55 Hours
Intensive Hands-On Training
Deep Learning • NLP • GenAI
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Become industry-ready with Neural Networks, CNNs, RNNs/LSTMs, Transformers, Prompt Engineering, fine-tuning LLMs, embeddings and vector databases. Build portfolio projects and earn a QR-verified certificate.
Curriculum includes data pipelines, tokenization, evaluation (BLEU, ROUGE, accuracy, F1), guardrails, lightweight MLOps, and deployment.
AI is transforming industries—be at the forefront with CDPL’s Hero Program. Build real projects in Computer Vision, NLP, and GenAI using Python, PyTorch, Transformers, and LLMs.
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Intensive Hands-On Training
Practical : Theory
Years of Expertise
Job Assistance
Doubt Solving
Global Certificates
Learn with mentor-led guidance, project-first pedagogy, and career support that aligns with industry hiring for AI/ML roles.
*Outcomes vary by prior experience, pace, and project depth.
An advanced, practical program to design, train, fine-tune, and deploy modern AI models— including Transformers, LLMs, and generative pipelines. Get industry-ready with hands-on projects, portfolio artifacts, and real deployment workflows.
Build CNNs, RNNs, and Transformers from scratch and with modern libraries. Understand optimization, regularization, and inference.
Apply prompt engineering, adapters (LoRA/QLoRA), and RAG to production use cases without overspending on compute.
Tokenization, embeddings, vector search, evaluation, and monitoring that stands up in real apps and APIs.
Package models with FastAPI, containerize with Docker, and ship to cloud with CI/CD and observability best practices.
Mitigate bias, implement guardrails, and comply with responsible AI guidelines for enterprise use.
Version datasets & models, track experiments, and automate training pipelines for repeatable results.
From neural network fundamentals to state-of-the-art NLP and generative AI, this course ensures you can ship working AI systems. You will implement tokenization & embeddings, construct and fine-tune Transformer/LLM architectures, and wire up RAG pipelines with vector databases. You’ll package models with FastAPI, containerize with Docker, and deploy to cloud with CI/CD while tracking experiments and monitoring performance.
Keywords: Deep Learning course, NLP course, Generative AI with Python, Transformer models, Large Language Models, LLM fine-tuning, RAG, vector databases, MLOps, AI deployment, prompt engineering.
An industry-aligned path from deep learning foundations to deploying Transformer-based applications and GenAI workflows.
Neural nets, activation functions, backprop, initialization, regularization. Build intuition with simple MLPs.
Data pipelines: cleaning, tokenization, vectorization, augmentations; datasets/dataloaders; efficiency tips.
CNNs for vision, RNN/LSTM/GRU, attention, Transformers; training loops, schedulers, checkpoints.
Losses, metrics, hyperparameter tuning, early stopping, mixed precision, error analysis, bias & robustness checks.
Export, ONNX/TorchScript, lightweight APIs, RAG with vector DBs, prompt engineering, safety/guardrails.
*Module order may vary based on cohort needs and instructor discretion.
Apply Deep Learning, NLP, and Generative AI to solve real problems. Build a job-ready portfolio with clean code, clear storytelling, and measurable impact.
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Classify customer sentiment from real-world reviews; surface topics and pain points for product teams.
Train and evaluate a CNN for multi-class image classification; deploy a lightweight inference API.
Fine-tune an open LLM for style-controlled generation and safe prompts with evaluation suites.
Forecast weekly sales; compare classical and ML approaches with robust backtesting and error analysis.
Create actionable cohorts for marketing using RFM scores and unsupervised learning.
Ship a small production app: data pipeline, model, API, and a minimal UI with auth and monitoring.
These industry-aligned projects demonstrate real metrics, clean engineering, and clear communication—exactly what hiring managers want for Data Analyst, ML Engineer, and Data Scientist roles.
*Project scope may vary by dataset, domain, and pace.
Real feedback from graduates who advanced their careers in Deep Learning, NLP, and Generative AI. Verified, job-focused, and packed with Python + Transformer projects recruiters recognize.
