Admissions Open for JANUARY Batch
Learn essential math linear algebra, calculus, probability, and logic for AI models.
Days : Tue & Thu
Duration : 5 Hours
Timings: 8 - 10 PM IST
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1 Months
5 Hours
Tue & Thu
Core Math Foundations
( ML Essentials )
This course covers the essential math building blocks you’ll need for any AI or data science work.
You’ll learn how numbers, equations, and relationships between variables work together. You’ll also
understand how to describe change, measure uncertainty, and use logic in AI problems.
Online Live Instructor-Led Learning
5 Hours
8 - 10 PM IST
Tue & Thu
By end of this course
Get stronger in
Vectors, matrices, and basic operations
Probability basics and data distributions
Get familier with
Calculus concepts like derivative and slope
Logical reasoning, counting, and graph theory
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is designed for learners who want to develop the essential math skills in linear algebra, probability, and calculus required to understand and build machine learning models
Prerequisites
Comfort with algebra, arithmetic, and basic graphing skills.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Comfort with algebra, arithmetic, and basic graphing skills.
Who Should Enroll?
This course is designed for learners who want to develop the essential math skills in linear algebra, probability, and calculus required to understand and build machine learning models
By end of this course
Get Stronger in
- Vectors, matrices, and basic operations
- Probability basics and data distributions
Get Familiar in
- Calculus concepts like derivative and slope
- Logical reasoning, counting, and graph theory
Course Contents
What is covered: Vectors, matrices, operations like addition and multiplication.
Application: Data representation, image processing, neural networks.
Example:
1. Image as Matrix: A grayscale image is a matrix of pixel values. Neural networks process these matrices to recognize objects.
2. Matrix Multiplication: Used to combine weights and inputs in every layer of a neural network.
What is covered: Studying change (derivatives), finding minimum/maximum values.
Application: Training models by minimizing error (loss), adjusting weights.
Example:
1. Gradient Descent: The process of finding the best model parameters by moving in the direction that reduces error, like rolling a ball downhill.
2. Backpropagation: Calculating how much each weight in a neural network should change to improve predictions.
What is covered: Measuring uncertainty, analyzing data, making predictions.
Application: Predicting outcomes, evaluating models, handling randomness.
Example:
1. Spam Detection: Using probability to decide if an email is spam based on words it contains.
2. Model Evaluation: Calculating accuracy, precision, and recall to see how well a model performs.
What is covered: Logic, graphs, counting, combinations.
Application: Social networks, recommendation systems, logical reasoning.
Example:
1.Friend Recommendations: Using graph theory to suggest new friends on social media.
2.Counting Possibilities: Calculating how many ways a password can be formed.
This section includes a comprehensive evaluation covering all course topics, designed to measure understanding and mastery of key mathematical concepts presented throughout the course
What is covered: Vectors, matrices, operations like addition and multiplication.
Application: Data representation, image processing, neural networks.
Example:
1. Image as Matrix: A grayscale image is a matrix of pixel values. Neural networks process these matrices to recognize objects.
2. Matrix Multiplication: Used to combine weights and inputs in every layer of a neural network.
What is covered: Studying change (derivatives), finding minimum/maximum values.
Application: Training models by minimizing error (loss), adjusting weights.
Example:
1. Gradient Descent: The process of finding the best model parameters by moving in the direction that reduces error, like rolling a ball downhill.
2. Backpropagation: Calculating how much each weight in a neural network should change to improve predictions.
What is covered: Measuring uncertainty, analyzing data, making predictions.
Application: Predicting outcomes, evaluating models, handling randomness.
Example:
1. Spam Detection: Using probability to decide if an email is spam based on words it contains.
2. Model Evaluation: Calculating accuracy, precision, and recall to see how well a model performs.
What is covered: Logic, graphs, counting, combinations.
Application: Social networks, recommendation systems, logical reasoning.
Example:
1.Friend Recommendations: Using graph theory to suggest new friends on social media.
2.Counting Possibilities: Calculating how many ways a password can be formed.
This section includes a comprehensive evaluation covering all course topics, designed to measure understanding and mastery of key mathematical concepts presented throughout the course
Phase 1: From Coders to Creators
You’ll set up your professional coding environment by installing VS Code and Jupyter, introduce ChatGPT as a coding co-pilot, and learn to build effective prompts to generate code, establishing a productivity mindset for modern development.
Learn to reframe coding as building blocks for real applications by working with CSV, JSON, and image datasets from relatable domains like YouTube, food, and books, developing a system-level thinking approach.
Master abstraction, reusability, and clarity in logic by breaking down real-world use cases like meal planners and birthday reminders into modular code components using functions, loops, and conditions.
Build a functional CLI project such as a task tracker or GPA calculator, solving real-world problems like smart schedulers or basic calculators while developing ownership and confidence in your coding abilities