Participants will explore the vast potential of generative AI across daily tasks and advanced applications, understanding its impact on business, society, and ethical considerations. By the end of the course, learners will be equipped with the skills to generate precise, contextually relevant responses, and apply generative AI in a collaborative, inclusive manner, unlocking new opportunities in the tech and AI fields and contributing to the advancement of this transformative technology.

Modules Included:

Gen AI:

Unit -1: Introduction to Generative AI

• Overview of Generative AI
• Distinction between traditional AI and Generative AI
• Applications of Generative AI in image, text, and audio generation

Unit-II: Fundamentals of Neural Networks

• Definition and overview of neural networks
• Biological inspiration for artificial neural networks
• Basic structure of a neural network: neurons, layers, and connections
• Activation functions: sigmoid, tanh, ReLU
• Forward Propagation and Backward Propagation

Unit-III: Generative Models

• Understanding GANs in-depth
• Exploring GAN architectures (DCGAN, WGAN, etc.)
• Training strategies for stable GANs
• Applications of GANs in image generation, style transfer, and data augmentation
• Lab: Implementing a GAN for Audio generation

Unit IV- Variational Autoencoders (VAEs) and LSTM

• Variational Autoencoders (VAEs) deep dive
• Long Short-Term Memory (LSTM) networks

Unit V: Architecture Large Language Models (LLM)

• Overview of popular LLMs in the industry (e.g., GPT-3, BERT, T5).
• Applications in various domains
• Transformer Architecture

Unit VI: Different Open-Source Large Language Models

• Overview of Hugging Face Transformers
• Loading and using pre-trained models from Hugging Face

Unit VII: ChatGPT-4

• Overview of GPT-4 architecture and capabilities
• Use cases and applications.

Unit VIII: Text classification, Language Generation and Summarization

• LLMs in Text Classification
• Implementing LLMs for Text Classification
• Advantages of Using LLMs for Text Classification
• Challenges of Using LLMs for Text Classification
• Generating creative text using LLMs
• Example: Generating creative text using LLMs
• LLM to generate more coherent text

Unit IX: Retrieval-Augmented Generative models (RAGs)

• Understanding the concept of retrieval-augmented models
• Applications of RAGs in NLP tasks
• Implementing a simple RAG model for information retrieval
• Lab: Building a basic RAG model

Unit X: Image Generation, Diffusion Model, DALL·E, Bing

• Understanding Diffusion Models
• Applications of Diffusion Model
• DALL-E
• Bing’s Image Generation Techniques
• Bing’s Image Generation-Application
• Lab: Implementing an image generation model using diffusion

Prompt Engineering

Unit - I: Introduction to AI & Language Models

• Artificial Intelligence
• Basics of Machine Learning
• Introduction to Natural Language Processing
• Key Challenges in NLP
• Overview of Language Models
• Understanding Transformers 
• How Language Models Learn
• Applications of Language Models in the Real World
• Setting Up a Python Environment for NLP (LAB)

Unit - II: Foundation of Prompt Engineering

• What is Prompt Engineering
• Importance of Prompt Design in AI Interactions
• Types of NLP Learning in Prompting

a.) Zero Shot Learning - Theory, Examples and Analysis (LAB)

b.) One Shot Learning - Theory, Examples and Analysis (LAB)

c.) Few Shot Learning – Theory, Examples and Analysis (LAB)

• Understanding Model Responses to Prompts
• Best Practices in Prompt Construction
• LAB: - Hands-on: Crafting Effective Prompts for Different Scenarios

Unit - III: Prompting Techniques

1. Chain of Thoughts Prompting
   • • Fundamentals
     • Role
     • Effective Strategies
     • Benefits and Limitations
     • Practical Applications of Chain-of-Thought Prompting
     • Case Studies
     • LAB: -Hands-on: Developing a Chain of Thoughts Prompting System
     • Overcoming Challenges

1. Iterative Prompting
   • • Fundamentals
     • Role
     • Effective Strategies
     • Benefits and Limitations
     • Practical Applications of Iterative Prompting
     • Case Studies
     • LAB: -Hands-on: Developing an Iterative Prompting System
     • Overcoming Challenges

