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Data Science & AI Advanced Code: ITA-112

Deep Learning & Generative AI

CNNs, RNNs, diffusion models and LLM application development with LangChain, vector databases and RAG pipelines.

★★★★★ 5.00 (458 students) Taught by Dr. Ayesha Khan 1,582 views
Deep Learning & Generative AI ΓÇö Data Science & AI course at ITA Academia
Duration
12 Weeks
Mode
Hybrid
Audience
University (BS / MS / PhD)
Certificate
Verified

CNNs, RNNs, diffusion models and LLM application development with LangChain, vector databases and RAG pipelines.

The Deep Learning & Generative AI program at ITA Academia is a 14-week, mentor-led track designed around real industry workflows. Every module combines live instruction with guided labs, and ends with a graded deliverable that goes straight into your portfolio.

What makes this course different

  • Project-based learning: you build real, demonstrable work ΓÇö not just slides and quizzes.
  • Industry mentors: taught by practitioners who are actively shipping in their domain.
  • Career systems: CV and portfolio reviews, mock interviews and placement support through our hiring-partner network.
  • Flexible pacing: morning, evening and live-online batches with recorded sessions for revision.

By the end of this course you will be able to

  • Build CNN and sequence models
  • Understand attention and transformer internals
  • Develop LLM apps with LangChain & OpenAI-compatible APIs
  • Implement RAG with vector databases
  • Fine-tune open models with LoRA

Certification

Graduates receive an ITA Academia verified certificate of completion and a transcript of assessed projects. Selected students are recommended to hiring partners and internship programs.

What you will learn

  • Build CNN and sequence models
  • Understand attention and transformer internals
  • Develop LLM apps with LangChain & OpenAI-compatible APIs
  • Implement RAG with vector databases
  • Fine-tune open models with LoRA

Who should attend

  • BS / MS / MPhil / PhD students and fresh graduates
  • Final-year project and thesis students
  • Professionals switching into a tech career

Prerequisites

Working knowledge of programming and the fundamentals covered in our foundation tracks. Admission is subject to a short technical interview.

Course curriculum · 8 modules

Hands-on sessions, guided labs and a graded assignment covering deep learning foundations.

  • Concepts & live demo
  • Guided lab exercise
  • Assignment & review

Hands-on sessions, guided labs and a graded assignment covering cnns & vision.

  • Concepts & live demo
  • Guided lab exercise
  • Assignment & review

Hands-on sessions, guided labs and a graded assignment covering sequence models & attention.

  • Concepts & live demo
  • Guided lab exercise
  • Assignment & review

Hands-on sessions, guided labs and a graded assignment covering transformers & llms.

  • Concepts & live demo
  • Guided lab exercise
  • Assignment & review

Hands-on sessions, guided labs and a graded assignment covering prompting, langchain & agents.

  • Concepts & live demo
  • Guided lab exercise
  • Assignment & review

Hands-on sessions, guided labs and a graded assignment covering rag & vector databases.

  • Concepts & live demo
  • Guided lab exercise
  • Assignment & review

Hands-on sessions, guided labs and a graded assignment covering fine-tuning & evaluation.

  • Concepts & live demo
  • Guided lab exercise
  • Assignment & review

Hands-on sessions, guided labs and a graded assignment covering generative ai capstone.

  • Concepts & live demo
  • Guided lab exercise
  • Assignment & review
Dr. Ayesha Khan

Dr. Ayesha Khan

Head of AI & Data Science · ex Careem

PhD in Computer Science with a decade of applied ML experience. Ayesha leads the AI & Data Science faculty and has shipped recommendation and forecasting systems used by millions of riders.

Machine LearningLLM Fine-tuningData Pipelines
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