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Batch Date: July 26th @7:30AM

Faculty: Mr. Naveen Mourya (9+ Yrs Of Exp,..)

Duration: 3 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:

ADVANCED GENERATIVE AI

Module 1: Python, Maths & Statistics Fundamentals

Objective: Build strong Programming, Maths & Statistical fundamentals for AI and data processing.

1.1 Python Basics

• What is Python and why is it popular in AI?
• Identifiers, Key Words, Basic Data types: (Int , Float, String, Complex & Boolean)
• Fundamental Data types: [List],(tuple),{set} & {dict}, Basic Syntax, Advantages.
• Operators, Operator precedence & Expressions
• Input and Output Statements

1.2 Control Flow

• What are Control Flow Statements in Python? Advantages & Basic Syntax
• Conditional Statements ( if, if-elif, if-else & if-elif-else)
• Transfer Statements (break, continue & pass)
• Iterative Statements (for & while)

1.3 Functions & Modules

• Functions: In built & User Defined Functions
• Parameters, Return Statements, Types of arguments: [positional, keyword, default & Variable length arguments], Types of Variables: [Global/Local Variables]
• Recursive/Nested/Anonymous/Normal/lambda & Syntax/ filter/map() with lambda & without lambda, reduce(): Advantages of these functions
• Function Aliasing, import concept and Function vs Module vs Library
• Standard Modules: datetime, os, math, random, re, json, requests & use cases

1.4 File Handling & Exception Handling

• What is File Handling: Importance in AI
• Types of Files: [Text Files, Binary Files]
• Opening & Closing a File, Reading Data from text files, Writing Data to text files.
• The with statement - (The seek() and tell() methods:)
• Handling csv files: [Writing data to csv file, Reading Data from csv file]
• Handling Json Files:[Writing data to Json file, Reading Data from Json file]
• Exception Handling: [What is Exception , Default Exception Handling in Python]
• REST APIs calling using Json, Import & Requests modules

1.5 Libraries

NumPy (for tensors, embeddings, vector math)

• NumPy Basics: What is NumPy and why is it important in ML/AI, NumPy installation
• Arrays: 1D, 2D, and higher dimensions, Applications
• Difference between Python list and NumPy array
• Numpy Advanced: Array indexing and slicing
• Mathematical operations
• Reshape, flatten
• Broadcasting
• Useful functions: arange(), linspace(), eye(), ones(), zeros()

Pandas (for cleaning and preparing tabular/nested data)

• Pandas Basics: What is Pandas and use cases
• Series and DataFrames, Applications
• Creating DataFrames from dict/list/CSV
• Viewing data: head(), tail(), info(), describe()
• Pandas Advanced: Indexing, slicing, filtering, Adding/deleting columns
• Aggregations: groupby(), sum(), mean()
• Handling missing values: isna(), fillna(), replace()

Matplotlib (for visualizing model performance & embeddings)

• Matplotlib Basics: Introduction to Matplotlib, pyplot and plotting syntax
• Line plots, bar plots, scatter plots
• Titles, labels, legends
• Matplotlib Advanced: Subplots, Histograms, Pie charts
• Styling: colors, markers, line types

1.6 Linear Algebra

• Vectors, Matrices, Matrix Operations
• Eigen Vectors & Eigenvalues

1.7 Probability & Statistics

• Probability, Conditional Probability & Distributions
• Statistical Measures(Z score, Skewness, Kurtosis, Geometric Distribution)
• Bias, Variance, Standard Deviation & Covariance
• Population, Sample, Data Types, Sampling Methods & Variables
• Measure of Central Tendency, Symmetry, Spread & Variability
• Hypothesis Testing(Null & Alternative Hypothesis, Type-I & II Errors)

Module 2: Machine Learning Essentials

Objective: Learn Machine Learning basics required for Generative AI

2.1 Machine Learning Basics

• What is Machine Learning, why it is important for AI & Applications
• Labeled aata & Unlabeled data, Types of ML
• ML WorkFlow

2.2 Supervised Learning

• What is Supervised Learning and its real-world applications
• Labeled data - Regression & Classification
• Linear & Logistic Regression
• Train-Test split, Model Evaluation Metrics - MSE/RMSE, R^2(Coefficient of Determinant)-Under/Over/Regular Fit, Accuracy, Precision , Recall, F1 Score

2.3 Unsupervised Learning

• What is Unsupervised Learning and its real-world applications.
• Unlabeled data - Clustering, Association & Dimensionality Reduction
• K-means clustering & Principal Component Analysis(PCA)
• Metrics: ROC-AUC

2.4 Feature Engineering

• One-Hot Encoding, Label Encoding
• Feature Scaling (MinMax, StandardScaler)

Module 3: Deep Learning & Neural Networks

Objective: Build Deep Learning Models using TensorFlow and PyTorch

3.1 Introduction to Neural Networks

• Perceptron, Feedforward Networks
• Backpropagation, Loss Functions
• Activation Functions: ReLU, Sigmoid, Softmax

3.2 Training Deep Learning Models

• Optimizers: SGD, Adam, RMSProp
• Epochs, Batches
• Learning Rate Schedules
• Regularization techniques (Dropout, L1/L2)

3.3 Advanced Architectures

• Network Design Best Practices
• Transfer Learning
• Multimodal Deep Learning

Module 4: Natural Language Processing (NLP)

Objective: Enable machines to understand and Process Human Language

4.1 Introduction to NLP, History of NLP

4.2 Text Pre-processing

What is Text Pre-processing and why is it essential for NLP?