1. Negative Prompting
   • • Fundamentals
     • Role
     • Effective Strategies
     • Benefits and Limitations
     • Practical Applications of Negative Prompting
     • Case Studies
     • LAB: -Hands-on: Developing a Negative Prompting System
     • Overcoming Challenges

1. Hybrid Prompting
   • • Fundamentals
     • Role
     • Effective Strategies
     • Benefits and Limitations
     • Practical Applications of Hybrid Prompting
     • Case Studies
     • LAB: -Hands-on: Developing a Hybrid Prompting System
     • Overcoming Challenges

Unit - IV: Prompt Chaining

• The Concept of Prompt Chaining and Its Applications
• Designing Effective Prompt Chains for Sequential Tasks
• Understanding the Logic Flow in Prompt Chains
• Case Examples: Prompt Chaining for Data Processing
• Analysing Results and Feedback Loops in Prompt Chaining
• Building Your Own Prompt Chain: A Step-by-Step Guide
• LAB: - Hands-on: Implementing a Prompt Chaining System for a Chosen Domain
• Advanced Prompt Chaining Techniques
• LAB: - Troubleshooting and Refining Prompt Chains
• Future Directions in Prompt Chaining Technology

Unit - V: Advanced Prompting Techniques

• • Overview of Advanced Prompting Techniques
  • Meta-Prompting: Theory and Applications
  • Prompt Tuning and Fine-Tuning Language Models
  • Leveraging External Knowledge Bases in Prompting
  • Advanced Evaluation Metrics for Prompting Strategies
  • Practical Implementations of Advanced Techniques
  • LAB: -Hands-on: Meta-Prompting for Custom Use Cases
  • LAB: -Developing a Fine-Tuned Model for a Specific Task
  • Challenges and Solutions in Advanced Prompting

Unit - VI: Ethical Considerations in Prompt Engineering

• The Ethical Landscape of Prompt Engineering
• Bias, Fairness, and Transparency in AI Responses
• Privacy Concerns with Language Models
• Ethical Design and Implementation of Prompts
• Global Perspectives on AI Ethics
• Developing Ethical Guidelines for Prompt Engineering
• LAB: - Hands-on: Analysing and Mitigating Bias in AI Outputs
• Case Studies: Ethical Prompt Engineering in Practice
• Building Trustworthy AI Systems through Responsible Prompting

What does a Gen AI and Prompt Engineer do?

A Prompt engineer possesses a diverse set of skills and knowledge required to perform various tasks and responsibilities related to prompt engineering. With exposure to different GPT's, they are adept at designing effective prompts, optimizing model performance, troubleshooting, debugging, and more. Their expertise plays a crucial role in driving innovation, creating engaging conversational experiences, and optimizing the performance of language models. As a sought-after professional in the industry, a multi-platform prompt engineer leverages their expertise to deliver cutting-edge solutions with precision, speed, and efficiency.

What are the domains where Gen AI and Prompt Engineer work?

A prompt engineer works across various domains and industries that leverage natural language processing and AI technologies. Some of the common domains where a multi-platform prompt engineer may work include.

• • Chatbot Development
  • Virtual Assistants
  • Customer Support
  • Content Generation
  • AI Research
  • Language Model Fine-Tuning
  • Social Network, Media, and Communication
  • SaaS industry
  • E-Commerce and Finance

Top Job Roles of Gen AI and Prompt Engineer

• • AI Prompt Engineer
  • Conversational AI Developer
  • NLP Engineer
  • AI Solution Architect
  • AI Research Scientist
  • ChatGPT/Bard/Bing Engineer

Salary of a Gen AI and Prompt Engineer in India.

According to recent surveys and reports, the average annual pay package for a Gen AI and prompt engineer in India is around ₹16 lakhs per year. However, the salary may vary depending on factors such as years of experience, skills, location, and the organization you work for. An Entry level Gen AI and Prompt Engineer gets an offer of ₹5.6 lakhs per year. Senior professionals with a considerable amount of experience can earn up to ₹30 lakhs per annum. Some startups and large technology firms offer higher salaries to attract top talent for critical roles in conversational AI and NLP technologies.

Is it worth to pursue a career in Gen AI and Prompt Engineer?

It's worth noting that the field of Gen AI and Prompt Engineering is rapidly evolving, and demand for skilled engineers is expected to increase in the coming years. With this projected growth, salaries may continue to rise, making it a lucrative career choice.

