ML & AI Foundations: From Intuition to Implementation

Learn fundamentals of ML & AI in a practical manner by building hands-on projects that can be added in your resume.

What you’ll learn
✓ Understand the basic maths & programming used to build projects in AI & ML
✓ Get practical idea of basic and advanced ML Concepts
✓ Learn to build hands-on AI & ML Projects from Scratch
✓ Complete your interview preparation for AI Based Roles by showcasing the projects effectively in your resume & being prepared for FAQ’s on the built projects
✓ Confidently explain ML Concepts in Interviews
✓ Build & Debug Models on your own
✓ Think beyond black-box ML
✓ Choose the right model for the right problem

Requirements
● Basic Knowledge of Python or willingness to learn basic python on the go.
● Basic high school maths like matrix multiplication and vector operations.

Description
This course builds strong ML foundations by combining clear intuition, solid math, and hands-on implementation.

You won’t just use ML libraries — you’ll understand how models work internally, why they work, and when they fail.

After completing this course, you will

• Think beyond black-box ML

• Confidently explain ML concepts in interviews

• Build and debug models on your own

• Choose the right model for the right problem

In short: from following tutorials → to real ML understanding.

This course is ideal for

• Students & freshers aiming for ML/Data roles

• Software professionals transitioning into ML

• Anyone who knows “some ML” but lacks confidence

This course helps you upgrade your career by building real ML depth, not just surface knowledge.

What is covered?

• Math foundations for ML (basic → advanced)

• Core models: Linear & Logistic Regression, Decision Trees, Neural Networks

• Ensemble methods: Bagging, Boosting, Random Forest

• Optimizers, regularization, overfitting & bias-variance tradeoff

• Hands-On Learning

• Movie rating classification (Kaggle + GPUs)

• Neural Network implementation from scratch

• Music genre classification using MFCC + Neural Networks

• Interview preparation session for all covered topics

In one line

A practical, concept-driven ML course that turns learners into confident ML engineers

Detailed Course Breakdown

• Section 1 : Overview
– Introduction to the Instructor & Course
– Why knowledge of basic maths is crucial for intuition in AI & ML
– Things we will be learning during the course

• Section 2: Probability & Statistics
– Probability & Stats
– Mean, Median & Mode
– Calculation Expected Value
– Variance & Covariance
– Normal Distribution
– Central Limit Theorem
– Conditional Probability
– Baye’s Theorem
– Maximum Likelihood Estimation

• Section 3: Linear Algebra
– Overview of Linear Algebra
– Scalar, Vectors, Matrix & Tensors
– Matrix Operations
– Rank & Linear Dependence
– Eigen Vectors & Eigen Values
– Principle Component Analysis

• Section 4: Calculus
– Overview of Calculus
– Derivatives & Gradients
– Gradient Descent Algorithm
– Chain Rule
– Fundamentals of Optimisation
– Local vs Global Maxima
– Convexity

• Section 5: Basics of Python
– Practical Python for ML & AI

• Section 6: Introduction to ML
– Overview & Introduction to ML
– Basics of ML
– Classification of ML
– Regression vs Classification
– Trainset / Validation Set / Testset
– Overfitting (Learning vs Memories)

• Section 7: Training of Models
– One-Hot Encoding

• Section 8: Regression Methods
– Linear Regression
– Parameters to tests models

• Section 9: Decision Trees
– Introduction to Decision Trees
– Training & Testing Process
– I.G in Decision Trees
– G.I in Decision Trees

• Section 10: Ensembles
– Introduction to Ensembles
– Bagging
– Boosting

• Section 11: Training of Models
– Practical Training Methodology

• Section 12: Advanced Machine Learning
– Overview in Advanced Machine Learning

• Section 13: Logistic Regression
– What is Logisitic Regression ?
– Why Logistic Regression ?
– Maths behind Logisitic Regression?
– Do I always need Binary Classification?

• Section 14: Neural Networks
– Architecture & Overview
– Dive into Neural Network
– Generalization
– Batch Processing
– Optimizer

• Section 15: Demo
– Kaggle Tutorial
– Demo for Projects & Model Training

• Section 16: Hands-On Practical Implementation of Projects
– Hands-on Logistic Regression Coding
– Hands-on Decision Trees Coding
– Hands-on Neural Network Coding
– Neural Network Coding for Multi Category Classification

• Section 17: Interview Preparation for Prepared Projects
– FAQ in Interviews on projects discussed in the course

Who this course is for
■ Students & freshers aiming for ML/Data roles
■ Software professionals transitioning into ML
■ Anyone who knows “some ML” but lacks confidence