🐍 Python
From zero to full-stack Python — practical, exam-oriented courses for every level
Beginner
10 hrs
Python Basics & Problem-Solving
Master syntax, conditions, and functions — no experience needed
Beginner
15 hrs
Python for Data Handling
Strings, files, and iterating over data with confidence
Intermediate
20 hrs
Python Data Structures
Lists, dictionaries, tuples, and regular expressions
Intermediate
15 hrs
Object-Oriented Programming in Python
Classes, inheritance, polymorphism, and encapsulation
Intermediate
25 hrs
Python for Web & Networking
Sockets, REST APIs, SQLite, and mini full-stack projects
Beginner
60 hrs
Full Python Application Programming
All five modules end-to-end — from basics to full-stack apps
🧠 AI & Neural Networks
ANN foundations through deep learning, competitive programming, and a full capstone project
Beginner
10 hrs
Introduction to Artificial Neural Networks
Biological neurons, perceptrons, and gradient descent — the foundation
Intermediate
12 hrs
Supervised Learning in Neural Networks
LMS, backpropagation, and MNIST digit classification
Intermediate
10 hrs
SVM and RBF Networks
Support Vector Machines, kernel methods, and function approximation
Intermediate
10 hrs
Attractor Networks & Associative Memory
Hopfield Networks, Boltzmann Machines, and TSP optimisation
Intermediate
10 hrs
Self-Organizing Maps & Unsupervised Learning
SOM, PCA, vector quantisation, and customer segmentation
Advanced
10 hrs
Advanced Topics: Deep Learning & RL
CNN, RNN/LSTM, reinforcement learning, and advanced optimisation
Advanced
8 hrs
Practical Applications of Neural Networks
Healthcare, finance, NLP, computer vision, and model deployment
All Levels
10 hrs
Exam Preparation & Problem-Solving Workshop
Mock tests, previous year papers, and interview question bank
Advanced
6 hrs
Neural Networks for Competitive Programming
Kaggle competitions, hackathons, and model speed optimisation
Advanced
12 hrs
Capstone: Build a Neural Network from Scratch
End-to-end: design, train, tune, and deploy — without libraries
∫ Complex Analysis
Complex functions, Cauchy-Riemann equations, conformal mappings, and contour integration
Intermediate
10 hrs
Introduction to Complex Analysis
Complex numbers, limits, continuity, and analytic functions
Intermediate
12 hrs
Cauchy-Riemann Equations & Analytic Functions
Verification, construction, and the Milne-Thompson method
Intermediate
8 hrs
Conformal Transformations
Standard mappings, bilinear transformations, and engineering uses
Intermediate
10 hrs
Complex Integration & Cauchy's Theorem
Line integrals, Cauchy's theorem, and the integral formula
📊 Probability & Statistics
Distributions, curve fitting, regression, and hypothesis testing — all exam-oriented
Intermediate
10 hrs
Probability Distributions — Discrete
Binomial and Poisson distributions with engineering applications
Intermediate
10 hrs
Probability Distributions — Continuous
Exponential, Normal distributions, and the Central Limit Theorem
Intermediate
8 hrs
Curve Fitting & Least Squares
Linear, parabolic, and power curve fitting from first principles
Intermediate
10 hrs
Correlation & Regression Analysis
Pearson's coefficient, rank correlation, and regression lines
Intermediate
8 hrs
Joint Probability Distributions
Joint PMF, marginal distributions, covariance, and independence
Intermediate
12 hrs
Sampling Theory & Hypothesis Testing
t-test, chi-square test, Type I/II errors, and A/B testing
Back to AI & Neural Networks
Intermediate
10 Hours
Self-Organizing Maps & Unsupervised Learning
SOM, PCA, vector quantisation, and customer segmentation
✓ Live group sessions (full course duration)
✓ Dedicated doubt-clearing within the batch
Pricing (per student)
10+ students
₹175 / hr
Total: ₹1,750
15+ students
₹150 / hr
Total: ₹1,500
20+ students
₹125 / hr
Total: ₹1,250
Prerequisites
- Completion of Introduction to ANN (or equivalent)
- Basic linear algebra (eigenvalues, eigenvectors)
- Familiarity with clustering algorithms (K-means)
Overview
Explores unsupervised learning techniques with a focus on Self-Organizing Maps. Students implement PCA for dimensionality reduction, train a SOM on a 2D grid, and apply it to a real-world customer segmentation problem.
Topics
| Hour | Topic | Details |
|---|---|---|
| 1 | Introduction to Unsupervised Learning | Supervised vs. unsupervised learning. |
| 2 | Maximal Eigenvector Filtering | Apply eigenvector filtering for dimensionality reduction. |
| 3 | Principal Component Analysis (PCA) | Implement PCA from scratch. |
| 4 | Generalised Learning Laws | Hebbian learning and other unsupervised rules. |
| 5 | Vector Quantisation | Apply vector quantisation for data compression. |
| 6 | Self-Organizing Feature Maps — Theory | Derive the SOM algorithm and its properties. |
| 7 | SOM Training & Visualisation | Train a SOM on a 2D grid and visualise results. |
| 8 | Applications of SOM | Use SOM for clustering and anomaly detection. |
| 9 | Growing Neural Gas | Implement Growing Neural Gas for adaptive clustering. |
| 10 | Case Study: Customer Segmentation | Apply SOM to segment customers by purchasing behaviour. |
Expected Outcomes
- Implement SOM for clustering and data visualisation.
- Apply PCA for dimensionality reduction.
- Use unsupervised learning techniques on real-world datasets.