Best Gen AI and Machine Learning Training in Bangalore

• Unit 1: Statistics, Probability, Linear Algebra, and Essential Mathematics for AI and Machine Learning
• Unit 2: NumPy Arrays, Vector Operations, Matrix Calculations, and Efficient Numerical Computing
• Unit 3: Pandas DataFrames, Data Loading, Filtering, Transformation, Aggregation, and Feature Preparation
• Unit 4: Data Cleaning, Missing Values, Outlier Treatment, Encoding, Scaling, and Feature Engineering
• Unit 5: Exploratory Data Analysis and Interactive Visualization for Understanding Machine Learning Datasets

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

• Unit 1: Regression and Classification with Linear Regression, Logistic Regression, Decision Trees, and Random Forest
• Unit 2: Ensemble Learning with Gradient Boosting, XGBoost, LightGBM, and AdaBoost Algorithms
• Unit 3: SVM, KNN, Naive Bayes, Model Selection, and Practical Classification Workflows
• Unit 4: Clustering, Dimensionality Reduction, K-Means, Hierarchical Clustering, and PCA
• Unit 5: Cross-Validation, Performance Metrics, Hyperparameter Optimization, and Model Interpretation

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

• Unit 1: Neural Networks, Perceptrons, Forward Propagation, Backpropagation, and Deep Learning Concepts
• Unit 2: Activation Functions, Loss Functions, Optimizers, Gradient Descent, Regularization, and Training Strategies
• Unit 3: CNN Architectures, Image Classification, Feature Extraction, and Computer Vision Applications
• Unit 4: NLP Foundations, Text Processing, RNNs, LSTMs, GRUs, Sequence Learning, and Text Classification
• Unit 5: Transfer Learning, Pretrained Models, Model Evaluation, and Fine-Tuning Deep Learning Networks

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

• Unit 1: Generative AI Fundamentals, Foundation Models, Transformer Architecture, Attention, and Self-Attention
• Unit 2: Tokenization, Embeddings, Positional Encoding, Context Windows, and LLM Input Processing
• Unit 3: BERT, T5, GPT, Llama, Mistral, Gemini, and Other Modern Large Language Model Concepts
• Unit 4: Prompt Engineering, Zero-Shot, Few-Shot, Role-Based, Structured, and Context-Aware Prompting
• Unit 5: Working with Commercial and Open-Source LLMs through APIs, Hugging Face, and Local Model Runtimes

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

• Unit 1: Retrieval-Augmented Generation Architecture, Knowledge Sources, Document Loading, and Context Retrieval
• Unit 2: Document Chunking, Embedding Models, Semantic Search, Similarity Matching, and Retrieval Strategies
• Unit 3: Vector Database Concepts, Indexing, Metadata Filtering, Hybrid Search, and Similarity Queries
• Unit 4: Developing Knowledge-Based AI Applications with LangChain, LlamaIndex, and Retrieval Pipelines
• Unit 5: Advanced RAG with Query Rewriting, Reranking, Hybrid Retrieval, GraphRAG Concepts, and RAG Evaluation

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

• Unit 1: Agentic AI Fundamentals, ReAct Patterns, Reasoning, Planning, Task Decomposition, and Decision Workflows
• Unit 2: Function Calling, Tool Usage, API Integration, Structured Outputs, and External System Connectivity
• Unit 3: Multi-Agent Systems, Agent Roles, Collaboration Patterns, Task Routing, and Workflow Orchestration
• Unit 4: Agent Memory, State Management, Human-in-the-Loop Controls, Error Handling, and Context Management
• Unit 5: Building Intelligent Automation Solutions with AI Agents for Multi-Step Business and Technical Tasks

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

• Unit 1: PEFT, LoRA, QLoRA, Instruction Tuning, Dataset Preparation, and LLM Customization
• Unit 2: Hugging Face Workflows, Synthetic Data, Model Adaptation, Quantization, and Open-Source LLM Optimization
• Unit 3: Multimodal AI for Text, Images, Audio, Video, Vision-Language Models, and Cross-Modal Applications
• Unit 4: LLMOps Fundamentals, Model Evaluation, Prompt Testing, Observability, Monitoring, Guardrails, and Cost Management
• Unit 5: Model Serving, API Integration, Containerization, Local LLM Deployment, and Production AI Workflows

Tools used in this Paper: Hugging Face, Unsloth, Ollama, vLLM, MLflow, Docker

• Unit 1: End-to-End AI Solution Design Combining Machine Learning, LLMs, RAG, Vector Stores, and AI Agents
• Unit 2: Developing Enterprise Knowledge Assistants with Custom Documents, Semantic Retrieval, and Advanced RAG
• Unit 3: Creating Domain-Specific AI Applications with Fine-Tuned Models, APIs, and Intelligent Automation
• Unit 4: Building User-Facing AI Solutions with Streamlit, Gradio, FastAPI, and Modern Deployment Practices
• Unit 5: Production Readiness, AI Evaluation, Responsible AI, GitHub Portfolio, and Capstone Presentation

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