Face account. Download and run 3 SLMs locally. Design a domain evaluation benchmark. Compare model outputs on domain tasks.
SLM Fine-tuning at Project Scale20h
- Custom dataset creation: web scraping, annotation, synthetic generation with LLMs
- Dataset formats and quality checks for instruction tuning
- Fine-tuning strategies: Full fine-tune vs. PEFT (LoRA, QLoRA, Adapters)
- Training with Hugging Face Trainer API and TRL library
- Evaluation: BLEU, ROUGE, perplexity, human evaluation rubric
- Instruction tuning and Direct Preference Optimization (DPO)
- Model merging techniques: SLERP, TIES
- Publishing models: Hugging Face Hub model cards
- Lab / Hands-on: Full fine-tuning pipeline: build domain dataset → fine-tune Phi-3 Mini or TinyLlama with LoRA → evaluate vs. base model → publish to Hugging Face Hub.
PROJECT 2 — Domain-Specific Small Language Model4h
- Fine-tune an SLM on a domain-specific dataset of your choice (Healthcare Q&A, Engineering Troubleshooting, Legal FAQ, Education Assistant, or Agriculture Advisory). Demonstrate measurable improvement over the base model on a custom evaluation set.

AI Agent Fundamentals + RAG Knowledge Systems20h
- AI Agent architectures: ReAct, Plan-and-Execute, Reflexion
- Tool use: web search, code execution, database query, external APIs
- Memory systems: short-term (conversation), long-term (vector store), episodic
- LangChain / LlamaIndex: chains, agents, memory, tools
- Why RAG? Retrieval-Augmented Generation — when and how
- Vector databases: FAISS, ChromaDB, Pinecone — embeddings and semantic search
- Advanced RAG: hybrid search, re-ranking, query routing, self-RAG
- Prompt engineering for agents: few-shot, chain-of-thought, structured outputs
- Lab / Hands-on: Build a research agent that reads and reasons over a document base using LangChain + your fine-tuned SLM. Implement a RAG system over a college or company knowledge base.
Multi-Agent Systems + Human-in-the-Loop20h
- Multi-agent frameworks: CrewAI, AutoGen, MetaGPT
- Agent roles: orchestrator, worker, critic, validator, planner
- Human-in-the-loop workflows — approval gates, feedback injection
- Agent evaluation: task success rate, faithfulness, efficiency
- Agent failure modes and fallback strategies
- Knowledge graphs for AI agents
- Responsible agentic AI — boundaries, permissions, transparency
- Lab / Hands-on: Build a 3-agent pipeline using CrewAI: Research Agent + Analysis Agent + Report Generation Agent. Integrate human-in-the-loop approval at the analysis stage.

AI Dashboard Development20h
- Streamlit for rapid AI application development — pages, state, file upload, forms
- Gradio for model sharing and interactive demos
- Plotly / Dash for data-heavy dashboards and analytics
- UI/UX principles for AI applications — clarity, feedback, error handling
- Integrating SLM inference, agent pipelines, and RAG into dashboard UI
- Real-time streaming responses in Streamlit/Gradio
- Lab / Hands-on: Build a multi-page Streamlit dashboard integrating your fine-tuned SLM + agent. Include a chat interface, document upload for RAG, and a model performance analytics page.
Full-Stack AI Applications & Deployment20h
- FastAPI for AI backend development — REST endpoints, async, request validation
- Model serving: batch inference, streaming, async queues
- Frontend integration: connecting React / HTML+JS to a FastAPI backend
- Authentication, session management, rate limiting, usage logging
- Docker containerization for AI services
- Deployment targets: Hugging Face Spaces, Render, AWS EC2 basics
- CI/CD for AI: GitHub Actions — test, build, deploy pipeline
- Lab / Hands-on: Build and deploy a full AI web application — FastAPI backend serving your SLM + agent, Streamlit/Gradio frontend, Dockerized, deployed to Hugging Face Spaces or Render.

MLOps, Responsible AI & Capstone Presentations20h
- Model versioning with MLflow and DVC
- Experiment tracking with Weights & Biases (W&B)
- Monitoring in production: data drift, model drift, performance degradation
- Responsible AI by Design: embedding ethical reasoning into system architecture
- Responsible AI in Design: awareness of societal impact, fairness, accountability
- AI Safety: prompt injection, adversarial inputs, jailbreaks and defenses
- IP, licensing, and open-source model policies
- Professional AI project presentation: GitHub, README, demo video, model card
- Lab / Hands-on: Audit your own AI system for bias and safety issues. Set up MLflow experiment tracking. Configure a GitHub Actions CI/CD pipeline. Prepare and deliver Capstone presentation.

Production AI System2h
- Each participant designs and delivers a complete production-ready AI system addressing a real-world problem. The system must include: a fine-tuned SLM or domain model, at least one AI agent with tool use, a deployed dashboard or web application, a REST API backend, and a CI/CD pipeline. Domains: Healthcare, Legal, Education, Agriculture, Manufacturing, Finance, or any socially impactful area.

