Geeky Codes | AI Engineering, Data Engineering & Machine Learning Tutorials
Stochastic Gradient Descent (SGD) is an optimization algorithm commonly used in machine learning for training models, particularly in large-scale and online learning settings. It is an iterative optimization algorithm that aims to minimize a cost or loss function by adjusting the model parameters. Here’s an overview of how SGD works: Basic Concept: Advantages and Considerations: … Read more
Introduction Quintile analysis is a statistical method used to divide a data set into five equal parts, each representing 20% of the total observations. This method is often used in finance, economics, and sociology to analyze distributions, rankings, or performance. Quintile Analysis: with random data Quintile analysis is a common framework for evaluating the efficacy … Read more
Installation or Setup Detailed instructions on getting pandas set up or installed can be found here in the official documentation. Installing pandas with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way to install not only pandas, but Python and the … Read more
With K-Means, you could use the inertia or the silhouette score to select the appropriate number of clusters, but with Gaussian mixtures, it is not possible to use these metrics because they are not reliable when the clusters are not spherical or have different sizes. Instead, you can try to find the model that minimizes … Read more
Word Level Tokenzation Splitting text into individual words “the quick brown fox” -> [“the”,”quick”,”brown”,”fox”] BUT Character Level Tokenization Splitting text into individual characters “the quick brown fox” -> [“t”,”h”,”e”,” “,”q”,”u”,”i”,”c”,”k”, …] But N-GRAM MODELS Splitting text into groups of consecutive words. N is the no. of words in a token. For a trigram model, “the … Read more
Appending a new row to DataFrame import pandas as pd df = pd.DataFrame(columns = [‘A’, ‘B’, ‘C’]) Appending a row by a single column value: df.loc[0, ‘A’] = 1 df Appending a row, given list of values: df.loc[1] = [2, 3, 4] df Appending a row given a dictionary: df.loc[2] = {‘A’: 3, ‘C’: 9, … Read more
Exploratory Data Analysis (EDA) is an essential step in any data science project. It involves investigating and analyzing datasets to understand their characteristics, identify patterns, detect outliers, and uncover relationships between variables. EDA helps in gaining initial insights into the data before diving into more complex analyses. The Foremost Goals of EDA Types of EDA … Read more
Introduction Anomaly detection (also called outlier detection) is the task of detecting instances that deviate strongly from the norm. These instances are of course called anomalies or outliers, while the normal instances are called inliers. Anomaly detection is very useful in a wide variety of applications, for example in fraud detection, or for detecting defective … Read more
Rather than manually searching for the optimal number of clusters, it is possible to use instead the BayesianGaussianMixture class which is capable of giving weights equal (or close) to zero to unnecessary clusters. Just set the number of clusters n_components to a value that you have good reason to believe is greater than the optimal … Read more
Before we move on to Gaussian mixture models, let’s take a look at DBSCAN, another popular clustering algorithm that illustrates a very different approach based on local density estimation. This approach allows the algorithm to identify clusters of arbitrary shapes. Understanding DBSCAN Clustering Algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular … Read more
Another use case for clustering is in semi-supervised learning, when we have plenty of unlabeled instances and very few labeled instances. Let’s train a logistic regression model on a sample of 50 labeled instances from the digits dataset: n_labeled = 50 log_reg = LogisticRegression() log_reg.fit(X_train[:n_labeled], y_train[:n_labeled]) What is the performance of this model on the … Read more
Image segmentation is the task of partitioning an image into multiple segments. In semantic segmentation, all pixels that are part of the same object type get assigned to the same segment. For example, in a self-driving car’s vision system, all pixels that are part of a pedestrian’s image might be assigned to the “pedestrian” segment … Read more
Introduction Another important improvement to the K-Means algorithm was proposed in a 2003 paper by Charles Elkan. It considerably accelerates the algorithm by avoiding many unnecessary distance calculations: this is achieved by exploiting the triangle inequality (i.e., the straight line is always the shortest) and by keeping track of lower and upper bounds for distances … Read more
Consider the unlabeled dataset represented in Figure below: you can clearly see 5 blobs of instances. The K-Means algorithm is a simple algorithm capable of clustering this kind of dataset very quickly and efficiently, often in just a few iterations. It was proposed by Stuart Lloyd at the Bell Labs in 1957 as a technique … Read more
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
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