180-Day AI and Machine Learning Course from Scratch

From First Principles to Production-Ready Intelligence

Welcome. If you are a Software Engineer, an Architect, a Product Manager, or a DevOps lead, you’ve likely seen "AI courses" that feel like either a dry math textbook or a shallow API tutorial. This is neither.

Think of this as an Engineering Residency. We aren't just teaching you to use models; we are teaching you to build, optimize, and deploy them with the same rigor you apply to high-scale distributed systems.

Why This Course?

In the world of Big Tech and Fintech, AI is no longer a "plugin." It is a core architectural component. Most engineers struggle with AI because they treat it as a "black box." This course peels back the layers. You will understand the mechanics of weight updates as clearly as you understand database indexes. We focus on the intuition behind the math, so when a model fails in production, you know which "knob" to turn.

What You’ll Build

You won't just run scripts; you will build a portfolio of intelligent systems:

• The Oracle: A real-estate pricing engine using multi-variable regression.

• The Guardian: A real-time credit card fraud detection system using Ensemble methods.

• The Matchmaker: A high-scale movie recommender system (Collaborative Filtering).

• Visionary: A Deep Learning image classifier using Convolutional Neural Networks (CNNs).

• Sentient: A Natural Language Processing (NLP) sentiment analyzer for financial news.

Who Should Take This Course?

This is designed for the Builders.

• Engineers/SREs: To understand the compute costs and latency trade-offs of different models.

• Architects: To design systems where AI components integrate seamlessly with microservices.

• Product Managers: To understand what is mathematically possible versus "AI hype."

• UI/UX Designers: To design interfaces for non-deterministic system outputs.

What Makes This Course Different?

• Zero-Abstraction Start: We build a neural network using basic Python math before we ever touch a library like PyTorch.

• The "Production" Lens: We don't stop at "80% accuracy." We discuss model drift, latency, and hardware constraints.

• No "Math Phobia": We explain Calculus through the lens of "finding the fastest way down a hill" and Linear Algebra as "efficiently organizing spreadsheets."

Key Topics Covered

• Foundations: Pythonic Data Science, Linear Algebra, and Vector Calculus.

• Classical ML: Feature Engineering, Regularization, and Bias-Variance Tradeoffs.

• Deep Learning: Backpropagation, Activation Functions, and Architectures (CNN, RNN, Transformers).

• MLOps: Model Versioning, Inference Pipelines, and Scaling.

Prerequisites

• Basic logic and a "hacker" mindset.

• No prior math or AI experience required; we build the ladder as we climb it.

Course Structure

The 180 days are divided into 4 High-Impact Modules.