Geeky Codes | AI Engineering, Data Engineering & Machine Learning Tutorials
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 as hard voting) to aggregate the predictions of all predictors in an ensemble, why don’t we train a model to perform this aggregation? Figure below … Read more
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 a nutshell, LLE works by first measuring how each training instance linearly relates to its closest neighbors (c.n.), and then looking for a low-dimensional representation … Read more
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 projects that will help you gain hands-on experience with full-stack development. Each project leverages modern technologies and includes step-by-step instructions to guide you through the … Read more
Introduction Learn how Retrieval-Augmented Generation (RAG) works by building an end-to-end RAG pipeline with Llama 2, LangChain, Hugging Face embeddings, and ChromaDB — and understand how this early architecture compares with modern production RAG systems. Originally published: August 2, 2024Updated: September 2026 Introduction Large Language Models (LLMs) can answer questions, summarize text, generate code, and … Read more
Learn how XGBoost works from decision trees and boosting intuition to gradient boosting, regularization, hyperparameter tuning, SHAP explainability, and production deployment. Includes practical Python examples for classification and regression. Originally published: April 18, 2024Updated: 3rd September 2026 Introduction If you have worked with tabular machine learning problems, you have probably encountered XGBoost. XGBoost, short for … Read more
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 the collective intelligence of multiple weak learners to create a robust and accurate predictive model. In this tutorial, we’ll embark on a journey to explore … Read more
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 this, Idea is that we want to reduce the value of coefficient(m). By doing this biasness can increase but variance decreases. Which is called bias … Read more
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 Exploratory Data Analysis and Feature Engineering. A lot of people start Kaggle with this competition and they get lost in extremely long tutorial kernels. This is a short kernel compared … Read more
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 of features can significantly influence model performance, mastering the art of feature engineering is indispensable. In this comprehensive guide, we’ll embark on a Titanic journey … Read more
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 an incredible amount of freedom and can fit a huge variety of complex datasets. But this great flexibility also means that it is prone to … Read more
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 or sorting emails, understanding how to tailor MLP architectures and activations is pivotal for optimizing performance. Regression MLPs Crafting an MLP architecture for regression tasks … Read more
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). The layers close to the input layer are usually called the lower layers, and the ones close to the outputs are usually called the upper … Read more
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 generated from a single Gaussian distribution form a cluster that typically looks like an ellipsoid. Each cluster can have a different ellipsoidal shape, size, density … Read more
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 majority of the available data is actually unlabeled: we have the input features X, but we do not have the labels y. Yann LeCun famously … Read more
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 error minimization.
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 clusters. The document also explores various linkage methods, dendrograms for visualization, and the validity of clusters over time.
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 the benefits of compression on datasets like MNIST. Additionally, it introduces Randomized PCA and Incremental PCA for efficiency in handling large datasets.
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) is highlighted as a key algorithm for preserving variance while reducing dimensions, with SVD as a method for determining principal components.
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 and risks of overfitting with increased dimensions. Dimensionality reduction techniques will be explored further.
Another very popular Boosting algorithm is Gradient Boosting. Just like AdaBoost,Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. However, instead of tweaking the instance weights at every iteration like AdaBoost does, this method tries to fit the new predictor to the residual errors made by the previous predictor. … Read more