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Batch
Date: Nov 5th @9:00AM
Faculty: Mr. N. Vijay Sunder Sagar (20+ Yrs of Exp,..)
Duration : 5 Months
Venue
:
DURGA SOFTWARE SOLUTIONS,
Flat No : 202,
2nd Floor,
HUDA Maitrivanam,
Ameerpet, Hyderabad - 500038
Ph.No: +91 - 8885252627, 9246212143, 80 96 96 96 96
Syllabus:
FULL STACK DATA SCIENCE
with Artificial Intelligence
(Generative AI & Agentic AI)
Module 1 : Data Science Introduction
- What is Data Science
- Why Data Scientists are in demand
- The growing need for Data Science
- Data Science Skills
- Data Science Use Cases
- Data Acquisition
- Sources of data
- Data Categorization
- Types of Data
- Techniques
- Data formats
- Data Quantity
- Data Quality
- Resolution Techniques
- Data Transformation
- File format Conversions
- Datascience Life Cycle
Module 2: Python for Data Science
- Python Datatypes
- Python variables
- Python Conditions & loops
- Python Functions basics
- Python Lambda
- Python Lists
- Python Tuples
- Python sets
- Python Dictionaries
- Python File Handling
- Jupyter Notebook setup
- Google Colab
Module 3: Numpy : Python for Data Analysis (Numerical Data)
- Introduction to Numpy
- Numpy Arrays
- Numpy Array Indexing
- 2-D and 3Dimensional Arrays
- Numpy Mathematical operations
- Numpy Flattening and reshaping
- Numpy Horizontal and Vertical Stack
- Numpy linespace and arrange
- Numpy asarray and Random numbers
- Numpy iterations and Transpose
- Numpy Array Manipulation
- Numpy Linear Algebra
- Numpy String Functions
Module 4: Pandas : Python for Data Analysis
- Introduction to Pandas
- Creating Pandas Series
- Creating Data Frames
- Pandas Data Frames from dictionaries
- Pandas Data Frames from list
- Pandas Data Frames from series
- Pandas Data Frames from CSV, Excel
- Pandas Data Frames from JSON
- Pandas Data Frames from Databases
- Extracting rows using loc and iloc
- Pandas dealing with rows and columns
- Pandas indexing and slicing
- Pandas Groupings and Aggregations
- Pandas Merging and concatenating
Module 5: Matplotlib : Python for Data Visualizations
- Introduction to matplotlib
- Installing matplotlib
- Generating graphs
- Normal plottings
- Generating Bargraphs
- Histograms
- Scatter plots
- Stack plots
- Pie plots
- Matplotlib working examples
Module 6: Statistical methods for Datascience
- Types of Statistics
- Descriptive Statistics
- Inferential Statistics
- Central Tendency Measures
- Mean,Median,Mode
- The Story of Average
- Dispersion Measures
- Variance,Range
- Data Distributions
- Sampling
- Types of Sampling
- What is Hypothesis testing
- Confidence Level
- Correlation Analysis
- Uses of Correlation
- Continuous variables
- Categorical variables
Module 7: Machine Learning
- Introduction to Machine Learning
- Areas of Implementation of Machine Learning
- Supervised vs Unsupervised Learning
- Implement Machine Learning Algorithms
- Linear Regression
- Non-Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines
- Naïve’s Bayes Classification
- K Nearest Neighbors
- Clustering Technique
- Partitioning Method
- K-Means Clustering
- Hierarchical Clustering
- Recommender Systems
- Pattern Matching A
- FP-Growth Algorithm
- Collaborative Filering
Module 8: Time Series Analysis and Forecasting
- Time Series Components
- Stationarity Testing (ADF)
- ARIMA Model Parameters
- Forecasting with Prophet
- Forecast Error Metrics
- Backtesting Techniques
- Trend Visualization Methods
- Confidence Interval Analysis
- External Variable Integration
- Model Selection Strategies
Module 9: MLOps Fundamentals
- MLOps Introduction
- CI/CD for ML
- MLflow Model Trackin
- Model Drift Monitoring
- Model Lifecycle Management
Module 10: BigData - Pyspark
- Introduction to Apache Spark
- Why PySpark
- Need for Pyspark
- In Memory Computing – Spark
- PySpark benefits to Professionals
- Components of Spark
- Spark Execution Architecture
- Pyspark sql
- Pyspark practical implementation
Module 11: NLP and Text Mining
- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
- Applications of Text Mining
- OS Module
- Reading, Writing to text and word files
- Setting the NLTK Environment
- Accessing the NLTK Corpora
II) Extracting, Cleaning and preprocessing Text
- Tokenization
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigrams & Ngrams
- Stemming
- Lemmatization
- Stop words
- POS Tagging
- Named Entity Recognition
- Syntax Trees
- Chunking
- Chinking
- Context Free Grammars (CFG)
- Automating Text Paraphrasing
Module 11: Deep Learning
- What is Deep Learning?
