Generative AI and Deep Learning Course | Certification

About Us

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About the Program

Our Advanced Certification Program in Generative AI & Deep Learning will make you a pioneer in crafting intelligent, creative, and autonomous systems, propelling you to the limelight of the AI revolution, although your existing technical expertise. This exhaustive program takes you beyond basic concepts into cutting-edge architectures and their proficient applications. You'll gain hands-on mastery with differing advanced tools and methods, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Transformer architectures (like LLMs), and prompt engineering, through severe projects and under the guidance of seasoned AI researchers and manufacturing specialists. This program is created for those enthusiastic to innovate and implement the next generation of AI solutions.

Practice as You Learn

Our curriculum is made specifically for practical exposure as it includes real-life problems, strictly making sure that what you study is directly aligned

Learn from Expert Professionals

Every session is conducted by well-experienced mentors who’ve built successful careers by working in the field and understand the real work

Interactive Learning with Practical Work

There has always been a lack of practical exposure in education. Here, we address it and make sure you learn practically at every step

Flexible Learning for Busy Schedules

No time to study at a fixed hour? We understand. Our live classes happen on weekends, with the option to choose either a morning or evening batch.

Connect with Experts and Advance Your Career

Digicrome supports and encourages students to connect with industry experts via webinars, Q&A sessions, and networking events.

Placement-Focused Training

We put your placement first by giving you marketable skills. We guarantee that you are not only studying but also completely equipped

Lifetime Access to Learning Materials

Once you're part of the course, you don't have to worry about missing anything or forgetting concepts. Digicrome offers lifetime course access

Industry Projects for Practical Learning

Job roles become easier when you understand the flow of tasks, and you're more likely to get hired when you have a portfolio.

Earn with Skills – Freelance Ready

Want to work in the tech field on your terms? We provide training that helps you market your skills, find the right clients, and manage projects

Course Overview

In the era of artificial intelligence, Generative AI and Deep Learning are reshaping industries, fueling innovation, and enabling revolutionary breakthroughs. The Professional Certification Program in Generative AI & Deep Learning is tailored to empower professionals and enthusiasts with cutting-edge expertise to excel in these transformative domains.

Why Pursue a Professional Certification in Generative AI & Deep Learning?

Generative AI, combined with the power of Deep Learning, is revolutionizing fields like healthcare, finance, entertainment, and autonomous systems. This certification program offers a well-structured and practical learning path, equipping you with the tools and knowledge to master advanced AI models, such as Generative Adversarial Networks (GANs) and Transformer-based architectures like GPT and BERT.

Program Highlights of the Professional Certification Program in Generative AI & Deep Learning

1. Comprehensive Curriculum

This program covers foundational and advanced concepts, including:

Deep Learning Fundamentals: Neural networks, backpropagation, and optimization techniques
Generative AI Models: GANs, Variational Autoencoders (VAEs), and Diffusion Models
Transformer Architectures: GPT, BERT, and applications in NLP
Reinforcement Learning: Integration with generative models
AI Ethics: Responsible AI practices and bias mitigation
Real-World Applications: Implementing generative AI in image generation, natural language processing, and beyond

2. Learn from Industry Experts

Gain insights from leading AI researchers and industry professionals:

Live interactive classes led by experienced instructors
Hands-on workshops using advanced tools and libraries like TensorFlow, PyTorch, and Hugging Face
Continuous mentorship from domain experts

3. Practical, Hands-On Learning

Apply your knowledge with real-world projects and assignments:

Work on capstone projects, such as building chatbots, creating deepfake detectors, or generating synthetic datasets
Develop deployable AI models with end-to-end pipelines
Receive personalized feedback to refine your skills

4. Networking & Collaboration

Collaborate with a diverse community of learners, mentors, and industry leaders
Participate in virtual hackathons and innovation challenges
Build lasting professional connections in the AI ecosystem

5. Flexible Learning Support

1:1 Personalized Doubt Clearing through video calls, email, and chat
Self-paced modules to accommodate your schedule
Dedicated program managers for continuous guidance

6. Globally Recognized Certification

Earn a Professional Certification in Generative AI & Deep Learning backed by industry leaders. Showcase your expertise to employers worldwide and stay competitive in the ever-evolving AI job market.

Why Choose This Program?