Our program trains you with the skills to expertise in the machine learning from scratch and introduce you an advanced approach for effective Data analysis Data visualization and retrieval, empowering the deep understanding of machine learning models with respect to real time applications using classification, regression, clustering and network models. The advanced learning systems showcase intense impact of machine learning that suggest a jump into real world analysis with enormous numbers of datasets, embark on a journey that will skyrocket your proficiency and open doors to new opportunities in the tech and AI provinces.

Modules Included:

1. Supervised Machine Learning

• Introduction to Artificial intelligence
• Introduction to machine learning
• supervised and unsupervised machine learning
• algorithms for supervised machine learning
• K-Nearest Neighbor Algorithm
• Naïve Bayes Classification
• Decision Trees
• Random forest
• Support Vector Machines
• Linear Regression
• Logistic Regression
• Ridge Regression
• Lasso Regression
• Polynomial Regression
• Bayesian Linear Regression
• Lab-Implementation of K-Nearest Neighbor (K-NN) algorithm used for classification and regression.
• Lab-Implementation of Naïve Bayes Classification module employs Bayes' Theorem to predict the probability of different classes for a given input.
• Lab-Implementation of machine learning algorithm used for classification and regression tasks that model decisions and their possible consequences in a tree-like structure.
• Lab-Implementation of an ensemble learning method for constructing a multitude of decision trees during training and outputting the mode of the classes for classification or mean prediction for regression from the individual trees.
• Lab-Implementation of functionality to train and utilize a Support Vector Machine (SVM) model for classifying data points into two categories based on their features.
• Lab-Implementation of Linear regression to model the relationship between a dependent variable and one or more independent variables
• Lab-Implementation of Logistic regression for binary classification, modeling the probability of a binary outcome as a function of one or more predictor variables.
• Lab-Implementation of Ridge regression  in predicting the target variable.
• Lab-Implementation of Lasso regression for both prediction and feature selection in high-dimensional datasets.
• Lab-Implementation of Polynomial regression for higher-order polynomial functions to capture more complex patterns in the data
• Lab-Implementation of Bayesian Linear Regression models to estimate posterior distributions, enabling uncertainty quantification in predictions.

2. Unsupervised and Reinforcement Learning

• algorithms for unsupervised machine learning
• Explaining Clustering methods
• K-means clustering
• Hierarchical Clustering
• Anomaly detection
• recommender systems
• content based filtering
• principal component analysis
• Apriori Algorithm
• Reinforcement Learning
• Algorithms for reinforcement learning
• Q learning
• R learning
• TD learning
• Life cycle of Machine learning
• Benefits and Real-world Applications
• Lab-Implementation of K-means clustering an unsupervised machine learning algorithm used to partition a dataset into K distinct, non-overlapping clusters by minimizing the within-cluster variance.
• Lab-Implementation of Hierarchical Clustering a bottom-up approach where clusters are recursively merged or top-down where clusters are recursively split.
• Lab-Implementation of Anomaly detection aiding in detecting anomalies or deviations from expected behavior.
• Lab-Implementation of recommender systems employs algorithms like collaborative filtering and content-based filtering to generate accurate recommendations.
• Lab-Implementation of recommendation technique based on the attributes or content of the items.
• Lab-Implementation of PCA a dimensionality reduction technique enabling simplified data analysis and visualization.
• Lab-Implementation of Apriori algorithm a classic data mining technique for finding frequent itemsets in a transaction database and extracting association rules.
• Lab-Implementation of Q-learning a reinforcement learning technique that learns optimal actions by iteratively updating action-values
• Lab-Implementation of reinforcement learning, enabling users to develop and train agents to make sequential decisions in dynamic environments efficiently
• Lab-Implementation of reinforcement learning method bridging the gap between dynamic programming and Monte Carlo methods

