The GenAI Odyssey: From Foundations to Frontier Models
Route: /courses/genai-odyssey/
Status: Working content draft for review
Purpose: Content architecture, copy direction, SEO starting points, and page-level placeholders
Source basis: WINNOW learning catalogue and Debojyoti Biswas profile documents supplied for the project
Important: Claims, dates, pricing, batch schedules, testimonials, placement outcomes, institutional clients, and certifications must not be published unless verified and approved.
Page purpose
Convert visitors who have an identified need in this subject area by clearly explaining the programme, expected capability, curriculum depth, learner fit and next step.
SEO starting point
Primary topic: generative AI course
Starting keyword batch
- generative AI course
- LLM course
- transformer architecture course
- RAG course
- Generative AI foundations
- embeddings attention transformers course
- fine tuning LLM course
- vector database RAG training
- LLM evaluation hallucination course
SEO caution: Validate volumes and search intent before finalising metadata. Avoid competing with the Courses overview page for broad terms.
Tentative content hierarchy
- Hero
- Why this programme exists
- Who it is for
- What you will be able to do
- Programme structure and learning approach
- Curriculum
- Prerequisites and course fit
- Instructor credibility
- FAQ
- Enquiry CTA
Draft page content
Hero
Flagship Track · Generative AI Foundation Series
The GenAI Odyssey: From Foundations to Frontier Models
The GenAI Odyssey is designed for learners who want to understand Generative AI beneath the chat interface. It moves from mathematics and Python through text representation, embeddings, neural networks, attention and transformers, then into fine-tuning, generation and RAG.
16 live sessions
Format: Live & hands-on
Cadence: Weekly
Primary CTA: Discuss Course Fit
Secondary CTA: View Curriculum
[CONFIRM: session duration, cohort size, online/offline availability, next batch date, fees]
Why this programme
The page should frame the course around a practical capability gap, not around software features alone.
Suggested copy:
The GenAI Odyssey is designed for learners who want to understand Generative AI beneath the chat interface. It moves from mathematics and Python through text representation, embeddings, neural networks, attention and transformers, then into fine-tuning, generation and RAG.
The objective is not to cover every possible feature or technique. WINNOW’s broader philosophy is to identify what matters in real work, teach it in a coherent sequence, and remove avoidable noise.
Who this is for
- Developers and data professionals moving into Generative AI
- ML learners who want a structured bridge into LLMs
- Technical professionals who want to understand transformers and RAG from first principles
- Serious learners who want more than prompt-engineering tutorials
[CONFIRM: whether students/freshers are a primary audience for this specific programme]
What you should be able to do afterwards
Understand the foundations and architecture behind modern language models and build readiness to evaluate, adapt and deploy Generative AI systems with stronger technical grounding.
Use a short capability list beneath this paragraph. Pull only from the curriculum and avoid unsupported employability claims.
[CONTENT NOTE: Convert curriculum items into 4 to 6 observable capabilities. For example, “clean and combine messy datasets” is stronger than “understand data cleaning”.]
How the programme works
Current source material supports:
- Live instruction
- Hands-on format
- Weekly cadence
- Applied exercises/projects or workshops where explicitly listed in the curriculum
[CONFIRM: assignments, office hours, recordings, mentoring, assessments, certificate, community access]
Curriculum
Session 01: Mathematics Essentials
Vectors, Matrices & Matrix Operations · Calculus, Derivatives & Gradients · Probability & Distributions
Session 02: Python Essentials
Core Python Programming Concepts · NumPy, Pandas & Tensor Operations · Google Colab Workflow
Session 03: The AI Ecosystem
Rule-Based Systems, ML & Deep Learning · Generative AI, LLMs & Foundation Models · Agentic AI & Use-Case Fitment
Session 04: Data Types & Pre-Processing
Data Types & Representation · Text Normalisation & Classical Pre-Processing · Word, Character & Subword Tokenization
Session 05: Text Representation
One-Hot Encoding & Bag of Words · N-grams & TF-IDF · Sparse versus Dense Representations
Session 06: Word Embeddings
Distributional Semantics & Cosine Similarity · Word2Vec, CBOW & Skip-Gram · Embedding Evaluation & Interpretation
Session 07: Workshop: Semantic Search
Customer-Support Query Matching · Embedding-Based Similarity & Top-k Retrieval · Retrieval Evaluation
Session 08: Neural Network Foundations
Neurons, Activation Functions & Power · Loss Functions & Gradient Descent · Forward Propagation & Backpropagation
Session 09: Neural Architectures for Text
FFNN & CNN for Text Classification · RNN, LSTM & GRU · Architecture Comparison & Limitations
Session 10: Workshop: Spam & Fraud Detection
SMS Classification Using Neural Networks · FFNN, CNN & GRU Comparison · Performance Evaluation & Error Analysis
Session 11: Sequence-to-Sequence & Attention
Encoder-Decoder Architecture · Context-Vector Bottleneck & Alignment · Attention Mechanism with Examples
Session 12: Transformer Architecture
Self-Attention & Scaled Dot-Product Attention · Multi-Head Attention & Positional Encoding · Transformer Encoder & Decoder
Session 13: Model Fine-Tuning
Pretraining, Full & Supervised Fine-Tuning · PEFT, LoRA, QLoRA & Prefix Tuning · Fine-Tuning versus RAG
Session 14: Decoding & Generation
Greedy Search & Beam Search · Top-k & Top-p Sampling · Temperature & Generation Trade-offs
Session 15: Workshop: RAG & Vector Databases
Document Chunking, Embeddings & Storage · Semantic Retrieval & Context Augmentation · LLM API Integration & Grounded Generation
Session 16: Model Landscape & Synthesis
Model Taxonomies, Families & Selection · LLM Evaluation, Hallucination & Key Risks · Milestone Review & Interview Preparation
Prerequisites and course fit
[CONTENT REQUIRED: final prerequisite statement]
Content guidance:
- State required prior knowledge honestly.
- If no formal prerequisite exists, describe the level of comfort expected.
- Link to a preceding WINNOW programme where appropriate.
- Explain when this programme would be too basic or too advanced.
Instructor
Short trust block:
Debojyoti Biswas brings 19 years of experience across analytics, model risk, AI governance and Generative AI risk and control, with professional experience spanning Wells Fargo, Citi, IBM, Genpact and Infosys.
Link: /instructor/
[APPROVE: exact designation wording and use of employer logos]
FAQ placeholders
- Who is this course for?
- What prior knowledge do I need?
- Is the programme online, offline, or hybrid?
- How long is each session?
- Will sessions be recorded?
- What software or accounts will I need?
- Is there a project or capstone?
- Will I receive a certificate?
- What is the fee?
- When is the next cohort?
Do not publish placeholder answers.
Final CTA
Not sure whether The GenAI Odyssey is the right starting point?
Discuss your current experience, goals and available time before choosing a programme.
CTA: Discuss Course Fit
[CONFIRM: WhatsApp, form, email or scheduling link]
Internal links
/courses//learning-paths//instructor//contact/- Relevant insight articles when published
Content/asset requirements
- [ASSET REQUIRED: course visual or consistent programme graphic]
- [CONTENT REQUIRED: fees]
- [CONTENT REQUIRED: batch schedule]
- [CONTENT REQUIRED: prerequisites]
- [CONTENT REQUIRED: session duration]
- [CONTENT REQUIRED: delivery location/mode]
- [CONTENT REQUIRED: certificate policy]
- [CONTENT REQUIRED: learner proof/testimonials, if available]