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Data Science & AI Bootcamp: ML, Deep Learning, LLMs & RAG
New
20 students
Data Science & AI Bootcamp: ML, Deep Learning, LLMs & RAG
18 modules, 480+ lessons & 23 real projects: Python, ML, PyTorch, NLP, Transformers, LLM fine-tuning, RAG & MLOps
Created byIHTISHAM AHMAD
Last updated 11/2026
English
What you'll learn
- Build real machine learning models end to end with Python, NumPy, Pandas, SQL and Scikit-learn on real datasets
- Train deep neural networks and computer vision models with PyTorch on your own laptop or free Colab GPUs
- Master NLP and Transformers from scratch, then fine-tune LLMs with LoRA, QLoRA, quantization and RAG pipelines
- Deploy models with MLOps: FastAPI, Docker, monitoring, security and responsible AI practices
- Build 23 portfolio projects including fraud detection, demand forecasting, RAG chatbots and AI agents — complete runnable code included
- Understand the math behind AI: probability, statistics, gradients, attention and backpropagation — explained with real data, never dry theory
Explore related topics
Course content
20 sections • 488 lectures • 46h 49m total length
Requirements
- No experience needed — we start from zero. A laptop (even 4GB RAM) works, plus a free Google Colab path for every GPU lesson
Description
This course contains the use of artificial intelligence.
This course was created with the support of AI tools for scripting, narration, and visuals, under the direction and editorial review of the instructor.
Data Science, Machine Learning & AI — from absolute zero to research level.
This complete bootcamp takes you from your first line of Python to training, fine-tuning and deploying modern AI systems. Every topic is taught twice: a concepts & intuition lesson, then a hands-on lesson with real data — so you never just watch, you actually build.
What makes this course different:
18 structured modules: Python, math & statistics, NumPy, Pandas & SQL, machine learning, deep learning with PyTorch, computer vision, NLP & Transformers from scratch, LLM engineering, RAG, LoRA/QLoRA fine-tuning, reinforcement learning & AI agents, generative AI, and MLOps.
23 complete portfolio projects with full runnable code: fraud detection, demand forecasting, student success analytics, chat-with-documents RAG, image classifier, LoRA lab, model deployment and more.
Runs on YOUR laptop: every GPU lesson ships with an RTX 3050 (4GB VRAM) path AND a free Google Colab path.
Real datasets only: penguins, movies, sales, network logs — the same files used in every lesson, so you can follow along line by line.
Practical tactics that actually matter: 4GB VRAM workarounds, quantization, mixed precision, gradient accumulation and batch size tuning.
You will learn the fundamentals the right way: data cleaning, feature engineering, model evaluation, cross-validation, explainability, time series, causal inference, unsupervised learning, recommenders, and experiment design. Then you go modern: scaled dot-product attention, multi-head attention, encoder-decoder architectures, GPT from scratch, embeddings, vector databases, RAG pipelines, DPO alignment, diffusion models and multimodal AI.
By the end you will have a portfolio of 23 working systems, the mathematical intuition to read research papers, and the engineering skill to deploy AI that survives real users.
Enroll today and start building — the first module takes you from zero to a working AI environment on your own machine.
Who this course is for:
- Beginners who want a complete data science, machine learning and AI career path — from first line of Python to research-level AI