Code in a Better Way
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…
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…
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…
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…
Word Level Tokenzation Splitting text into individual words “the quick brown fox” -> BUT Character Level Tokenization Splitting text into individual characters “the quick brown fox” -> But N-GRAM MODELS…
Appending a new row to DataFrame import pandas as pd df = pd.DataFrame(columns = ) Appending a row by a single column value: df.loc = 1 df Appending a row,…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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 (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…
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…
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…
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…