“This program helped me move from theory to production. The LLM fine-tuning and RAG modules were exactly what hiring teams asked me about.”
“Hands-on NLP projects, clean evaluation checkpoints, and clear rubrics. I shipped a transformer pipeline to the cloud in week three.”
“I landed my first role in Generative AI. The portfolio reviews and mock interviews made a real difference in my confidence.”
“Crystal-clear teaching, strong MLOps focus, and ethical AI practices. The program mirrors real-world workflows and expectations.”
“Vector search, embeddings, and FastAPI deployment in one stack. I showcased measurable metrics recruiters loved.”
“Up-to-date curriculum on Transformers, LoRA/QLoRA, and guardrails. The capstone aligned perfectly with interview questions.”
“This program helped me move from theory to production. The LLM fine-tuning and RAG modules were exactly what hiring teams asked me about.”
“Hands-on NLP projects, clean evaluation checkpoints, and clear rubrics. I shipped a transformer pipeline to the cloud in week three.”
“I landed my first role in Generative AI. The portfolio reviews and mock interviews made a real difference in my confidence.”
“Crystal-clear teaching, strong MLOps focus, and ethical AI practices. The program mirrors real-world workflows and expectations.”
“Vector search, embeddings, and FastAPI deployment in one stack. I showcased measurable metrics recruiters loved.”
“Up-to-date curriculum on Transformers, LoRA/QLoRA, and guardrails. The capstone aligned perfectly with interview questions.”
Read independent reviews and ratings for our Python-based Deep Learning, NLP, and Generative AI training program. Alumni highlight real-world projects, interview preparation, and production deployment skills.
Careers across Artificial Intelligence, Data Science, Machine Learning, NLP, and Generative AI — from Data Analyst to ML Engineer and Applied Scientist.
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…and many more leading product & services companies.
Designed for motivated learners to become job-ready in Deep Learning, NLP, and Generative AI.
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Advance your expertise in Deep Learning, NLP, and Generative AI with production-style labs.
Build a job-ready portfolio with mentor-reviewed projects, interview prep, and placement assistance.
Integrate and deploy AI models in real systems with evaluation, monitoring, and lightweight MLOps.
Design and ship cutting-edge apps using Transformers, prompt engineering, and RAG with vector DBs.
A job-ready AI/ML stack for real projects: modeling, MLOps, APIs, and visualization. Learn the tools that recruiters recognise and teams use.
python, tensorflow, pytorch, keras, nltk, spacy, hugging face, docker, numpy, pandas, scikit-learn, fastapi, ai tools, machine learning libraries, data science stack, generative ai framework
Master the Python data science toolkit end-to-end — from data wrangling and feature engineering to deep learning, LLMs, and deployment.
*Tooling may vary by project and track.
Follow these 4 proven steps to go from learner to a job-ready Deep Learning / NLP / Generative AI professional with a portfolio recruiters trust.
Deep Learning fundamentals → NLP pipelines → Transformers & LLMs. Master embeddings, tokenization, and hands-on model training with Python.
Ship an end-to-end RAG app, a fine-tuned LLM (LoRA/QLoRA), and a vision/NLP capstone. Document with READMEs, metrics, and demos.
Expose FastAPI endpoints, containerize with Docker, and deploy to cloud. Add vector search, logging, evals, and guardrails for safe AI.
Resume ATS optimization, mock interviews, DS/ML warm-ups, and project storytelling. Target ₹10–20 LPA roles in AI Engineer / NLP / GenAI Dev.
Learn from anywhere. Your journey to a Generative AI career starts here.
All the essentials about our Deep Learning, NLP & Generative AI program—entry requirements, duration, tools, certification, and placements.
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Enroll now to get 100% job assistance, mentor-led guidance, and a portfolio of real AI projects using Python, PyTorch, LLMs, and Transformers.
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Limited seats • Flexible schedules • Mentor feedback on every project
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