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GEN AI & LLM ENGINEER

Prerequisite: IT Professionals with Strong Python (or any language) Coding Skills, Mathematical and Statistical Expertise, SQL/Database Experience, Cloud Platform Knowledge, and Good Communication Skills.

Job Prospects: GEN AI & LLM ENGINEER

 

COURSE  REGISTRATION LINK

 

COURSE-1: Python - Quick Recap (10 Sessions)

 

MODULE-1: PYTHON FUNDAMENTALS

  • Python Installation (Windows / Mac)
  • Variables and Data Types
  • Operators
  • Conditional Statements & Loops
  • Functions
  • File Handling 
  • Exceptional Handling

 

MODULE-2: DATA STRUCTURES

  • Lists
  • Dictionaries
  • Tuples
  • Sets

 

MODULE-3: OBJECT-ORIENTED PROGRAMMING

  • Classes, Objects and Methods
  • Inheritance and Polymorphism
  • Abstraction and Encapsulation

 

MODULE-4: ADVANCED PYTHON

  • Decorators
  • Packages and Modules
  • API Development (Flask)
  • SQL, MySQL and SQLite Overview

 

MODULE-5: PYTHON LIBRARIES

  • NumPy
  • Pandas
  • Matplotlib
  • Scikit
 
 

COURSE-2: Data Modeling Introduction (5 Sessions)

 

MODULE-1: EDA-EXPLORATORY DATA ANALYSIS WITH THE DATASET

  • Understanding Data Distribution
  • Identifying Missing Values
  • Handling Outliers
  • Data Visualization Techniques 
  • Feature Selection

 

MODULE-2: UNDERSTANDING MODEL METRICS

  • Introduction to Model Metrics
  • Classification Metrics (Accuracy, Precision, Recall, F1-Score, ROC Curve and AUC)
  • Regression Metrics (MAE, MSE, RMSE & R-Squared)
  • Clustering Metrics (Silhouette Score, Davies-Bouldin Index, Inertia)
 
 

MODULE-3: VECTOR DATABASE

  • Introduction to Vector Databases (e.g., Pinecone, Milvus)
  • Use Cases in Natural Language Processing (NLP) (e.g., Semantic Search, Recommendation Systems)
  • Hands-on with a Vector Database (e.g., Using Python to interact with a Vector DB)
 
 
 

COURSE-3: Artificial Intelligence (AI) Introduction (10 Sessions)

 

MODULE-1: MACHINE LEARNING (ML) CONCEPTS

  • Supervised Learning (Linear, Logistic Regression)
  • Unsupervised Learning (K-means & DBSCAN Clustering)
  • Reinforcement Learning (Q-Learning)
  • Identifying the Suitable ML Algorithm 
  • Implementation Using Scikit-learn

 

MODULE-2: DEEP LEARNING (DL) CONCEPTS

  • Neurons, Perceptrons
  • Layers
  • Activation Functions
  • Forward Propagation, Back Propagation
  • Loss Functions, Optimization
 
 

MODULE-3: TENSORFLOW BASICS

  • Building and Training Models
  • TensorFlow Keras API
  • Hands-on with TensorFlow for Basic Models
  • Introduction to TensorFlow Datasets
  • Custom Training Loops and Saving Models
 
 

MODULE-4: DEEP LEARNING NEURAL NETWORKS

  • Basics of Convolutional Neural Network (CNN)
  • Introduction to Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) & Gated Recurrent Unit (GRU)
  • Introduction to Natural Language Processing (NLP)
  • Text Preprocessing Techniques
  • Key NLP Tasks and Examples
  • Word Embeddings

 

COURSE-4: Cloud Platform (AWS) Introduction (2 Sessions)

 

MODULE-1: AMAZON WEB SERVICES (AWS) FUNDAMENTALS

  • Overview of AWS
  • AWS Compute and Storage Services
  • AWS Database Services
  • Deployment and Management Tools
  • Monitoring, Security, and Cost Management
 

MODULE-2: BUILDING MODEL USING SAGEMAKER

  • Introduction to Amazon SageMaker
  • Setting Up SageMaker
  • Preparing Data
  • Building and Training Models
  • Deploying and Managing Models
 
 

COURSE-5: GEN AI & Large Language Models (LLMs) (13 Sessions)

 

MODULE-1: TRANSFORMERS

  • Building Transfomers Models & Architecture
  • Encoder & Decoder Architectures
  • Attention Mechanisms, Self Attention and Multi-Head Attention
  • Position-Wise Feed-Forward Networks (FFN)
  • Transfer Learning with Transformers 
 

MODULE-2: LARGE LANGUAGE MODELS (LLMs)

  • Key Concepts of LLMs
  • Introduction to Generative Pre-trained Transformer (GPT)
  • Introduction to Bidirectional Encoder Representations from Transformers (BERT)
  • Introduction to Text-to-Text Transfer Transformer  (T5)
  • Evaluation Metrics
 

MODULE-3: GENERATIVE ARTIFICIAL INTELLIGENCE (GEN AI)

  • Key Concepts of GenAI
  • Introduction to Generative Adversarial Networks (GAN)
  • Introduction to Variational AutoEncoders (VAE)
  • Text Generation Techniques

 

MODULE-4: RETRIEVAL-AUGMENTED GENERATION (RAG)

  • Introduction to RAG
  • Retrieval Mechanism
  • Generation Techniques
  • Fusion Techniques
  • Evaluation Metrics
 

STUDENT ASSIGNMENTS

  • Build an ML Loan Prediction Model with Linear Regressionn
  • Build an ML Classification Model using Logistic Regression
  • Build a DL Model for Next Word Generation
  • Build a DL Model for Text Summarization
  • Build a DL Model for Sentimental Analysis
  • Mini-Project to Build a Chatbot
 

 

PRACTICE PLATFORM

  • Google Colab
  • Kaggle
  • AWS
 

SELF-STUDY

  • Mathematics (Linear Algebra, Calculus, Probability) and Statistics
  • Python Coding 
  • Cloud Platform (AWS / Azure / GCP)
  • Databases and SQL

 

  • Live Training with Demos
  • Practical Assignments & Assessments
  • Real-Time Scenarios
  • Communication Skills Guidance* 
  • Effective Resume Building*
  • Interview Preparation / Mock Interview* 
  • Placement Assistance*  

*Selected Individuals Only

 

Live Classes: ~40 Sessions (~60 Hours)

Assignments: 80+ hours

Duration: ~10 Weeks 
 
FEE: US $800
 
 
 
 
**As per the tutor's discretion, some of the provided course content may be altered/omitted to suit the class needs**
 
**Products and Logos Mentioned are Trademarks of the Respective Companies**
 
**Provided Individual Course Fee is not applicable for Corporate Customers**
 
 






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