Best Gen AI and Machine Learning Training in Chennai

• Unit 1: Linear Algebra, Calculus, and Probability Fundamentals for Machine Learning Models
• Unit 2: High-Performance Vector Computations and Matrix Operations using NumPy
• Unit 3: Data Transformation, Wrangling, Indexing, and Feature Manipulation via Pandas
• Unit 4: Missing Value Imputation, Outlier Detection, Data Cleaning, and Feature Scaling
• Unit 5: Exploratory Data Analysis (EDA) and Visualization with Matplotlib, Seaborn, and Plotly

Tools used in this Paper: NumPy, Pandas, Matplotlib, Seaborn, Jupyter Notebook

• Unit 1: Supervised Learning: Linear/Logistic Regression, Decision Trees, and Random Forests
• Unit 2: Advanced Ensemble Methods: Gradient Boosting, XGBoost, LightGBM, and AdaBoost
• Unit 3: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Naive Bayes Classifiers
• Unit 4: Unsupervised Learning: K-Means Clustering, Hierarchical Clustering, and PCA
• Unit 5: Model Evaluation Metrics, Cross-Validation Strategies, and Hyperparameter Tuning

Tools used in this Paper: Scikit-learn, XGBoost, Python, Jupyter Notebook

• Unit 1: Neural Network Architecture, Perceptrons, Forward and Backward Propagation
• Unit 2: Activation Functions, Loss Optimization, Gradient Descent Variants, and Regularization
• Unit 3: Convolutional Neural Networks (CNNs) for Image Processing and Computer Vision
• Unit 4: Recurrent Neural Networks (RNNs), LSTMs, GRUs, and Sequence Modeling
• Unit 5: Training Deep Neural Networks, Transfer Learning, and Model Fine-Tuning

Tools used in this Paper: PyTorch, TensorFlow, Keras, CUDA

• Unit 1: Attention Mechanisms, Self-Attention, and Transformer Architecture Fundamentals
• Unit 2: Tokenization, Word Embeddings, Positional Encoding, and Vector Representations
• Unit 3: Pre-training, Encoder-Decoder Models (BERT, T5), and Autoregressive Models (GPT Series)
• Unit 4: Prompt Engineering Techniques: Zero-Shot, Few-Shot, and Chain-of-Thought Prompting
• Unit 5: Integrating Commercial and Open-Source LLMs via Hugging Face and API Endpoints

Tools used in this Paper: Hugging Face Transformers, OpenAI API, Ollama, Python

• Unit 1: RAG Architecture Fundamentals, Context Retrieval, and Document Ingestion
• Unit 2: Text Chunking Strategies, Vector Embeddings Generation, and Semantic Search
• Unit 3: Vector Database Management: Indexing, Similarity Search, and Metadata Filtering
• Unit 4: Building Enterprise RAG Applications using LangChain and LlamaIndex Frameworks
• Unit 5: Advanced RAG Strategies: Hybrid Search, Re-Ranking, Query Rewriting, and Evaluation

Tools used in this Paper: LangChain, LlamaIndex, Pinecone, ChromaDB, FAISS

• Unit 1: Introduction to Agentic AI, Reasoning Frameworks (ReAct), and Planning Loops
• Unit 2: Tool Integration, External Function Calling, and Structured API Actions
• Unit 3: Multi-Agent Collaboration Networks, Role Assignment, and Task Delegation
• Unit 4: State Management, Memory persistence, and Human-in-the-Loop Workflows
• Unit 5: Orchestrating Autonomous Workflows for Complex Enterprise Task Automation

Tools used in this Paper: CrewAI, LangGraph, AutoGen, Python

• Unit 1: Parameter-Efficient Fine-Tuning (PEFT), LoRA, and QLoRA Techniques
• Unit 2: Dataset Curation, Instruction Tuning, Synthetic Data Generation, and Alignment
• Unit 3: Model Quantization (GGUF, AWQ, GPTQ) and High-Throughput Model Serving
• Unit 4: LLMOps Pipeline Setup: Model Monitoring, Logging, Guardrails, and Cost Optimization
• Unit 5: Production Deployment of Fine-Tuned Gen AI and Machine Learning Models

Tools used in this Paper: vLLM, DeepSpeed, Unsloth, MLflow, Docker

• Unit 1: Full System Design: Combining ML Pipelines, Vector Stores, and Gen AI Agents
• Unit 2: Building Enterprise Search Systems with Custom Document Retrieval and RAG
• Unit 3: Developing Domain-Specific Fine-Tuned LLMs for Automated Industry Workflows
• Unit 4: Building Interactive AI Applications using Streamlit, Gradio, and FastAPI Frameworks
• Unit 5: Enterprise Deployment, Technical Documentation, GitHub Portfolio, and Systems Showcase

Tools used in this Paper: Python, PyTorch, LangChain, FastAPI, Streamlit, Git