- Machine Learning vs. Deep Learning
- Use cases of Deep Learning
- Human Brain vs. Neural Network
- What is Perceptron?
- Learning Rate
- Activation Function
- Single Layer Perceptron
Module 12: TensorFlow 2.0 :
- Introduction to TensorFlow 2.x
- Installing TensorFlow 2.x
- Defining Sequence model layers
- Activation Function
- Layer Types
- Model Compilation
- Model Optimizer
- Model Loss Function
- Model Training
- Digit Classification using Simple Neural Network in TensorFlow 2.x
- Improving the model
- Adding Hidden Layer
- Adding Dropout
- Using Adam Optimizer
Module 13: Convolution Neural Network
- Image Classification Example
- What is Convolution
- Convolutional Layer Network
- Convolutional Layer
- Filtering
- ReLU Layer
- Pooling
- Data Flattening
- Fully Connected Layer
- Predicting a cat or a dog
- Saving and Loading a Model
- Face Detection using OpenCV
Module 14: Boltzmann Machine & Autoencoder
- What is Boltzmann Machine (BM)?
- Identify the issues with BM
- Why did RBM come into the picture?
- Step-by-step implementation of RBM
- Distribution of Boltzmann Machine
- Understanding Autoencoders
- Architecture of Autoencoders
- Brief on types of Autoencoders
- Applications of Autoencoders
Module 15: Generative Adversarial Network (GAN)
- Which Face is Fake?
- Understanding GAN
- What is Generative Adversarial Network?
- How does GAN work?
- Step by step Generative Adversarial Network implementation
- Types of GAN
- Recent Advances: GAN
Module 16: Emotion and Gender Detection
- Where do we use Emotion and Gender Detection?
- How does it work?
- Emotion Detection architecture
- Face/Emotion detection using Haar Cascade
- Implementation on Colab
Module 17: Introduction to RNN and GRU
- Issues with Feed Forward Network
- Recurrent Neural Network (RNN)
- Architecture of RNN
- Calculation in RNN
- Backpropagation and Loss calculation
- Applications of RNN
- Vanishing Gradient
- Exploding Gradient
- What is GRU?
- Components of GRU
- Update gate
- Reset gate
- Current memory content
- Final memory at current time step
Module 18: LSTM
- What is LSTM?
- Structure of LSTM
- Forget Gate
- Input Gate
- Output Gate
- LSTM architecture
- Types of Sequence-Based Model
- Sequence Prediction
- Sequence Classification
- Sequence Generation
- Types of LSTM
- Vanilla LSTM
- Stacked LSTM
- CNN LSTM
Module 19: OpenCV
- Why is OpenCV used?
- What is OpenCV
- Applications
- Demo: Build a Criminal Identification and Detection App
Module 20: Generative AI
- What is Generative AI?
- Generative AI Evolution
- Differentiating Generative AI from Discriminative AI
- Types of Generative AI
- Generative AI Core Concepts
- LLM Modelling Steps
- Transformer Models: BERT, GPT, T5
- Training Process of an LLM Model like ChatGPT
- The Generative AI development lifecycle
- Overview of Proprietary and Open Source LLMs
- Overview of Popular Generative AI Tools and Platforms
- Ethical considerations in Generative AI
- Bias in Generative AI outputs
- Safety and Responsible AI practices
Module 21: Prompt Engineering
- Introduction to Prompt Engineering
- Structure and Elements of Prompts
- Zero-shot Prompting
- One-shot Prompting
- Few-shot Prompting
- Instruction Tuning Basics
- Prompt Testing and Evaluation
- Prompt Pitfalls and Debugging
- Prompts for Different NLP Tasks (Q&A, Summarization, Classification)
- Understanding Model Behavior with Prompt Variation
Module 22: Advanced Prompting Techniques
- Chain-of-Thought (CoT) Prompting
- Tree-of-Thought (ToT) Prompting
- Self-Consistency Prompting
- Generated Knowledge Prompting
- Step-back Prompting
- Least-to-Most Prompting
- Adversarial Prompting & Prompt Injection
- Auto-prompting techniques
- Prompt testing and validation methodologies
Module 23: Working with LLM APIs
- LLM Landscape: OpenAI, Anthropic, Gemini, Mistral API, LLaMA
- Core Capabilities: Summarization, Q&A, Translation, Code Generation
- Efficient Use of Tokens and Context Window
- Calling Tools
- Functions With LLMs
- Deployment Considerations for Open-Source LLMs (Local, Cloud, Fine-Tuning)
- Rate Limits, Retries, Logging
- Understanding Cost, Latency, and Performance and Calculating via Code
Module 24: Building LLM Apps with LangChain &LlamaIndex
- LangChain Overview
- LlamaIndex Overview
- Building With LangChain: Chains, Agents, Tools, Memory
- Understanding LangChain Expression Language (LCEL)
- Working With LlamaIndex: Document Ingestion, Index Building, Querying
- Integrating LangChain and LlamaIndex: Common Patterns
- Using External APIs and Tools as Agents
- Enhancing Reliability: Caching, Retries, Observability
- Debugging and Troubleshooting LLM Applications
Module 25: Developing RAG Systems
- What is RAG and Why is it Important?