3. Neural Networks and Advanced Learning

• Intuition of Neural Networks
• Neural network models
• Python for Neural networks
• Training Neural Networks
• Activation functions
• Backpropagation
• Applying machine learning and evaluation of a model
• Regularization and Bias/Variance
• Machine learning Development progress
• Decision tree learning
• Tree ensembling
• Lab-Implementation of "Training Neural Networks" for understanding Neural networks, covering their architecture, training process, and applications
• Lab-Implementation of Activation functions in neural networks to determine the output of a neuron.
• Lab-Implementation of Backpropagation computational algorithms to train neural networks enabling efficient learning of complex patterns and tasks.
• Lab-Implementation of machine learning techniques for emphasizing model evaluation for robust performance assessment
• Lab-Implementation of "Regularization and Bias/Variance" for foundational understanding and bias variance for overfitting and underfitting problems
• Lab-Implementation of neural network machine learning models using python programming language
• Lab-Implementation of Decision tree learning a machine learning technique to maximize information gain or minimize impurity at each node.
• Lab-Implementation of Tree ensembling computational algorithms such as Random Forest and Gradient Boosting for aggregating predictions

What does a Machine learning developer do?

A machine learning developer possesses a diverse set of skills and knowledge required to perform various tasks and responsibilities related to machine learning models and algorithms to solve problems on machine learning specific problems to enhance the capabilities of the system. With exposure to various machine learning, reinforcement learning algorithms, the developer can optimize model performance, troubleshooting, debugging, and more through various strategies for software development and data analytics etc… Their expertise plays a decisive role in driving innovation, able to practically optimize the projects to deliver a best version of a model with high efficiency

What are the domains where machine learning developer work?

A machine learning developer works across various domains and industries that leverage Machine learning and AI technologies. Some of the common domains where a machine learning developer may work include.

• • Virtual Assistants
  • Healthcare- diagnostic imaging, drug discovery
  • Finance sector
  • AI Research and development
  • Ecommerce recommendations systems and demand forecasting
  • Natural language Generation, Computer vision
  • Social Network, Media, and Communication
  • Industries that provide software as a service
  • Autonomous vehicles
  • Smart agriculture
  • Marketing -customer segmentation, churn prediction

Top Job Roles of machine learning developer 

• •  Machine learning architect
  • Machine learning Developer
  • Machine learning Engineer
  • AI Solution Architect
  • ML data Scientist
  • Machine learning research scientist

Salary of a Machine Learning Developer in India.

According to recent surveys and reports, the average annual pay package for a machine learning developer in India is around ₹9 lakhs per year. However, the salary may vary depending on factors such as years of experience, skills, location, and the organization you work for. An Entry level ML engineer gets an offer of ₹7 lakhs per year. Senior professionals with a considerable amount of experience can earn up to ₹36 lakhs per annum. Some startups and large technology firms offer higher salaries to attract top talent for critical roles in upgraded AI and ML technologies.

Is it worth to pursue a career in Machine Learning developer?

As the Artificial Intelligence and Machine Learning is rapidly evolving, and demand for ML developers are expected to increase in the coming years with respect to the projected growth, salaries for ML developer may continue to rise, making it a lucrative career choice.

Anyone can attend the course.

Unit-1: Python Programming
 

            Chapter I: - Introduction to Python
                               a.) Introduction

                               b.) Features

                               c.) Advantages

                               d.) Applications

                               e.) IDE

            Chapter II: - Basics of Python
                                a.) Tokens

                                b.) Variables

                                c.) Data Types

                                d.) Escape Sequence

                                e.) Comments

            Chapter III: - Control Flow Statements
                                 a.) Selection Flow

                                 b.) Loop Flow

                                 c.) Sequential Flow

            Chapter IV: - Python Data Structures
                                  a.) Indexing

                                  b.) Slicing

                                  c.) Strings

                                  d.) Lists

                                  e.) Tuples

                                  f.) Sets

                                  g.) Dictionary

            Chapter V: - Functions
                                 a.) User-Defined Functions

                                 b.) Built-In Functions

                                 c.) Variable Scope

                                 d.) Match-Case Functions

                                 e.) Lambda Functions

                                 f.) Recursion Functions

                                 g.) Modules

           Chapter VI: - Object Oriented Programming
                                 a.) OOP Fundamentals