Learn the latest advancements in Generative AI and Deep Learning
Gain job-ready skills with practical, project-based learning
Access lifetime career support, including resume building, interview preparation, and placement assistance
Unlock career opportunities in high-demand fields such as AI research, robotics, gaming, and more

Take the Leap into the Future of AI

Empower yourself with advanced skills in Generative AI and Deep Learning and lead innovation in your field. Join Digicrome’s Professional Certification Program to build a career at the forefront of artificial intelligence.

📌 Apply Now to transform your career and shape the future of AI. Don't miss this opportunity to stay ahead in the rapidly evolving world of technology!

Digicrome has meticulously crafted this Job-Ready Certification Program to give your career the boost it deserves. With a focus on practical learning, industry-recognized credentials, and real-world applications, this program is designed to equip you with the skills and confidence to achieve unparalleled career growth.

Course Includes :

Price
172,500 + GST
US Price
$1999.00
Dubai Price
7352.00AED
Certifications
Yes
Language
English (US)

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Our Course Curriculum

100% Trusted And Golden Opportunities With Key Features That will Help You To Transform Your Career

1.1 What is ML, Why ML, Types of ML, (Training, Validation, and Testing Set)

1.2 Train/Test Split, Preprocessing of Data (LabelEncoder, OneHotEncoder), Standardization of Data

1.3 Hyperparameters, Selection and Fine Tuning of Models, (Main Challenges - Overfitting, Underfitting, Poor Quality Data, Irrelavant Features, etc.)

2.1 Descriptive Statistics - Estimates of Location (Mean, Weighted Mean, Trimmed Mean, Median, Weighted Median, Mode, Outliers), Estimates of Variability (Deviations, Variance, Standard Deviation, Mean Absolute Deviation, Median Absolute Deviation, Range, Percentiles, Quantiles, Deciles, Interquartile Range, Degrees of Freedom), Skewness and Kurtosis

2.2 Sampling Techniques - Bias Sample, Population, Random Sampling, Stratified Sampling, Simple Random Sampling, Bootstrap, Resampling

2.3 Inferential Statistics - Confidence Intervals, Normal Distribution (Z-score, QQ-Plot), T-Distrubtion and T-test, Binomial Distribution, Chi-Square Distribution and Chi-Square Test, F-Distribution, F-test, ANOVA Test, Poisson Distribution, Exponential Distribution, Weibull Distribution

2.4 Correlation Coffecient, Coefficient of Determination, Simple Linear Regression in Statistics

3.1 Performance Metrics - Accuracy, Recall, Precision, F1 Score, Confusion Matrix, Classification Report, Precision/Recall Tradeoff, ROC Curve, AOC Curve

3.2 Classification Models - Gradient Descent and Stochastic Gradient Descent, Logistic Regression, K Nearest Neighbors (KNN), Naive Bayes, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Decission Trees

3.3 Ensembling Methods - Bagging (Voting Classifer, Cross Validation, etc.), Boosting (XG Boost, Adaboost, etc.), Random Forest Classifer, Stacking

3.4 Advanced Techniques - Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, Multilabel Classification, L1 and L2 Regularization for overfitting, Handling Class Imabalance

3.5 Classification Project - Real World Use Case

4.1 Introduction - Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Cost Function and Gradient Descent

4.2 Performance Metrics - Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, etc.

4.3 Challenges - Heteroskedasticity, Non - Normality of Data, Multicollinearity of Data, etc.

4.4 Regression Models - Decision Tree Regressor, Support Vector Machine (SVM), K Nearest Neighbors (KNN)

4.5 Ensemble Models - Cross Validation, Voting Classifier, Random Forest, Bagging and Boosting Methods

4.6 Advanced Techniques - Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, L1 and L2 Regularization

4.7 Regression Project - Real World Use Case

4.1 Introduction to Unsupervised Learning

4.2 Clustering Methods - KMeans, Hierarchical, Model Based Clustering, DBSCAN Clustering, Anamoly Detection using Gaussian Mixture Models