- Addressing LLM limitations with RAG
- The RAG Architecture: Retriever, Augmenter, Generator
- DocumentLoaders
- Embedding Models in RAG
- Customizing Prompts for RAG
- Advanced RAG Techniques: Re-ranking retrieved documents
- Query Transformations
- Hybrid Search
- Parent Document Retriever and Self-Querying Retriever
- Evaluating RAG Systems: Retrieval Metrics
Module 26: Vector Databases and Embeddings
- What are Text Embeddings?
- How LLMs and Embedding Models generate embeddings
- Semantic Similarity and Vector Space
- Introduction to Vector Databases
- Key features: Indexing, Metadata Filtering, CRUD operations
- ChromaDB: Local setup, Collections, Document and Embedding Storage
- Pinecone: Cloud-native, Indexes, Namespaces, and Metadata filtering
- Weaviate: Open-source, Vector-native, and Graph Capabilities
- Other Vector Databases: FAISS, Milvus, Qdrant
- Vector Indexing techniques
- Data Modeling in Vector Databases
- Updating and Deleting Vectors
- Choosing the Right Embedding Model
- Evaluation of Retrieval quality from Vector Databases
Module 27: Building End-to-End GenAI Applications
- Architecting LLM-Powered Applications
- Types of GenAI Apps: Chatbots, Copilots, Semantic Search / RAG Engines
- Design Patterns: In-Context Learning vs RAG vs Tool-Use Agents
- Stateless vs Stateful Agents
- Modular Components: Embeddings, VectorDB, LLM, UI
- Key Architectural Considerations: Latency, Cost, Privacy, Memory, Scalability
- Building GenAI APIs with FastAPI
- RESTful Endpoint Structure
- Async vs Sync, CORS, Rate Limiting, API Security
- Orchestration Tools: LangServe, Chainlit, Flowise
- Cloud Deployment: GCP
- Containerization and Environment Setup
Module 28: Evaluating GenAI Applications and Enterprise Use Cases
- Evaluation Metrics: Faithfulness, Factuality, RAGAs, BLEU, ROUGE, MRR
- Human and Automated Evaluation Loops
- Logging, Tracing, and Observability Tools: LangSmith, PromptLayer, Arize
- Prompt and Output Versioning
- Chain Tracing and Failure Monitoring
- Real-Time Feedback Collection
- GenAI Use Cases: Customer Support, Legal, Healthcare, Retail, Finance
- Contract Summarization
- Legal Q&A Bots
- Invoice Parsing with RAG
- Product Search Applications
- Domain Adaptation Strategies
Module 29: Multimodal LLMs
- Introduction to Multimodal LLMs (GPT-4V, LLaVA, Gemini)
- How multimodal models process different data types
- Use Cases: Image Captioning, Visual Q&A, Video Summarization
- Working with Vision-Language Models (VLMs): Image inputs, text outputs
- Image Loaders in LangChain/LlamaIndex
- Simple visual Q&A applications
- Audio Processing with LLMs: Speech-to-Text (ASR)
- Text-to-Speech (TTS) integration
- Video understanding with LLMs
- Challenges in Multimodal AI
- Ethical Considerations in Multimodal AI
- Agent Frameworks (AutoGPT, CrewAI, LangGraph, MetaGPT)
- ReAct and Plan-and-Act agent strategies
- Future Direction
Module 30: LLMOps and Evaluation
- Introduction to LLMOps: Managing the ML Lifecycle for Large Language Models
- Introduction to Model Finetuning: When Prompt Engineering Isn’t Enough
- Overview of Parameter-Efficient Finetuning (PEFT)
- LoRA (Low-Rank Adaptation): Concept and Architecture
- QLoRA: Quantized LoRA for Finetuning Large Models Efficiently
- Adapter Tuning: Modular and Lightweight Finetuning
- Comparing Finetuning Techniques: Full vs. LoRA vs. QLoRA vs. Adapters
- Selecting the Right Finetuning Strategy Based on Task and Resources
- Introduction to Hugging Face Transformers and PEFT Library
- Setting Up a Finetuning Environment with Google Colab
