AI & ML TRACK

Master AI to Create Solutions
That Think, Learn & Predict

Learn how to design, train, and deploy machine learning models through mentor-guided, real-world projects.

Next CohortStarts 1 September 2026
Duration7-8 Weeks
Format100% Online
Live mentor support
4 real-world projects
Verified portfolio
Project-based assessment
HR-visible skill profile

Become an AI/ML Engineer

Cohort + PAT Bundle

9,999

Program Pricing (Pilot Cohort)

  • Includes Live Mentor Support
  • Every project mentor-verified before certification
  • 100% Online Format
  • Portfolio review and interview preparation.

Speak to an Advisor

Not sure if this is the right fit? Drop your details and we'll call you back within 24 hours to discuss your goals.

Why This Track?

Artificial Intelligence is reshaping every industry. This track gives you the practical skills to build predictive models, NLP apps, and smart systems.

Data preprocessing and feature engineering
Supervised & unsupervised learning
Neural networks & deep learning
Natural Language Processing (NLP)
Model deployment and evaluation
Industry-Relevant Projects: Students work on real-world datasets, build predictive models, and deploy AI solutions.

Who This Track Is For

  • College students (any stream)
  • Beginners in AI & ML
  • Coding enthusiasts with interest in algorithms
  • Career switchers aiming for AI/ML roles
  • Professionals seeking AI upskilling
Note: Python basics will be taught inside the track.

Learning Tools and Methodology

Master an industry-standard technical stack through our hands-on engineering pedagogy and structured practice.

Tools You Will Master

Python
Pandas, NumPy
Scikit-learn
ML Algorithms
TensorFlow / Keras
Deep Learning
PyTorch
Deep Learning
NLTK / SpaCy
NLP Libraries
Jupyter
Notebooks
Flask / Streamlit
Model Deployment
Docker
Containerization

How You Will Learn

Live Mentor Sessions

Project-led guidance

Real Datasets

Work with authentic data

Weekly Milestones

Track progress regularly

Recorded Lessons

For concept revision

Step-by-step

Guided project building

Doubt Support

Clear your queries

Project Roadmap

A rigorous 4-project spine that mirrors actual industry workflows, augmented by targeted add-on mini-projects.

Core 4-Project Spine

Step-by-step development phases mirroring real assessment rounds

★ Industry Mirrored
Solo Project 1
Weeks 1–2
Mirrors: Technical phone screen
Project Brief

Given a clean dataset and a specified target metric, build and evaluate a baseline. Reproducibility is graded: same seed, same numbers.

Solo Project 2
Weeks 3–4
Mirrors: Applied domain round
Project Brief

A dirty dataset with leakage planted in it and no stated metric. Choose the metric, justify it, and find the leak. Finding the leak is most of the grade.

Pair Project
Weeks 5–6
Mirrors: Live ML system design round
Project Brief

One student owns retrieval, the other owns generation and evaluation, on a shared RAG service. They must agree an interface and an eval set before either writes code.

Capstone
Weeks 7–8
Mirrors: Full funnel, end to end
Project Brief

Student-proposed applied ML system with a real evaluation story — what it gets wrong, measured. (e.g. Full RAG/agent capstone).

8 Targeted Mini-Projects

Hands-on skill builders covering practical tools & concepts

★ Build & Verify
1
Hashing

Deduplication / embedding-cache layer for repeated inference calls

2
Heaps

Top-k retrieval ranker for a RAG search feature

3
Linked List + Hashmap

LRU cache for inference responses

4
Trees (BST / Trie)

Prefix/vocabulary search over an embedding index

5
Graphs (BFS/DFS)

Dependency resolution for a multi-step agent workflow

6
Graphs (weighted)

Cost-aware router across multiple model endpoints

7
Dynamic Programming

Token-budget / context-window allocator

8
Arrays / Sliding Window

Streaming-token rate limiter for API calls

Built for Recruiter Review

Your Portfolio Output

Your SkillCred portfolio isn't a static resume. It is a live, verified candidate profile showcasing working architectures, clean code, and assessment scores structured for recruiter review.

Verified PAT Score

Recruiters filter candidates by actual competency scores across system design and code quality.

Architecture & Implementation

Showcase the real-world systems you designed and deployed, not just code snippets.

Target Career Roles

Machine Learning Engineer
AI Engineer
NLP Engineer
Data Scientist
AI Support Specialist
SC

Candidate #SC-7241

PAT ID: pat_8291_active

★ PAT Certified
Sample Profile — Illustrative Only
Recruiter Skill Matches:
Python
Machine Learning
NLP
AI Deployment
Verified Project Catalog:
Sentiment Analysis Pipeline

FastAPI pipeline serving a text classification model built with scikit-learn.

✓ Mentor Verified
Pythonscikit-learnFastAPIDocker
F1 Score89/100
Latency (ms)94/100

Mentor Support & Frequently Asked Questions

Understand how working professionals review your code every week and get answers to common questions before starting your journey.

Mentor Support & Verification

Our mentors don't just teach — they verify your skills. Every project you build is reviewed, ensuring you meet industry standards before you get certified.

  • Assign & explain projects
  • Review project submissions
  • Verify project completion
  • Provide feedback
  • Approve assessment eligibility
  • Issue recommendation letters
Mentor Verified

Projects Are Not Self-Assessed

"You cannot certify yourself. A working professional mentor must start, review, and approve your work."

Frequently Asked Questions

Do I need prior Python experience?

No — Python basics are included in the track.

Are projects real-world?

Yes, each project replicates real industry problems.

Is deployment included?

Yes, models are deployed via Streamlit / Flask for live testing.

Will I be job-ready?

The track covers building, evaluating, and deploying AI systems.

Lock in Your Pilot Pricing

Secure your spot today with a fully refundable deposit. Fully credited toward your final enrollment balance.

Pilot cohort pricing available.