Ready to chart a clear path from complete beginner to confident AI engineer? In Episode 26, Nisaar and Rohan unveil a 12-week Generative AI roadmap packed with curated resources, hands-on projects, and expert guidance. Here’s what we’ll cover:

Roadmap Link : https://chatgpt.com/s/t_6873bcbce6708191b9bdbb3b878e31eb
Gtihub : https://github.com/NisaarAgharia/GenAI-Roadmap-for-Beginners

Timestamps
00:00 Intro & Episode Overview
10:09 Week 1 – Self-Learning AI: Structured Roadmap
13:50 Week 1 – Hands-On Generative AI Experimentation
16:27 Week 1 – Exploring Generative AI Applications & Security
18:26 Week 1 – MCP: Streamlined Third-Party Integrations
26:47 Week 2 – Backpropagation & Gradient Descent Fundamentals
28:05 Week 2 – Overfitting, Misclassification & Backpropagation
30:49 Week 3 – Key Videos to Strengthen AI Foundations
32:10 Week 3 – Structured Output & Clarity in AI Responses
32:50 Week 3 – Foundational Concepts & Progression Steps
39:00 Week 4 – Neural Networks 101: Single-Layer Models
40:37 Week 4 – Subscribing to Quality AI Channels
41:17 Week 4 – “Attention Is All You Need” Paper Significance
43:34 Week 5 – AI Model Architectures & Performance Implications
45:01 Week 5 – Transformer Architecture: GPT & BERT Overview
47:04 Week 5 – In-Depth Transformer Layer Breakdown
49:16 Week 6 – Scalable Vector Databases for RAG
56:05 Week 6 – Latent-Space Visualization in VAEs
1:01:37 Week 7 – GANs & Diffusion: Noise Reduction Techniques
1:05:57 Week 7 – Diffusion Model Training Process

Week 1: Introduction to Generative AI
– What is generative AI? Key concepts, real-world examples (ChatGPT, DALL·E) and why it matters in 2025.

Week 2: ML & Deep Learning Foundations
– Core ML/DL building blocks: neural networks, backpropagation, training vs. inference.

Week 3: NLP Basics & Text Generation
– From n-grams and RNNs to LSTMs, understanding how language models predict and generate text.

Week 4: Transformers & Modern LLMs
– The “Attention Is All You Need” breakthrough, parallel sequence processing, and GPT-style architectures.

Week 5: Autoencoders & VAEs
– Learning latent spaces, reparameterization trick, and generating new images by sampling.

Week 6: GANs
– The adversarial game of Generator vs. Discriminator, DCGAN, StyleGAN, and photorealistic image synthesis.

Week 7: Diffusion Models
– The forward-noising and reverse-denoising process driving Stable Diffusion and DALL·E 3.

Week 8: Prompt Engineering & Practical LLM Use
– Crafting prompts for zero-shot and few-shot tasks, temperature tuning, and building intelligent chatbots.

Week 9: Hands-On GenAI Projects
– Rapid prototyping with Hugging Face Transformers, Diffusers, and Gradio; deploy shareable demos.

Week 10: Fine-Tuning Techniques
– Customizing LLMs via LoRA and DreamBooth; create domain-specific models with minimal data.

Week 11: Deployment & Scaling
– From Gradio protos to production APIs (FastAPI, SageMaker, Hugging Face Endpoints); optimization via quantization & distillation.

Week 12: Ethics, Bias & Future Trends
– Responsible AI, hallucination mitigation, IP considerations, and what’s next in multimodal & aligned AI.

Each week comes with hand-picked YouTube lectures (MIT, 3Blue1Brown, StatQuest, Two Minute Papers), Coursera & DeepLearning.AI courses (audit-friendly), interactive tutorials (Hugging Face, fast.ai), and key articles/reports to deepen your understanding and accelerate your progress.

Whether you’re just launching into Python or plotting your first GenAI startup, this episode delivers a step-by-step guide and all the resources you need to become a generative AI engineer in 2025. Let’s dive in!

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