• Tokenization, Stopword Removal
• Stemming, Lemmatization
• POS Tagging, Named Entity Recognition (NER)

4.3 Text Vectorization

What are Word Embeddings and why are they important?

• Bag of Words, Countvectorizer
• TF-IDF
• Word2Vec, GloVe, FastText
• Sentence Embeddings using BERT

4.4 Applications

• Text Classification
• Spam Detection
• Sentiment Analysis (Movie Reviews, Product Reviews)
• RNN (Recurrent Neural Networks)
• LSTM (Long Short-Term Memory)
• GRU (Gated Recurrent Unit)

Mini project and Exercises on this topic are by default covered

Module 5: Foundations of Generative AI

Objective: Understand the Principles of Generative AI and its real-world uses.

5.1 What is Generative AI? and How is it different from traditional AI?

• Generative vs Discriminative Models
• Use Cases: Text, Audio, Image, Chatbots
• Text Generation Applications

5.2 Tools and Datasets

• Hugging Face Transformers
• OpenAI GPT APIs
• Google Gemini, Mistral, LLaMA
• OpenAI API Key usage and implementation
• Free Llama API access options (OpenRouter, Together AI, Puter.js)

Mini project and Exercises on this topic are by default covered

Module 6: Transformers & Large Language Models

Objective: Master the architecture that powers GPT, BERT, and LLaMA

6.1 Transformers Architecture

• Encoder-Decoder Mechanism
• Self-Attention, Multi-Head Attention
• Positional Encoding
• Cross vs Self Attention

Detailed problem explaining the Architecture will be covered.

Exercises based on Transfer learning are by default covered in this stage.

6.2 Transfer Learning

• What is Transfer Learning and why is it important in AI?
• Pretrained Models and Fine-Tuning
• Use Cases and Real-World Examples

Module 7: Large Language Models (LLMs)

Objective: Understand the core concepts behind LLMs like GPT, BERT, and LLaMA.

7.1 What are LLMs?

• What are Large Language Models and why are they important?
• How LLMs differ from traditional NLP models
• Evolution from RNNs to Transformers to LLMs

7.2 Types of LLMs

• GPT, BERT, RoBERTa, T5, LLaMA
• Instruction-Tuned Models
• Parameter Sizes: 7B, 13B, 70B
• Fine-Tuning vs Prompt Tuning

7.3 Fine-Tuning Fundamentals

• What is Fine-Tuning and why is it critical for Generative AI?
• Full Fine-Tuning vs. Parameter-Efficient Methods
• Data Requirements and Preparation
• Avoiding Catastrophic Forgetting
• Evaluation Strategies

7.4 Parameter-Efficient Fine-Tuning

• LoRA (Low-Rank Adaptation) & QLoRA
• PEFT (Parameter Efficient Fine-Tuning)
• Adapter Tuning

Module 8: Embeddings & Semantic Search

Objective: Understand how LLMs represent text and retrieve meaning.

8.1 Embedding Fundamentals

• What Are Embeddings?
• High-Dimensional Representation

8.2 Embedding Models

• SentenceTransformers
• BERT-based Embeddings
• OpenAI Embedding Models

8.3 Semantic Search

• Cosine Similarity
• KNN Search in Vector Space
• Use Cases: FAQ Bots, Contextual Search

8.4 Working with Vector Stores

• Inserting Embeddings into FAISS, Pinecone, ChromaDB
• Mini project/ Exercises are by default covered in this stage

Module 9: RAG, LangChain & Orchestration

Objective: Build systems that combine LLMs with your custom data

9.1 LangChain Framework

What is LangChain and why is it important in LLM workflows?

• Introduction to the LangChain framework
• Understanding the purpose and core components of LangChain Framework
• PromptTemplate, LLMChain, SequentialChain
• Memory, Tools, Agents
• Output Parsers
• Basics of LlamaIndex

9.2 Vector Databases

What are Vector Databases and why are they important for storing embeddings?

• FAISS, Pinecone, ChromaDB
• Sentence Embeddings with Transformers
• Storing & Retrieving Documents

9.3 Retrieval-Augmented Generation (RAG)

What is RAG and how it improves LLM performance?

• End-to-End Architecture: Query → Embed → Retrieve → Prompt
• Custom Loaders (PDFs, Docs)
• Building applications using Retrieval-Augmented Generation

Mini project / Exercises are by default covered in this stage

Module 10: Fine-Tuning & Model Customization

Objective: Tailor LLMs to your specific domain and tone.

10.1 Fine-Tuning Techniques

What is Fine-Tuning and why is it critical for Generative AI?

• Basics of Prompt Engineering
• LoRA (Low-Rank Adaptation)
• PEFT (Parameter Efficient Fine-Tuning)
• Hugging Face Trainer for Custom Models

Module 11: Capstone Projects

Objective: Showcase your knowledge in real-world applications.

Capstone Projects

Along with multiple mini projects covered for each and every topic in the flow. In this stage, we will create specific GEN AI applications like

Covering 15+ mini projects in the curriculum and 2 capstone projects. In this stage, we will create specific GEN AI applications like

Enterprise-Grade Capstone Projects

Project 1: End-to-End AWS GenAI Application

Project 2: End-to-End GCP GenAI Application

Module 12: Interview Preparation

• Common Gen AI Interview Questions
• Resume & Portfolio Preparation
• Industry Knowledge
• Advanced Learning Resources
• Staying Current with GenAI Research