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🤖Mathematics and Statistics for AI and Machine Learning hero

Level: Beginner

🤖Mathematics and Statistics for AI and Machine Learning

Learn the essential math behind AI and machine learning. Master linear algebra, calculus, probability, statistics, and optimization with practical examples instead of complex proofs.

4.0

3.2k+ learners

Duration

0 min

Learners

3.2k+ Enrolled Students

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Post Session

Mentor support

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Includes

• Expert Designed Curriculum

• Doubt Clearing Session

• Forever Community Access

After this course, you can

Master AI math fundamentals

Understand ML statistics

Apply probability confidently

Prepare for advanced ML

Course Curriculum

Expand or explore the full learning path.

Lessons

Start here after enrolling. These lessons form the core learning path for the course.

Why You Must Understand Data Before Modeling It

Mean, Median, Mode: The Three Ways to Find "The Middle"

Variance & Standard Deviation: How Spread Out Is Your Data?

Percentiles & Quartiles: Slicing Data Into Pieces

Skewness & Kurtosis: When Data Leans and When It Spikes

Visualizing Stats: Histograms, Box Plots, and Spotting Outliers

What Probability Really Is, and Why Prediction Needs It

Basic Probability Rules: AND, OR, NOT

Conditional Probability: How New Information Changes the Odds

Independence: When Events Don't Care About Each Other

Bayes' Theorem: Updating Your Beliefs with Data

Expected Value: What to Expect in the Long Run

What a Distribution Is, and Why Real-World Data Follows Patterns

Normal Distribution: The Famous Bell Curve

Bernoulli & Binomial: Yes/No Events and Counting Successes

Poisson Distribution: Counting Arrivals and Rare Events

Uniform Distribution: When Everything Is Equally Likely

Central Limit Theorem: The Most Magical Result in Statistics

Sampling and Sampling Distributions: Learning from a Slice

Confidence Intervals: How Sure Are We, Really?

Hypothesis Testing: The Logic of Proving Things with Data

p-values and Significance: What They Mean and What They Don't

# t-tests and Chi-square Tests: The Tests You'll Actually Use

A/B Testing: Inference in the Real World

Vectors and Vector Operations: Your Features Have a Shape

Matrices and Matrix Multiplication: Your Dataset Is a Matrix

Linear Transformations: How Matrices Move and Stretch Space

Eigenvalues and Eigenvectors: Finding the Directions That Matter

Matrix Decomposition: SVD and PCA Explained Simply

Where This Shows Up in ML: Neural Nets, PCA, and Recommendations

Derivatives and Gradients: The Slope Tells You Where to Go

Partial Derivatives: Changing One Thing at a Time

The Chain Rule, and Why It Powers Backpropagation

Gradient Descent Intuition: Rolling Downhill to the Answer

Learning Rates, Convexity, and Why Models Get Stuck

Local Minima and How to Escape Them

Maximum Likelihood Estimation: Picking the Most Believable Model

Bayesian Inference Basics: Prior, Likelihood, Posterior

Confident vs Guessing: Uncertainty in Model Predictions

Probabilistic Graphical Models: A Friendly Intro

Information Theory Basics: Entropy and Surprise

Reading ML Papers: Recognizing the Math in the Wild

Live Session Content

This topic will be taught live in an interactive session, allowing you to engage with the instructor and ask questions in real time. While recordings will be available, attending live offers the best learning experience.

Post Session Study Material

This section will be unlocked after the session. You'll get access to exclusive bonus content, additional examples, and on-demand resources to support continuous learning and deeper understanding.

Requirements

What you should have before starting the course.

Basic high school mathematics

Basic Python knowledge is helpful

No AI or ML experience required

Willingness to solve practice problems

Tools you are going to use

Scikit-learn

Scikit-learn

MLflow

MLflow

Python

Python

Pandas

Pandas

Matplotlib

Matplotlib

Jupyter

Jupyter

Learn From Expert Instructor

Meet the mentors leading the live sessions.

Aarav Sharma

Aarav Sharma

Senior Frontend Engineer, StudioLabs

Aarav has mentored 1200+ learners and specializes in production UI systems and scalable frontend architecture.

LinkedIn
Ananya Mehta

Ananya Mehta

Design Technologist, CraftLabs

Ananya blends design and code to help learners ship portfolio-ready projects.

LinkedIn
Kabir Sayed

Kabir Sayed

Frontend Mentor, PixelWorks

Kabir focuses on clean code, accessibility, and practical frontend delivery.

LinkedIn

You should join this course if you

Students & Fresh Graduates

Build a strong portfolio

Learn from live mentors

Get feedback on your work

Working Professionals

Upgrade frontend skills

Ship real projects

Join a peer community

Career Switchers

Structured learning path

Hands-on practice

Guided sessions

Freelancers & Creators

Build client-ready sites

Improve delivery speed

Learn modern workflows

What you will get after completing this course

Completion certificate

Receive an official course completion certificate

Earn skill-focused feedback from mentors

Showcase your project in the Shattak community

Access lifetime notes and references

Stay connected with instructors for guidance

Completion bonus

Certificate access, community showcase, and lifetime mentor guidance.

Hear From Learners Who've Taken This Course

Honest feedback from learners who completed the live sessions.

4.8

24

Clear structure, solid examples, and a fast pace that keeps you engaged.

Riya Jain

Riya Jain

B.Tech, NIT Trichy

4.6

18

Practical sessions with feedback that helped improve my portfolio.

Kabir Singh

Kabir Singh

Frontend Intern, PixelWorks

4.9

12

Loved the structure and the live walkthroughs.

M

Meera Patel

Design Graduate

See what they have build

ZS

Zainab Shaikh

Portfolio Landing Page

Portfolio Landing Page

300 Likes

View Live
DS

Devendra Singh

Product Marketing Site

Product Marketing Site

214 Likes

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NA

Nadia Ahmed

Interactive Web Story

Interactive Web Story

183 Likes

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₹1,299

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Get Life Time Access

₹0

₹1,299

100% off