4.3 Dimensionality Reduction using Principal Component Analysis

4.4 Building and Working of Recommendation Engines

Basic Concepts

1.1 Biological to Artificial Neurons

1.2 The perceptron

1.3 Multi-layer Perceptrons (MLPs)

1.4 Input Layer, Hidden Layers and Output layers

1.5 Weights and Biases

1.6 Regression MLPs

1.7 Classification MLPs

1.8 Activation functions and Optimizers

2.1 Building a Neural Network using Sequential API

2.2 Building a Neural Network using Functional API

2.3 Building a Neural Network using Subclassing API

2.4 Saving and Restoring a Model

2.5 Callbacks

3.1 Vanishing/Exploding Gradients Problem

3.2 Batch Normalization 3.3 Gradient Clipping

3.4 Transfer Learning - Using Pretrained Layers

3.5 Pretraining on Auxiliary Task

3.6 Faster Optimizers - RMSprop, AdaGrad, Adam, Nadam, Nesterov Accelerated Gradient

3.7 Learning Rate Scheduling

4.1 How to choose number of hidden layers and number of Neurons

4.2 Learning Rate, Optimizer, Batch Size, Loss Functions and Activation Functions

4.3 L1 and L2 Regularization

4.4 Dropouts and Batch Normalization

4.5 Max Norm Regularization

Convolutional Neural Networks (CNNs)

1.1 Structure - How CNNs are different from Traditional Neural Networks

1.2 Building Blocks - Filters, Kernals, Feature Maps, Pooling (Max, Average, Global), Padding (Valid vs Same)

1.3 Architectural Designs for Generative AI - Transposed Convolutions, Unsampling Techniques, Residual Connections (Skip Connections)

1.4 Types of CNNs in Generative AI - Encoder-Decoder, UNet, VGG and ResNet Variants, Dilated Convolutions, MultiScale Convolutions, Attention Mechanisms, Conditional CNNs

1.5 Relevance - High Resolution Image Generation, Image Synthesis, Texture Synthesis, Video Generation

2.1 Core Concepts - Hidden State, Back Propagation through time, Challenges (Vanishing/Exploding Gradients, Short term Memory)

2.2 Basic Architectures (Simple and Deep RNNs), Advanced Architectures (Long Short-Term Memory, Gated Recurrent Units), Bidirectional RNNs, Sequence to Sequence Models)

2.3 RNN Variants for Generative AI - Attention Mechanisms in RNNs, Conditional RNNs, Hierarchical RNNs)

2.4 Incorporate Transformers, Hybrid Models (Combination of RNNs with CNNs and Attention Mechanisms for Generative AI)

2.5 Applications - Text Generation, Music Compostion, Speech Synthesis, Video Generation, Language Translation

3.1 Architecture - Encoder/Decoder Structure, Self Attention Mechanism, Positional Encoding, Residual Connections, Training of Transformer Models

3.2 Variants of Transformers - Encoder Only, Decoder Only, Encoder-Decoder, Vision Transformers, Multimodel Transformers, Efficient Transformers

3.3 Attention Mechanisms - Soft, Hard, Sparse, and Cross Attention Mechanisms

3.4 Fine Tuning and Transfer Learning - Prompt Engineering, Few-shot and Zero-shot Learning, LoRA (Low Rank Adaptation)

3.5 Transformer Models for Text Generation - BERT, GPT (2,3,4), BART, CLIP

3.6 Relevance for Generative AI - Autoregressive Modelling, Masked Language Modelling, Sequence to Sequence Models, Reinforcement Learning with Human Feedback (RLHF)

4.1 Relevance for Generative AI - Dimentionality Reduction, Data Denoising, Anomaly Detection, Image Generation, Feature Extraction, Latent Space Manipulation, Data Generation

4.2 Training of Autoencoders, Architecture - Encoder, Decoder and Latent Space (Bottleneck)

4.3 Types of Autoencoders - Vanilla Autoencoders, Denoising Autoencoders, Sparse Autoencoders, Convolutional Autoencoders, Variational Autoencoders, Contractive Autoencoders, Stacked Autoencoders, Adversarial Autoencoders

4.4 Advance Architectures - Beta-VAE, Conditional Autoencoder, Seuqence to Sequence Autoencoder, and Graph Autoencoder

5.1 Applications of GANs in Generative AI - Image Generation, Video Generation, Text to Image Synthesis, Music and Audio Generation, Style Transfer

5.2 Architecture - Generator, Discriminator, Adversarial Loss

5.3 Types of GANs - Vanilla GANs, Deep Convolutional GANs, Conditional GANs, Wasserstein GANs, Progressive Growing GANs, Cycle GANs, Style GANs, BigGANs, Pix2Pix.