                                 b.) Access Modifiers

                                 c.) Constructors

                                 d.) Encapsulation

                                 e.) Inheritance

                                 f.) Polymorphism

                                 g.) Abstraction

          
          Chapter VII: - File Handling
                                a.) Introduction

                                b.) Access Modes

                                c.) File Methods

                                d.) File Operations

         Chapter VIII: - Errors and Exceptions
                                a.) Errors

                                b.) Exceptions

                                c.) Built-in Exceptions

                                d.) User-Defined Exceptions

                                e.) Exception Handling

                                f.) Logging

          Chapter IX: - Miscellaneous Concepts
                                a.) Iterators

                                b.) Generators

                                c.) Decorators

                                d.) Closures

                                e.) Getters and Setters

                                f.) Output Formatting

Unit-2: Data Visualization 
          

           Chapter I: - Data Visualization Foundation
                              a.) Introduction

                              b.) Data Visualization Lifecycle

                              c.) Steps of Data Visualization

Chapter II: - NumPy 
                    a.) Introduction

                    b.) Datatypes

                    c.) Attributes

                    d.) Numerical Ranges

                    e.) Array Manipulation

                    f.) Broadcasting

                    g.) View & Copy

                    h.) Indexing & Slicing

                    i.) Linear Algebra

                    j.) Sorting

     
           Chapter III : - Pandas

                      a.) Introduction

                      b.) Data Structures

                      c.) Re-Shaping Data

                      d.) Grouping Data

                      e.) Subset Observations

                      f.) Subset Variables

                      g.) Logic Operations

                      h.) Summarization

                      i.) Join & Merge

                      j.) Missing & Tidy Data
 

Chapter IV: - Matplotlib
                     a.) Introduction

                     b.) Functions

                     c.) Colormaps

                     d.) Axes Class

                     e.) Subplots

                     f.) Plots

Chapter V: - Seaborn
                    a.) Introduction

                    b.) Relplots

                    c.) Distplots

                    d.) Catplots

Unit-3: Statistics

                       Chapter I: - Statistics Foundation

                                          a.) Introduction

                                          b.) Population & Sampling

                                          c.) Sampling Methods

                                          d.) Types of Data

                                          e.) Level of Measurement

                                          f.) Types of Variables

            Chapter II: - Data Cleansing

                                           a.) Introduction

                                           b.) Dirty Vs Clean Data

                                           c.) Features of Clean Data

                                           d.) Data Cleansing Process

                                           e.) Data Validation

                                           f.) Data Screening

                                           g.) Outliers

                       Chapter III: - Central Tendency
                                           a.) Introduction

                                           b.) Mean

                                           c.) Median

                                           d.) Mode

                        
                       Chapter IV: - Variability

                                             a.) Introduction

                                             b.) Range

                                             c.) Interquartile Range

                                             d.) Standard Deviation

                                             e.) Variance

                       Chapter V: - Frequency Distribution

                                            a.) Introduction

                                            b.) Skewness

                                            c.) Kurtosis

                                            d.) Types of FD

                                            e.) Grouped FD

                                            f.) Ungrouped FD

                                            g.) Relative FD

                                            h.) Cumulative FD

                                            i.) Relative Cumulative FD

                     
                       Chapter VI: - Probability Distribution

                                           a.) Introduction

                                           b.) Types of PD

                                           c.)  Discrete PD

                                           d.) Types of Discrete PD

                                           e.) Continuous PD

                                           f.) Types of Continuous PD

                                           g.) Central Limit Theorem

 