Ready to start 3-MONTH COMPREHENSIVE AI PROGRAM?

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Enroll Now Back to Courses

Highlights

Industry-grade curriculum updated quarterly
Real production-style projects
1-on-1 mentor support
Career counselling & resume reviews
Mock interviews with hiring partners
Lifetime access to course materials

Learning Outcomes

Build real projects you can showcase in interviews
Apply industry tools and workflows confidently
Understand core concepts through hands-on practice
Earn a certificate on successful completion

AI Concepts, Career Landscape & Data Foundations20h
- AI landscape 2025 — real applications, career roles (Data Analyst → AI Engineer → AI Architect)
- What is data? Structured, unstructured, semi-structured; data quality vs. quantity
- Types of AI models — text-to-text, image-to-text, multimodal, audio
- Dataset formats: JSONL, CSV, text files; train/validation/test splits
- Types of datasets: labelled, unlabelled, instruction-output
- AI ethics overview — bias, fairness, accountability, transparency
- Lab / Hands-on: Map 10 real-world AI applications. Build a mind-map of 'AI Ecosystem & Career Paths'. Create a custom dataset using a dataset generator tool for a chosen domain.
Python for Data Science + Math & Statistics for AI20h
- Python syntax, data structures, file handling, OOP
- NumPy arrays, Pandas DataFrames, data transformations
- Statistics: mean, median, variance, distributions, correlation vs. causation
- Linear algebra: vectors, matrices — intuition and Python implementation
- Probability basics, hypothesis testing, A/B testing
- Data visualization with Matplotlib and Seaborn
- Lab / Hands-on: Work through the Titanic and a custom domain dataset — compute statistics, handle missing values, visualize distributions, and export a clean dataset.
Data Preprocessing, Feature Engineering & ML Foundations16h
- Data quality: missing values, outliers, noise handling
- Encoding categorical data, scaling, normalization, standardization
- Feature extraction from images, text, and signals
- Supervised vs. Unsupervised Learning — ML workflow overview
- Key algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost, SVM, KNN, K-Means, PCA, Naive Bayes
- Model evaluation: Accuracy, Precision, Recall, F1, AUC, RMSE, Confusion Matrix
- Overfitting, underfitting, cross-validation, hyperparameter tuning (GridSearchCV)
- Ensemble methods: Bagging, Boosting, Stacking
- Lab / Hands-on: Full preprocessing and ML pipeline: clean a raw dataset, engineer features, train 3 different models, evaluate and compare. Deploy best model as a simple prediction script.
PROJECT 1 — End-to-End Machine Learning Solution4h
- Build a complete ML solution for a real-world problem: customer churn prediction, loan default classification, medical diagnosis, or crop yield prediction. The solution must include full data preprocessing, feature engineering, model training, evaluation, and a simple inference interface.

Deep Learning — Neural Networks, CNN, GAN20h
- Neural network fundamentals: perceptron, hidden layers, activation functions
- Forward propagation, backpropagation, gradient descent
- Regularization: dropout, batch normalization, L1/L2
- PyTorch workflow: tensors, datasets, dataloaders, training loop
- CNN architecture: filters, pooling, flattening — image classification
- Transfer Learning: ResNet, VGG, MobileNet — fine-tune for custom image sets
- Generative Adversarial Networks (GANs) — architecture, training dynamics, use cases
- Lab / Hands-on: Image classification with CNN + Transfer Learning on a custom image dataset. Train a simple GAN for synthetic data generation.
NLP, Sequence Models & Transformer Architecture20h
- NLP pipeline: tokenization, stop words, stemming, lemmatization
- Vectorization: Bag-of-Words, TF-IDF, Word2Vec
- Sentiment analysis with classical NLP
- Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
- Transformer architecture: self-attention, multi-head attention, positional encoding
- BERT, GPT, T5 — architecture overview and use cases
- Pre-training vs. fine-tuning — transfer learning for NLP
- Lab / Hands-on: Text classification with LSTM. Sentiment analysis with BERT (Hugging Face). Explore attention visualization on a classification task.
LLM vs SLM — Understanding Scale & the SLM Ecosystem16h
- Why SLMs matter — efficiency, on-premise deployment, domain specialization
- SLM ecosystem: Phi-3 Mini, Mistral 7B, Gemma 2B, TinyLlama, Qwen-2
- Tokenization, embeddings, and context windows in depth
- Benchmarking SLMs: MMLU, HellaSwag, domain-specific evaluation
- Hugging Face Hub: model discovery, versioning, and deployment
- Responsible AI in LLMs: hallucinations, bias, safety red-teaming
- Lab / Hands-on: Set up Hugging Face account. Download and run 3 SLMs locally. Design a domain evaluation benchmark. Compare model outputs on domain tasks.
SLM Fine-tuning at Project Scale20h
- Custom dataset creation: web scraping, annotation, synthetic generation with LLMs
- Dataset formats and quality checks for instruction tuning
- Fine-tuning strategies: Full fine-tune vs. PEFT (LoRA, QLoRA, Adapters)
- Training with Hugging Face Trainer API and TRL library
- Evaluation: BLEU, ROUGE, perplexity, human evaluation rubric
- Instruction tuning and Direct Preference Optimization (DPO)
- Model merging techniques: SLERP, TIES
- Publishing models: Hugging Face Hub model cards
- Lab / Hands-on: Full fine-tuning pipeline: build domain dataset → fine-tune Phi-3 Mini or TinyLlama with LoRA → evaluate vs. base model → publish to Hugging Face Hub.
PROJECT 2 — Domain-Specific Small Language Model4h
- Fine-tune an SLM on a domain-specific dataset of your choice (Healthcare Q&A, Engineering Troubleshooting, Legal FAQ, Education Assistant, or Agriculture Advisory). Demonstrate measurable improvement over the base model on a custom evaluation set.

