10b.Train a neural network| bare-bones Python | forward pass, gradients, and weight updates manually

10a. Deep Learning Systems: Building a PyTorch-like Library from Scratch – Lecture 1

9c. Forward and Backpropagation By Hand: Training a Real Neural Net on Pen & Paper

9b. 🧠 Mathematics of Backpropagation — Deriving It from Scratch for Complete Understanding

9a. Understanding Backpropagation: Gradients, Forward Pass, and Core Prerequisites

8b. Loss Functions | Cross-Entropy, KL Divergence, in under 2 hours

8a. Informatin thoery to understand loss functions

7b. How Neural Networks Learn ANY Function: The Universal Approximation Theorem Explained

7a. MLPs as Universal Boolean Function Mappers | Demystifying Depth vs. Width

6b. Learning Algorithms for Neural Networks | Taylor Series and Gradient Descent | Part 2

6a. Learning Algorithms for Neural Networks | Introduction to Gradient Descent Part 1

5. Perceptron Learning Algorithm: Convergence, Limitations, and Evolution to Multi Layer Perceptron

4. Building Artificial Neurons, Perceptrons & its Learning Algorithms with Code

#10 Discrete Distributions: Geometric, Negative Binomial, and Poisson | Expectations & examples

#9 Binomial sums | Hypergeometric Distributions| Expectation, CDF, and PDF | Examples | P4MLDL

#8.1 Probability in Genetics | Random Variable examples

#8 Understanding Random Variables | Discrete RV | Bernoulli Trials | Binomial Distributions

#7 Gamblers Ruin | Mutually exclusive vs independent events | Conditional Independence | examples

#6 Understanding Independence in Probability with Examples

#5.1 Applications of Bayes' Rule | Medical Paradox | Monty Hall using conditional probabilities

#5 Conditional Probability Explained: Bayes' Theorem & Applications in ML/DL | Examples

#4.1 Matching problem | Probability for ML and DL

#4 Understanding Probability for ML and DL : Fundamental Terms, Axioms & Examples

#3 Probability prerequisites 2 |Combinatorics | permutations & combinations | Sum & Mul Rules

#2 Probability prerequisites 1: Set Theory | Birthday Paradox & Monty Hall Problem

#1 What is Probability? Why Machine Learning and Deep Learning Rely on Probability Theory?

DeepSeek R1 paper walk through !! How to read Research Papers

2. From Synthetic Data Introducing Machine Learning and Deep Learning | Curve fitting

Exploratory Data Analysis|Univariate Analysis, Descriptive Statistics, Histograms, Density Plots etc

Univariate Missing Value Handling Techniques | end of distribution| missing indicator and more

Mastering Missing Values in ML: Listwise Deletion & Univariate Imputations | mean,median,mode,etc

Getting Started with Machine Learning: Exploratory Data Analysis & Data Preparation/Preprocessing

Linear Algebra | Machine Learning | elimination & it's geometric meaning | Code Elimination Part 5

Linear Algebra for Machine learning | Visualising Linear equations |Span | Linear Combination Part 5

Linear Algebra for Machine Learning | Visualizations of Linear Algebra basics | Code Part 4

Matplotlib | Animations | FuncAnimation | ArtistAnimation

Matplotlib | Pie Charts | Voilin Plot | Contours plots etc | Machine Learning Deep Learning Part V

Matplotlib | Stack Plot | Step Plot | Histograms | Machine Learning | Deep Learning | Part 4

Matplotlib | Types of plots | Line , Scatter Bar etc | Machine Learning | Deep Learning

Matplotlib | Machine Learning | Deep Learning | Part II

Generative AI | A Gentle introduction | LLMs | What to use them for

Pandas IV | Combining and merging datasets | Machine Learning | Deep Learning

Pandas Part III | Multilevel Indexing | Hierarchical Indexing | Machine Learning | Deep Learning

Pandas Part II | Machine Learning | Deep Learning

Pandas Part 1 | Machine Learning | Deep Learning

1. Machine Learning | Deep Learning | A complete Introduction

Machine Learning | Deep Learning | NumPy Part III

Machine Learning | Deep Learning | NumPy Part II

Machine Learning | Deep Learning| NumPy Part 1

S12:Art of programming | Scope, LEGB, Lambda Expressions & Anonymous Functions

Session 11: Python |Return Statement, Scope Rules, Global vs. Local Scope, NameError ad more

Effective Learning Techniques: Active Recall, Spaced Repetition, Mind Mapping & More!

S2: Linear Algebra |Decision Boundaries - WtX=0 | Equation of Line, Plane,Hyperplane| Classification

Science-Based Time Management Techniques: Mastering Productivity

S1: Basics of Linear Algebra | Vector Addition, Multiplication, Dot Product | Projections and more

S0: Linear Algebra | Understanding the prerequisites of Linear Algebra: Vectors and Distances Part I

Session 10: Unpacking Arguments | What happens when you call a function | Pass by Value & Reference

Session 9: Function in programming | Different types of parameters | *args and **kwargs | Python

Session 8 | Loops | Advance looping concepts in python

Session 7 | Control Flow | for loop | while loop