                   Chapter VII: - Estimations

                                          a.) Introduction

                                          b.) Point Estimates

                                          c.) Interval Estimates

                                          d.) Confidence Intervals

                                          e.) Confidence Level

                                          f.) Margin of Error

                                          g.) Standard Error

                                          h.) Z-score Vs T-score

                                          i.) Effect Size

                             
                   Chapter VIII: - Statistical Tests

                                          a.) Hypothesis Testing

                                          b.) Parametric Tests

                                          c.) Non-Parametric Tests

                                          d.) Correlation

                                          e.) Correlation Coefficient

                                          f.) Causation

      Unit-4: Machine Learning 
 

                   Chapter I: - ML Introduction

                                      a.) Introduction

                                      b.) ML Lifecycle

                                      c.) Types of ML

                                      d.) Supervised Learning

                                      e.) Unsupervised Learning

                                      f.) Reinforced Learning

       Chapter II: - Regression Analysis

                                      a.) Introduction

                                      b.) Linear Regression

                                      c.) Multiple Regression

                                      d.) Logistic Regression

                                      e.) Polynomial Regression

                                      f.) Non-Linear Regression

                                      g.) Lasso Regression

                                      h.) Ridge Regression

                 
                 Chapter III: - ML Key Concepts

                                      a.) Training Data

                                      b.) Testing Data

                                      c.) Validate Data

                                      d.) Hyperparameter Tuning

                                     e.) Variables

                                     f.) Encoding

                                     g.) Bias and Variance

                Chapter IV: - ML Monitoring

                                      a.) Precision

                                      b.) Accuracy

                                      c.) Recall

                                      d.) F1 Score

                                      e.) ROC and AUC

                                      f.) Confusion Matrix

                 Chapter V: - ML Algorithms

                                      a.) K-Means Clustering

                                      b.) Naïve Bayes Classifier

                                      c.) K Nearest Neighbour

                                      d.) Decision Tree

                                      e.) Random Forest

                                      f.) Support Vector Machine

                                      g.) Principal Component Analysis

                                      h.) Apriori Algorithm

    Unit-5: Deep Learning 
 

                 Chapter I: - Deep Learning Foundation

                                    a.) Introduction

                                    b.) BNN

                                    c.) Perceptron

                                    d.) Propagation

                                    e.) Cost Function

                                    f.) Gradient Descent

                 Chapter II: - Artificial Neural Network

                                    a.) Introduction

                                    b.) Neurons

                                    c.) Neuron Architecture

                                    d.) Activation Functions

                                    e.) Neural Networks

                 Chapter III: - Convolutional Neural Network

                                      a.) Introduction

                                      b.) CNN Architecture

                                      c.) Input Layer

                                      d.) Image Matrix

                                      e.) Convolutional Layer

                                      f.) Pooling

                                      g.) Output Layer

                 Chapter IV: - Recurrent Neural Network

                                      a.) Introduction

                                     b.) Types of RNN

                                     c.) Natural Language Toolkit (NLTK)

                                     d.) Long Short-Term Memory (LSTM)

                                     e.) Gated Recurrent Unit Networks (GRU)   

               Chapter V: - Generative Learning

                                    a.) Introduction

                                    b.) Autoencoders

                                    c.) GANS

                                    d.) Ensemble Learning

               Chapter VI: - Deep Learning Framework

                                    a.) Introduction

                                    b.) TensorFlow

                                    c.) Keras

                                    d.) PyTorch

What does Deep Learning do?

Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions. You can use deep learning methods to automate tasks that typically require human intelligence, such as describing images or transcribing a sound file into text. 

What are the domains where deep learning work?

Deep learning applications to bring us self-driving cars and virtual assistants like Alexa, Siri, and Google Assistant. But today, these creations are part of our everyday life. Deep Learning continues to fascinate us with its endless possibilities such as fraud detection and pixel restoration. Deep learning is an ever-growing industry, clearly and power ahead your career. Some of the common domains where deep learning may work include.

• • Self-Driving Cars
  • Fraud Detection
  • Virtual Assistance
  • Visual Recognition
  • Healthcare
  • Colourisation of Images
  • Automatic Machine Translation
  • Automatic Handwriting Generation
  • Pixel Restoration
  • Photo Descriptions
  • Election Predictions

Top Job Roles of Deep Learning

• • Data Scientist
  • Data Engineer
  • NLP Engineer
  • AI Solution Architect
  • Deep Learning Engineer
  • Automation Engineer

Salary of a Deep Learning Engineer in India.

According to recent surveys and reports, Deep Learning Engineer salary in India ranges between ₹ 3.0 Lakhs to ₹ 24.0 Lakhs with an average annual salary of ₹ 8.0 Lakhs. The starting pay for deep learning engineers in India can range anywhere between ₹3–15 LPA. Individuals who fall on the higher end of the salary scale may have advanced qualifications, prior work experience, or may work for top players in the industry. The salary may vary depending on factors such as years of experience, skills, location, and the organization you work for. Some startups and large technology firms offer higher salaries to attract top talent for critical roles in AI technologies.

Is it worth to pursue a career in Deep Learning?