AI Agent Fundamentals + RAG Knowledge Systems20h
- AI Agent architectures: ReAct, Plan-and-Execute, Reflexion
- Tool use: web search, code execution, database query, external APIs
- Memory systems: short-term (conversation), long-term (vector store), episodic
- LangChain / LlamaIndex: chains, agents, memory, tools
- Why RAG? Retrieval-Augmented Generation — when and how
- Vector databases: FAISS, ChromaDB, Pinecone — embeddings and semantic search
- Advanced RAG: hybrid search, re-ranking, query routing, self-RAG
- Prompt engineering for agents: few-shot, chain-of-thought, structured outputs
- Lab / Hands-on: Build a research agent that reads and reasons over a document base using LangChain + your fine-tuned SLM. Implement a RAG system over a college or company knowledge base.
Multi-Agent Systems + Human-in-the-Loop20h
- Multi-agent frameworks: CrewAI, AutoGen, MetaGPT
- Agent roles: orchestrator, worker, critic, validator, planner
- Human-in-the-loop workflows — approval gates, feedback injection
- Agent evaluation: task success rate, faithfulness, efficiency
- Agent failure modes and fallback strategies
- Knowledge graphs for AI agents
- Responsible agentic AI — boundaries, permissions, transparency
- Lab / Hands-on: Build a 3-agent pipeline using CrewAI: Research Agent + Analysis Agent + Report Generation Agent. Integrate human-in-the-loop approval at the analysis stage.

AI Dashboard Development20h
- Streamlit for rapid AI application development — pages, state, file upload, forms
- Gradio for model sharing and interactive demos
- Plotly / Dash for data-heavy dashboards and analytics
- UI/UX principles for AI applications — clarity, feedback, error handling
- Integrating SLM inference, agent pipelines, and RAG into dashboard UI
- Real-time streaming responses in Streamlit/Gradio
- Lab / Hands-on: Build a multi-page Streamlit dashboard integrating your fine-tuned SLM + agent. Include a chat interface, document upload for RAG, and a model performance analytics page.
Full-Stack AI Applications & Deployment20h
- FastAPI for AI backend development — REST endpoints, async, request validation
- Model serving: batch inference, streaming, async queues
- Frontend integration: connecting React / HTML+JS to a FastAPI backend
- Authentication, session management, rate limiting, usage logging
- Docker containerization for AI services
- Deployment targets: Hugging Face Spaces, Render, AWS EC2 basics
- CI/CD for AI: GitHub Actions — test, build, deploy pipeline
- Lab / Hands-on: Build and deploy a full AI web application — FastAPI backend serving your SLM + agent, Streamlit/Gradio frontend, Dockerized, deployed to Hugging Face Spaces or Render.

MLOps, Responsible AI & Capstone Presentations20h
- Model versioning with MLflow and DVC
- Experiment tracking with Weights & Biases (W&B)
- Monitoring in production: data drift, model drift, performance degradation
- Responsible AI by Design: embedding ethical reasoning into system architecture
- Responsible AI in Design: awareness of societal impact, fairness, accountability
- AI Safety: prompt injection, adversarial inputs, jailbreaks and defenses
- IP, licensing, and open-source model policies
- Professional AI project presentation: GitHub, README, demo video, model card
- Lab / Hands-on: Audit your own AI system for bias and safety issues. Set up MLflow experiment tracking. Configure a GitHub Actions CI/CD pipeline. Prepare and deliver Capstone presentation.

Production AI System2h
- Each participant designs and delivers a complete production-ready AI system addressing a real-world problem. The system must include: a fine-tuned SLM or domain model, at least one AI agent with tool use, a deployed dashboard or web application, a REST API backend, and a CI/CD pipeline. Domains: Healthcare, Legal, Education, Agriculture, Manufacturing, Finance, or any socially impactful area.

3-MONTH COMPREHENSIVE AI PROGRAM

Course Overview

Highlights

Learning Outcomes

Course Curriculum

Ready to start 3-MONTH COMPREHENSIVE AI PROGRAM?

3-MONTH COMPREHENSIVE AI PROGRAM

Course Overview

Highlights

Learning Outcomes

Course Curriculum

Ready to start 3-MONTH COMPREHENSIVE AI PROGRAM?