time steps and hidden state (MEMORY) in RNNs

Recurrent Neural Nets - weights and biases

Representing sequences in RNN models

Idea of recurrence in RNN models

Synopsis: MLPs for Text Classification

Momentum: A Key Component in Modern Optimization Algorithms

Z-Test: A Key Tool in Inferential Statistics

Applying the Runs Test for Randomness in Data Sequences

Statistical Significance with Ranked Data: The Sign Test Approach

Beyond Chi-Square: When to Employ Fisher's Exact Test

Expected vs. Observed: Using Chi-Square to Analyze Data Patterns

ANOVA (Analysis of Variance) Explained

Beyond the T-Test: Exploring Non-Parametric Alternatives

Mastering the T-Test: A Comprehensive Guide for Data Scientists

Mastering Inferential Statistics for Data Science: A Comprehensive Guide

Enhancing Nearest Neighbor Search: Overcoming Brute Force Limitations with KD Trees and Ball Trees

Model persistence : Saving and loading the trained model (KNN)

K-Nearest Neighbors (KNN) for Regression: A Simple Yet Effective Approach

Enhancing KNN Performance: A Deep Dive into Hyperparameter Optimization

Precision, Recall, PR curves and Learning curves: The Cornerstones of Model Evaluation

Integrating Categorical Features in KNN

Feature Scaling : A Deep Dive into KNN Optimization

Tie Situation Strategies for Optimal KNN Predictions

KNN Precision: Choosing the Optimal 'k' for Your Dataset

K-Nearest Neighbors: Data Proximity for Effective Predictions

From Geometry to Data Science: Basic distance and similarities measures

From Supervised to Unsupervised Models: A Comprehensive ML Journey

Exploring Statistical Measures: Kurtosis, Skewness, and Symmetry

Data Distributions: A Comprehensive Guide, with hands on python code

From Basics to Code: Exploring Data Dispersion Measures

Central Measures in Data: From Basics to Winsorizing

Exploring Simple and Advanced Sampling Strategies: with Python Demos

Foundations of Statistical Understanding : Data Types, Tables, and Feature Types Explored

Essential stats for DS : why stats and maths - overview

Enhancing ML Model Generalization: Best Practices in Data Splitting

Overcoming Data Imbalance: The Role of SMOTE in Machine Learning

Practical Guide to Implementing Data Scaling Techniques in Python

A Hands-On Exploration of Data Encoding Methods

Outlier & Cardinality assessment : Python Code Demos and Strategies

Missing values (Part 2) : Nearest Neighbor-Based Interpolation with scikit-learn

Beyond NaN: Understanding and Tackling Missing Data in Python - part 1

Numeric Insights: Basic sanity check for Data Analysis

Making sense of DATA for ML/DL modeling

Python - Essential Components for Data Science/ML Projects

Machine Learning: Concepts, Models, and Platform Insights

Generating Data with Gaussian Naive Bayes: from concepts to implementation

Handling Massive Datasets: Out-of-Core Multinomial Naive Bayes Approach

From Words to Documents: Understanding Doc2Vec with Gensim

Optimizing Word Vector Generation: A Gensim Word2Vec Approach with Negative Sampling

Hands-on guide on Word2Vec: Gensim's Hierarchical Softmax Strategies

Transforming Words into Vectors: Hands on with Gensim's Word2Vec

Understanding Text Embedding Layers in Keras/TensorFlow

Constructing Training Set for Word Embedding Algorithms

From Words to Vectors : Understanding Word2Vec Embeddings

Beyond TF-IDF: Exploring BM25 for Enhanced Document Ranking and Vectorization

IMDB Sentiment Analysis : Hashing Vectorizer with Online SGD Classifier

Continuous ML model training: with TF-IDF Vectorizer

TF-IDF Explained clearly: The Key to Textual Understanding

Mastering Bag of Words and Count Vectorization

Decoding Language: Byte Pair Encoding in Large Language Models and Generative AI