- Preparing Custom Datasets for Instruction Tuning and Task Adaptation
- Monitoring Training Metrics and Evaluating Fine-tuned Models
- Use Cases: Domain Adaptation, Instruction Tuning, Sentiment Customization
Module 31: Agentic AI
- Agentic AI Introduction
- AI Agents vs. Agentic AI
- Comparison: Agentic AI, Generative AI, and Traditional AI
- Agentic AI Building Blocks
- Autonomous Agents
- Human in the Loops Systems
- Single and Multi Agent AI Systems
- Agentic AI Frameworks Overview
- Ethical and Responsible AI
- Agentic AI Best Practices
Module 32: Agentic AI: Architectures and Design Patterns
- Agentic AI Introduction
- AI Agents vs. Agentic AI
- Comparison: Agentic AI, Generative AI, and Traditional AI
- Agentic AI Building Blocks
- Autonomous Agents
- Human in the Loops Systems
- Single and Multi Agent AI Systems
- Agentic AI Frameworks Overview
- Ethical and Responsible AI
- Agentic AI Best Practices
Module 33: Working with LangChain and LCEL Topics
- Components and Modules
- Data Ingestion and Document Loaders
- Text Splitting
- Embeddings
- Integration with Vector Databases
- Introduction to Langchain Expression Language (LCEL)
- Runnables
- Chains
- Building and Deploying with LCEL
- Deployment with Langserve
Module 34: Building AI Agents with LangGraph Topics
- Introduction to LangGraph
- State and Memory
- State Schema
- State Reducer
- Multiple Schemas
- Trim and Filter Messages
- Memory and External Memory
- UX and Human-in-the-Loop (HITL)
- Building Agent with LangGraph
- Long Term Memory
- Short vs. Long Term Memory
- Memory Schema
- Deployment
Module 35: Implementing Agentic RAG
- What is Agentic RAG?\
- Agentic RAG vs. Traditional RAG
- Agentic RAG Architecture and Components
- Understanding Adaptive RAG
- Variants of Agentic RAG
- Applications of Agentic RAG
- Agentic RAG with Llamaindex
- Agentic RAG with Cohere
Module 36: Developing AI Agents with Phidata
- Agents
- Models
- Tools
- Knowledge
- Chunking
- Vector DB
- Storage
- Embeddings
- Workflows
- Developing Agents with Phidata
Module 37: Multi Agent Systems with LangGraph CrewAI
- Multi Agent Systems
- Multi Agent Workflows
- Collaborative Multi Agents
- Multi Agent Designs
- Multi Agent Workflow with LangGraph
- CrewAI Introduction
- CrewAI Components
- Setting up CrewAI environment
- Building Agents with CrewAI
Module 38: Advanced Agent Development with Autogen
- Autogen Introduction
- Salient Features
- Roles and Conversations
- Chat Terminations
- Human-in-the-Loop
- Code Executor
- Tool Use
- Conversation Patterns
- Developing Autogen-powered Agents
- Deployment and Monitoring
Module 39: AI Agent Observability and AgentOPs
- AI Agent Observability and AgentOPs
- Langfuse Dashboard
- Tracing
- Evaluation
- Managing Prompts
- Experimentation
- AI Observability with Langsmith
- Setting up Langsmith
- Managing Workflows with Langsmith
- AgentOps Practical Implementation
Module 40: Building AI Agents with No/Low- Code Tools
- Introduction to No-Code/Low-Code AI
- Benefits and Challenges of No-Code AI Development
- Key Components of No-Code AI Platforms
- Building AI Workflows Without Coding
- Designing AI Agents with Drag-and-Drop Interfaces
- Integrating No-Code AI with Existing Systems
- Customizing and Fine-Tuning AI Solutions
- Optimizing Performance and Efficiency in No-Code AI
- Security and Compliance Considerations in No-Code AI
- Best Practices for Deploying No-Code AI Solutions
- Real-World Use Cases and Applications of No-Code AI
- calling and Future Trends in No-Code AI