5.4 Challenges - Mode Collapse, Non-Convergence, Vanishing Gradients

5.5 Advance Concepts (Attention GANs, 3D GANs, Speech GANs, Multi-Model GANs), Metrics (Inception Score, Fréchet Inception Distance, Perceptual Path Length)

5.6 Metrics - Inception Score, Fréchet Inception Distance, Perceptual Path Length

5.7 Key differences between Autoencoders and General Adversarial Networks

Objective

1.1 Answering complex user queries using Retrieval-Augmented Generation (RAG).

1.2 Generating high-quality images using prompt.

1.4 Deploying the application for real-world use.

2.1 Text Query Answering Module (using RAG with Transformers).

2.2 Creative Writing Module (text generation using GPT or custom Transformer models).

2.3 Image Generation Module (using Diffusion Models like Stable Diffusion or DALL·E).

2.4 Unified Frontend Interface for multi-modal interaction.

2.5 Backend API for serving models.

2.6 Deployment: Cloud-based or on-premise

3.1 Backend - FastAPI, Flask, Hugging Face Transformers, PyTorch, Tensorflow, OpenCV, Lancedb for Vector Search

3.2 Frontend - Gradio, Streamlit

3.3 Deployment - AWS, GCP, Azure, Docker, Kubernetes

4.1 A fully functional multi-modal AI assistant with text and image generation capabilities.

4.2 A deployed system accessible via a web interface.

4.3 A scalable architecture ready for real-world applications.

5.1 Mastery of Retrieval-Augmented Generation (RAG) for text generation.

5.2 Hands-on experience with text-to-image generation.

5.3 Ability to fine-tune transformer models for creative writing and specific tasks.

5.4 Development of full-stack AI applications with backend and frontend integration.

5.5 Deployment of models using Docker and cloud platforms.

5.6 Knowledge of scalable AI systems with Kubernetes.

5.7 Practical experience in data preprocessing for text and image tasks.

5.8 Use of evaluation metrics for assessing generative models.

5.9 Documentation of systems and API integration for real-world applications.

5.10 Exposure to AI ethics, deployment best practices, and model security.

Sample Projects You'll Build

Get hands-on experience with real-world inspired projects. These are some examples of what you'll build during the course.

AI-Powered Resume Analyzer for Recruiters

Develop a smart recruitment assistant that reads resumes, extracts key skills, compares them to job descriptions, and ranks the candidates automatically. The system uses LLMs to also generate professional summaries of top applicants, helping HR teams reduce screening time by over 60%.
Skills Required: Python, NLP, GPT-4/OpenAI API, Named Entity Recognition (NER), Streamlit, Prompt Engineering

Sales Forecasting with Time Series and AI Narratives

Build an end-to-end forecasting system for retail companies using historical sales data. The model not only predicts upcoming sales trends but also creates executive-friendly reports in natural language using GenAI—perfect for C-suite dashboards.
Skills Required: Time Series Modeling (ARIMA/Prophet), Pandas, Matplotlib, GPT-3/4 APIs, Data Wrangling, Prompt Crafting

Healthcare Chatbot for Symptom Analysis

Create a conversational AI tool that interacts with users to understand their symptoms and provides probable diagnosis suggestions based on verified medical datasets. It uses GenAI to explain the conditions in simple language and recommend doctor visits when necessary.
Skills Required: NLP, Transformers, Dialog Management, Hugging Face, Streamlit, LangChain, LLM Integration

AI-based Stock Market News Summarizer & Analyzer

Scrape real-time financial news and use sentiment analysis to evaluate the impact on stock performance. Then, use Generative AI to convert raw data into clean, digestible investment insights, summaries, and risk alerts for traders.
Skills Required: Web Scraping (BeautifulSoup/Selenium), Sentiment Analysis, Text Summarization, LangChain, GPT APIs, Python

Personalized Movie Recommendation System with Generative Reviews

Build a movie recommendation engine using collaborative filtering that not only suggests films based on user behavior but also generates custom reviews and movie summaries using LLMs to improve user engagement.
Skills Required: Recommendation Algorithms, Machine Learning, Matrix Factorization, Python, OpenAI API, Data Handling

Fake News Detector with AI-Generated Explanations

Train a classification model to detect fake news articles from real ones, and use Generative AI to explain why a particular article was flagged, breaking down language patterns, sources, and credibility metrics.
Skills Required: NLP, Supervised Learning, Scikit-learn, GPT-4 for Explanations, Tokenization, LangChain

