Machine Learning

Train-Test Split Method

Label Encoding Process

One-Hot Encoding

Linear Regression with OLS

Evaluation Metrics for Regression

Logistic Regression

K-Nearest Neighbors

Confusion Matrix Analysis

ROC Curve and AUC

Decision Tree Analysis

Random Forest and Bagging

Gradient Boosting

Naive Bayes Classifier

Support Vector Machines

K-Means Clustering

Cosine Similarity Metric

K-Fold Cross Validation

Bias vs Variance

Ridge and Lasso Regression

Label Imbalance Problem

Training on Imbalanced Dataset

Hard vs Soft Labelling

Sliding Window for Time Series

Model and Data Drift

Deep Learning

Perceptron Classifier

Weights & Biases

Activation Functions in DL

Neural Network Visualizer

How Loss is Calculated

Softmax and Cross-Entropy

Gradient Descent

Linear Regression with Gradient Descent

Optimizers in Neural Networks

Optimizers in 3D

Learning Rate Scheduling

Batch Processing in Networks

Gradient Descent Batch Processing

Feature Scaling & Weight Bias

Weight Initialization Methods

Vanishing & Exploding Gradients

Batch Normalization in Deep Networks

Overfitting vs Underfitting

Model Training Curves

Dropout Regularization

Regularization Techniques

Early Stopping

Hyperparameter Tuning

Data Sparsity

Neural Network for Regression

Neural Network for Unsupervised Learning

Model Reproducibility

Model Training on CPU vs GPU

Algorithms

Big-O Notation and Time Complexity

Python List Lab

Python String Lab

Python Dictionary Lab

Linear Search

Binary Search

Interpolation Search

Fibonacci Search

Bubble Sort

Selection Sort

Insertion Sort

Recursion and the Call Stack

Divide and Conquer

Merge Sort

Quick Sort

Counting Sort

Radix Sort

Stacks (LIFO)

Queues (FIFO)

Linked Lists

Hash Tables and Hashing

Binary Search Trees

Heaps and Priority Queues

Heap Sort

Trie (Prefix Tree)

Union-Find (Disjoint Set)

Graph Representations

Breadth First Search

Depth First Search

Cycle Detection

Topological Sort

Dijkstra's Algorithm

Bellman-Ford and Negative Weights

A* Pathfinding Algorithm

Minimum Spanning Tree

Two Pointers

Sliding Window

Greedy Algorithms

Backtracking Search Method

Dynamic Programming

KMP String Matching

NLP

ASCII Character Codes

NLP Tokenizer

Text Normalization Pipeline

Stemming vs Lemmatization

N-gram Explainer

Word Cloud Generator

Why Text Encoding is Needed in NLP

Text Encoding Techniques in NLP

How Words are Represented in Neural Networks

What are Embeddings?

How are Embeddings Generated?

How Neural Networks Process Text

What is a Sequence?

Sequential Data Preparation with Sliding Window

Normalization Techniques for Sequential Data

Limitations of ANN with Sequential Data

What is a Recurrent Cell?

How RNN Processes Text

RNN Architecture

Interactive 3D RNN Visualizer

Backpropagation Through Time

Vanishing Gradient Problem in RNN

What is LSTM?

Forget Gate in LSTM

Input Gate in LSTM

Candidate Memory in LSTM

Output Gate in LSTM

How LSTM Processes Text

What is a GRU?

What is a Bidirectional Layer?

The Attention Mechanism

Query, Key and Value

Self-Attention

Multi-Head Attention

Positional Encoding

The Transformer Architecture

Computer Vision

How Neural Networks Process Images

Grayscale Image Processing

RGB Image Processing

Real-time Edge Detection

Convolutional Layer

Padding in CNN

Strides in CNN

Parameter Sharing in CNN

ReLU Activation in CNN

Pooling Layer

Fully Connected Layer in CNN

Calculating Parameters in CNN

CNN Architecture

Data Loaders in CNN

Image Data Augmentation

Transfer Learning with CNN

Database

What are Relational Databases?

What are Non Relational Databases?

Datatypes in SQL

DDL in SQL

DML in SQL

Where Clause in SQL

Limit and Offset in SQL

SQL GroupBy Visualizer

SQL Joins Visualizer

Common Table Expressions in SQL

SQL Window Functions

Regular Expressions in SQL

Gen AI

How LLMs Process Text?

Byte Pair Encoding Tokenizer

How LLMs Predict the Next Word?

Causal Language Modeling

What is Masked Language Modeling?

Quantization in LLMs

LoRA in LLMs

Knowledge Distillation in LLMs

Maths

Equation of a Line (y = mx + c)

Logarithms

Exponentials

Vectors and the Dot Product

Vector Norms

Distance Metrics

Matrix Multiplication

A Matrix is a Transformation

Eigenvalues and Eigenvectors

Derivatives and Slope

The Chain Rule

Partial Derivatives and the Gradient

Mean, Mode and Median

Mean, Variance and Standard Deviation

Covariance and Correlation

Probability Basics

Conditional Probability

Bayes' Theorem

The Normal Distribution

Maximum Likelihood Estimation

Entropy and Information