Where machine learning algorithms generally need human correction when they get something wrong, deep learning algorithms can improve their outcomes through repetition, without human intervention. A machine learning algorithm can learn from relatively small sets of data, but a deep learning algorithm requires big data sets that might include diverse and unstructured data. Think of deep learning as an evolution of machine learning. Deep learning is a machine learning technique that layer’s algorithms and computing units—or neurons—into what is called an artificial neural network. These deep neural networks take inspiration from the structure of the human brain. Data passes through this web of interconnected algorithms in a non-linear fashion, much like how our brains process information. 

Anyone can attend the course.

Our program equips you with the skills to gain knowledge, practical skills, and a functional knowledge of how Generative AI works. This program describes in detail the transformer architecture that powers the LLM, and how fine-tuning enables LLMs to be adapted to a specific use case.

Modules Included:

1. Introduction to AI 

•  What is artificial Intelligence 
•  Building blocks of AI 
•  Type of AI 

2. Introduction to Machine Learning

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Dataset
• Data Preprocessing and Data Cleaning

3. Introduction to GenAI

•  Overview of GenAI
• Use Cases of GenAI
• LLM Introduction
• LLM Use Cases
• GenAI configuration Inference Parameters
• GenAI Project Lifecycle

4. LLM Pretraining, Scaling Laws and Fine tunning

• Pre-training LLM
• Computational Challenges
• Scaling Laws
• Single task and multi-task fine-tunning
•  PEFT techniques

5. Reinforcement Learning

• RLHF overview
• RLHF: feedback from human
• RLHF: Reward Model

What does a LLM Developer do?

A Large Language Model developer possesses a diverse set of skills and knowledge required to perform various tasks and responsibilities related to LLM Developer such as design, develop, optimize Large Language Models, NLP Systems, frameworks, and tools for both front-end and back-end development. Their expertise plays a crucial role in driving innovation, creating engaging conversational experiences, and optimizing the performance of language models. As a sought-after professional in the industry, a Large Language Developer leverages their expertise to deliver cutting-edge solutions with precision, speed, and efficiency.

What are the domains where LLM developer work?

A Large Language Model Developer works across various domains and industries that leverage natural language processing and AI technologies. Some of the common domains where a LLM Developer may work include.

• • Chatbot Development
  • Virtual Assistants
  • Customer Support
  • Content Generation
  • AI Research
  • Language Model Fine-Tuning
  • Social Network, Media, and Communication
  • SaaS industry
  • E-Commerce and Finance

Top Job Roles of LLM Developer

• • LLM Research Scientist
  • LLM Developer
  • LLM Data Scientist
  • LLM Machine Learning Engineer
  • LLM Business Analyst
  • LLM Solution Architect
  • LLM Consultant
  • LLM Technical Writer
  • LLM Evangelist

Salary of a LLM Developer in India.

According to recent surveys and reports, the average annual pay package for a LLM Developer in India is around ₹16 lakhs per year. However, the salary may vary depending on factors such as years of experience, skills, location, and the organization you work for. An Entry level LLM developer gets an offer of ₹5.6 lakhs per year. Senior professionals with a considerable amount of experience can earn up to ₹30 lakhs per annum. Some startups and large technology firms offer higher salaries to attract top talent for critical roles in conversational AI and NLP technologies.

Is it worth to pursue a career as LLM Developer?

It's worth noting that the field of Large Language Models is rapidly evolving, and demand for skilled engineers is expected to increase in the coming years. With this projected growth, salaries for Large Language Model Developers may continue to rise, making it a lucrative career choice.

Anyone can attend the course.

Our program trains the fundamental data structures like arrays, linked lists, trees, graphs, and hash tables, alongside algorithms for sorting, searching, and optimization. Special emphasis is placed on AI-specific techniques such as dynamic programming, heuristic search, and graph traversal. The course integrates theoretical knowledge with practical coding exercises, preparing students to solve complex AI problems efficiently. By the end, learners will be equipped with the skills to implement and optimize algorithms crucial for advanced AI development.

