What is Clustering? A Simple Approach
As you enjoy a hike in the mountains, you stumble upon a plant you have never seen before. You look around and you notice a few more. They are not…
What is Stacking of Models in Machine Learning?
The last Ensemble method we will discuss in this series is called stacking (short for stacked generalization). It is based on a simple idea: instead of using trivial functions (such…
Locally linear Embedding For Dimensionality Reduction in Machine Learning
Locally Linear Embedding (LLE) is another very powerful nonlinear dimensionality reduction (NLDR) technique. It is a Manifold Learning technique that does not rely on projections like the previous algorithms. In…
Web Development Project Guide for Freshers
Web Development Project Guide for Freshers Starting your journey as a web developer can be both exciting and challenging. To make the process smoother, here’s a detailed guide to beginner-friendly…
RAG using Llama 2, Langchain and ChromaDB
Introduction Objective Use Llama 2.0, Langchain and ChromaDB to create a Retrieval Augmented Generation (RAG) system. This will allow us to ask questions about our documents (that were not included…
XGBoost: A Comprehensive Tutorial
Introduction: In the realm of machine learning algorithms, XGBoost stands tall as a powerhouse, renowned for its efficiency, effectiveness, and versatility. This tutorial aims to provide a thorough understanding of…
Ensemble Learning: A Comprehensive Guide to AdaBoost and Gradient Boosting
Introduction: In the realm of machine learning, ensemble learning techniques such as AdaBoost and Gradient Boosting have revolutionized the way we approach classification and regression tasks. These powerful algorithms harness…
Implementing Ridge Regression from Scratch
For Linear Regression in Machine learning with two variables we have to find 2 coefficient. In case of overfitting these 2 coefficients can be very high. Y=mX+c So to handle…
Titanic – Advanced Feature Engineering Tutorial
Introduction I decided to write this kernel because Titanic: Machine Learning from Disaster is one of my favorite competitions on Kaggle. This is a beginner level kernel which focuses on…
Feature Engineering in Machine Learning
Introduction Feature engineering, often hailed as the cornerstone of machine learning, holds the power to transform raw data into actionable insights. In the realm of predictive modeling, where the quality…
Avoiding Overfitting Through Regularization
With thousands of parameters you can fit the whole zoo. Deep neural networks typically have tens of thousands of parameters, sometimes even millions. With so many parameters, the network has…
Regression and Classification Multi Layer Perceptrons
Introduction In the dynamic landscape of machine learning, Multilayer Perceptrons (MLPs) emerge as formidable tools capable of handling both regression and classification tasks with finesse. Whether you’re predicting housing prices…
Multi-Layer Perceptron and Backpropagation | Deep Learning
An MLP is composed of one (passthrough) input layer, one or more layers of TLUs, called hidden layers, and one final layer of TLUs called the output layer (seeFigure below).…
Gaussian Mixtures Models in Machine Learning
A Gaussian mixture model (GMM) is a probabilistic model that assumes that the instances were generated from a mixture of several Gaussian distributions whose parameters are unknown. All the instances…
Introduction to Unsupervised Learning Techniques
Introduction Although most of the applications of Machine Learning today are based on supervised learning (and as a result, this is where most of the investments go to), the vast…
Kernel PCA in Machine Learning
The post discusses Kernel Principal Component Analysis (kPCA), highlighting its application in nonlinear dimensionality reduction and suggesting methods for selecting kernels and tuning hyperparameters through grid search and reconstruction pre-image…
Hierarchical Clustering in Machine Learning
The content discusses K-Means and Hierarchical Clustering algorithms. K-Means requires predefined clusters and is sensitive to initial centroids and outliers. Hierarchical Clustering offers an agglomerative and divisive approach without preset…
Choosing the Right Number of Dimensions in Dimensionality Reduction
The content discusses dimensionality reduction using PCA, emphasizing the importance of preserving a significant portion of variance, typically 95%. It explains how to compute PCA, options for variance preservation, and…
Main Approaches for Dimensionality Reduction
This content discusses dimensionality reduction approaches, focusing on projection and Manifold Learning. It explains how projection simplifies high-dimensional data, exemplified by datasets like the Swiss roll. Principal Component Analysis (PCA)…
Introduction to Dimensionality Reduction
The text discusses the curse of dimensionality in machine learning, highlighting challenges in high-dimensional spaces. It suggests reducing features to improve training efficiency and visualization, while addressing potential information loss…