E-Commerce Product Image Caption Generator

Combine computer vision and GenAI to generate detailed product descriptions from images, helping e-commerce businesses automate their product listing processes. The AI understands product types, styles, and features from images and writes marketing-friendly captions.
Skills Required: Deep Learning (CNNs), Vision-Language Models (BLIP, CLIP), Transformers, Image Preprocessing, Python

Smart City Traffic Flow Predictor with AI Reporting

Use IoT sensor data to analyze and predict peak traffic hours in metropolitan cities. The project integrates dashboards and uses GenAI to auto-generate policy recommendations, reducing congestion and improving route planning.
Skills Required: Time Series Analysis, Feature Engineering, Data Visualization (Plotly/Dash), GPT Reporting, Pandas

Customer Churn Prediction with LLM-Generated Recommendations

Build a model that predicts which customers are most likely to leave a service or subscription. Then, use LLMs to generate actionable, segment-specific retention strategies such as discounts, loyalty points, or personalized messages.
Skills Required: Classification Models, Logistic Regression, Data Cleaning, GPT-4 Integration, Customer Analytics

10. GenAI-Powered Legal Document Summarizer

Tackle the complexity of legal documents by developing a tool that extracts clauses, highlights key points, and summarizes entire contracts into human-readable formats—ideal for law firms, startups, and compliance teams.
Skills Required: Text Parsing, NLP, Transformers, LLM Summarization (LLM Chains), Domain-Specific Prompting, LangChain

TESTIMONIALS

What Students Say About
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Students love the hands-on learning, expert mentors, and real-world projects that make the Digicrome experience truly exceptional.

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Best Platform

“A very well-structured and well-balanced program content that you won't find in other programs or certification programs. Being a beginner in DS, I found the structure of the Executive PG Programme from Digicrome most helpful. They even teach you most of the prerequisites with prep sessions before you even start the course.

Vikas Kumar

AI Engineer

Nancy Chaudhary

“Everybody should be focusing on growing their business online right now. At DigiCrome, you will get to learn how maximum ROI, how to grow your business online, convert to sales and all that. , The best part about DigiCrome is its faculty who have previously worked in a renowned company like Google.

Nancy Chaudhary

NLP Engineer

Best Data Science Institute

“These guys are talented, incredible, and reliable. They always go above and beyond for us. Their work is fantastic, and they make a great team together. Highly recommend

Kushal Sharma

ML Engineer

Best Learning Platform

“Truly professional in my experience. I mean the whole team is keen to provide quality service and support. Highly recommended and trustworthy.

Hitender Solanki

AI Developer

Best Data Science Course

“Digicrome is a platform that provides professional assistance for your career development and a friendly environment when working with seniors as well. When it is about the counselling, it ensures to provide the correct and a successful path for the students

Priya Bala

Data Analyst

Best Institute for Data Science

“Awesome. Team is very talented and extremely helpful. Quality knowledge is provided. Recommended for anyone who is interested to make career in this field.

Chetan Kaushik

Data Scientist

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By Joining Our Program, Underlying Are The Key Featuers You Will Get

Get Lifetime Access to LMS

Your growth doesn’t end with the course—and neither does your access. With lifetime entry to our Learning Management System, you can revisit lessons, sharpen your skills, and stay up to date whenever you need. Learn at your pace, on your terms—because real learning is lifelong.

Live Interactive Online Sessions

" Make your weekends count with live sessions guided by experienced industry professionals. These aren’t just classes—they’re your space to ask, discuss, and truly understand. Connect with peers, solve real-world challenges together, and build confidence that lasts beyond the classroom. All scheduled keeping your time and growth in mind.

Regular Evaluations for better learning

We don’t just track your progress—we walk with you every step of the way. Through timely check-ins, constructive feedback, and personalised support, we help you understand what’s working and where to improve. It’s not about marks, it’s about momentum—so you keep moving forward with clarity and confidence.

Personalized Doubt Sessions

Every learner has unique challenges—and that’s why we offer one-on-one mentor support tailored just for you. From untangling tough topics to guiding your next steps, our mentors are here to listen, support, and keep you moving forward. Because real learning happens when someone’s genuinely there for you.