Modules Included:

1. Introduction to Data Structures and Algorithms       

1. Overview of data structures and algorithms in AI.         
2. Importance of efficiency and complexity analysis.      
3. Notion of Time and Space Complexity

2. Utilizing Data Structures for AI Problem Solving       

1. Applying DSA concepts to solve AI problems.
2. Machine Learning- Decision Trees and Linear Algebra Operations      
3. Deep Learning- Neural Networks, Graph Neural Networks      
4. Natural Language Processing-Tries, Dynamic Programming   
5. Search Algorithms-Graph Traversal, Heirostic Search
6. Optimization Problems- Genetic Algorithms, Linear Programming      
7. Robotics and Perception-Spatial Data Structures, Convolutional Neural Networks    
8. Recommender Systems- Matrix Factorization, Graph Databases         
9. Anomaly Detection-Clustering Algorithms      

3. Linear Data Structures          

1. Arrays 
2. Linked Lists      
3. Stacks 
4. Queues              
5. Lab- Implementation of Anomaly Detection using Array/Linked Lists/Graphs in Clustering Algorithms
6. Lab- Execution of Tree and Array Data Structures in Decision Tree Classifier

4. Non-Linear Data Structures

1. Trees   
2. Graphs                
3. Hash tables
4.  Lab-Execution of Recommender Systems using Graph data structures        Large Language Models
5. Lab-Neural networks: understanding the data structures behind ANNs, CNNs, and RNNs.

5.  Advanced Data Structures  

1. Heaps 
2. priority queues
3. Red Black Trees             
4. B Trees,B+ Trees            
5. Prefix Trees(Tries)         
6. Lab-Execution of Heuristic Search Algorithms (A* search/Hill Climbing) using Priority Queue data structure
7. Lab-Implementation of Storing Dictionaries in NLP using Prefix Trees

6. Basic Sorting and Searching Algorithms       

1. Sorting algorithms: Quick sort, merge sort ,heap sort 
2. Uninformed Searching algorithms: Binary search, depth-first search (DFS), breadth-first search (BFS)               
3. Informed Searching algorithms: A* Search, Heuristic Search, Hill Climbing   
4. Lab-Solving Maze Problem using DFS/BFS
5. Lab-Web Crawling /Finding Shortest Path problem using BFS

What does an AI /ML Engineer or AI software Architect do?

An AI /ML Engineer or AI software Architect possesses a diverse set of skills and knowledge required to perform various tasks and responsibilities related to AI based Data Structures and algorithms for meaningful data management and retrieval. With exposure to data structures integrated with AI and ML, an AI/ML Engineer or AI Software Architect applies data structures and algorithms to create powerful, scalable AI systems. Utilizing structures like trees and graphs, they manage data efficiently. Algorithms such as dynamic programming and search strategies drive AI methods, including neural networks and decision trees. This knowledge ensures they can optimize performance, build resilient models, and address intricate AI challenges.

What are the domains where an AI /ML Engineer or AI software Architect work?

An AI /ML Engineer or AI software Architect works across various domains and industries that leverage software development and AI based application development. Some of the common domains where an AI /ML Engineer or AI software Architect may work include.

• • Virtual Assistants
  • Customer Support bots
  • AI Research and development
  • Large Language Model Fine-Tuning
  • Natural language Generation
  • Social Network, Media, and Communication
  • Industries that provide software as a service
  • E-Commerce and Finance

Top Job Roles of Data Structures and Algorithms for AI course

• • AI/ML Engineer

  • Data Scientist
  • AI software Architect
  • AI Research Scientist
  • NLP Engineer
  • Computer Vision Engineer
  • Big Data Engineer

Salary of an AI /ML Engineer or AI software Architect in India.

According to recent surveys and reports, the average annual pay package for an AI /ML Engineer or AI software Architect in India is around ₹12 lakhs per year. However, the salary may vary depending on factors such as years of experience, skills, location, and the organization you work for. An Entry level Semantic LLM gets an offer of ₹7 lakhs per year. Senior professionals with a considerable amount of experience can earn up to ₹36 lakhs per annum. Some startups and large technology firms offer higher salaries to attract top talent for critical roles in Software Architecture and Application Development.

Is it worth to pursue a career in Data Structures and Algorithms for AI?

Pursuing a career in Data Structures and Algorithms for AI is highly valuable. These foundational skills are crucial for developing efficient, scalable AI solutions, optimizing performance, and solving complex problems. Mastery in these areas open up opportunities in various AI fields, making you a sought-after professional in the rapidly growing AI industry.