Hands-On Projects & Case Studies

Whichever path you choose—be it Data Science, AI, or Analytics—you won’t just study concepts, you’ll apply them. Through practical projects inspired by real industry scenarios, you’ll build the skills and confidence to solve challenges that companies face every day. It’s not just learning—it’s preparing for what comes next.

Focused Learning Tracks

Your career journey is unique—and your learning should reflect that. Choose a course that matches your goals, and dive deep into the skills that matter most in your domain. Whether you're drawn to tech, business, or the creative world, you'll gain focused expertise that sets you up for success, your way

Interview Preparation

We don’t just help you learn—we help you land the job. With personalised career guidance and realistic mock interviews, you’ll get the support you need to present your strengths, handle tough questions, and walk into every interview prepared and self-assured. Because your success is our goal, right from day one.

Our FAQs

Imperative FAQs About Us!

Most AI and ML courses stick to the basics or theory. This course goes further with complete project deployment and a capstone project that gives you experience like building a real product. You gain hands-on experience in transformers, GANs, and autoencoders. If your target is product or research roles, this is for you.

This course can significantly help you if you are making your transition to AI-specific roles. It will train you in tools like TensorFlow, PyTorch, and HuggingFace, along with Cloud Deployment. These are some of the key needs for an AI engineer or MLOps job

Yes, the final project includes building a multi-modal AI assistant. You will use transformers, diffusion models, and APIs, structured exactly like popular AI tools such as ChatGPT or image generators like DALL·E, giving real product-building experience.

You can be a generative AI specialist after completing this course, as the curriculum includes topics often asked for by top companies. It covers broad areas like RAG transformers, RLHF, LoRa, GANs, and multi-modal systems, a must for industry roles.

This course focuses on both practice and theory. It makes sure you learn completely hands-on and use the relevant industry tools and technologies. The course includes 48+ live sessions with every topic from classification to transformers and more.

A wide range of opportunities will open up for you as you complete the course. This includes AI Engineer, Generative AI Developer, and Data Scientist. You become eligible for over 30 job profiles, including roles in research and development.

Yes, the course is designed for every level of learner, be it a beginner or an intermediate. It starts from the foundations and by the 6th month grows to the expert level. Many non-tech learners have also made easy transitions to full-time AI engineering roles.

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Advanced Certification Program in Generative AI & Deep Learning

What Our Program Offers?

Trusted by world's best Organisations

About the Program

Practice as You Learn

Learn from Expert Professionals

Interactive Learning with Practical Work

Flexible Learning for Busy Schedules

Connect with Experts and Advance Your Career

Placement-Focused Training

Lifetime Access to Learning Materials

Industry Projects for Practical Learning

Earn with Skills – Freelance Ready

Course Overview

Course Includes :

Share Now

Our Course Curriculum

Introduction to Machine Learning

Statistics for Machine Learning

Classification Techniques

Regression Techniques

Unsupervised Learning

Introduction to Neural Networks

mplementation using Tensorflow and Keras

Training Deep Neural Networks

Fine Tuning Models

Types of Networks for Generative AI

Recurrent Neural Networks (RNNs)

Transformer Models

Autoencoders

General Adversarial Networks (GANs)

Capstone Project - Multi-Modal AI Assistant (MMAI)

Project Deliverables

Technologies Used

Expected Outcomes

Learning Outcomes

Languages and Tools Covered

Sample Projects You'll Build

AI-Powered Resume Analyzer for Recruiters

Sales Forecasting with Time Series and AI Narratives

Healthcare Chatbot for Symptom Analysis

AI-based Stock Market News Summarizer & Analyzer

Personalized Movie Recommendation System with Generative Reviews

Fake News Detector with AI-Generated Explanations

E-Commerce Product Image Caption Generator

Smart City Traffic Flow Predictor with AI Reporting

Customer Churn Prediction with LLM-Generated Recommendations

10. GenAI-Powered Legal Document Summarizer

Trusted by millions of learners around the Globe

Moments of Honour

Our Placed Learners In Different Big Firms

Mohd. Jubair Khan

NAMAN SHARMA

Abhinav Tiwari

Raushan Kumar

Santosh Singh

Mansi Dwivedi

Asha Sandeep

Avantika Verma

Shivani Kiran

Manohar Ramchandra

Mukesh Ghodeswar

Ritika Sadhwani

Tanuja Sharma

Sanchita Biswas

M YashWanth Kumar

Bhavesh Baghele

Ranjan Adhav

What Students Say About
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