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PCA in Python: Understanding Principal Component Analysis

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Principal Component Analysis (PCA) is a cornerstone technique in data analysis, machine learning, and artificial intelligence, offering a systematic approach to handle high-dimensional datasets by reducing complexity. By distilling data into uncorrelated dimensions called principal components, PCA retains essential information while mitigating dimensionality effects. With diverse applications including dimensionality reduction, feature selection, data compression, and […]

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How to Calculate R-Squared in Python (SkLearn and SciPy)

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Welcome to our exploration of R-squared (R2), a powerful metric in statistics that assesses the goodness of fit in regression models. R2 represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s). In this post, we’ll guide you through the essentials of R2 and demonstrate how to calculate

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One-Hot Encoding in Machine Learning with Python

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Feature engineering is an essential part of machine learning and deep learning and one-hot encoding is one of the most important ways to transform your data’s features. This guide will teach you all you need about one hot encoding in machine learning using Python. You’ll learn grasp not only the “what” and “why”, but also

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Python Optuna: A Guide to Hyperparameter Optimization

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In this complete guide, you’ll learn how to use the Python Optuna library for hyperparameter optimization in machine learning. In this blog post, we’ll dive into the world of Optuna and explore its various features, from basic optimization techniques to advanced pruning strategies, feature selection, and tracking experiment performance. You’ll also learn how to visualize

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Support Vector Machines (SVM) in Python with Sklearn

2 Comments / Machine Learning, Python Posts, Scikit-Learn /

In this tutorial, you’ll learn about Support Vector Machines (or SVM) and how they are implemented in Python using Sklearn. The support vector machine algorithm is a supervised machine learning algorithm that is often used for classification problems, though it can also be applied to regression problems. This tutorial assumes no prior knowledge of the

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One-Hot Encoding in Scikit-Learn with OneHotEncoder

4 Comments / Machine Learning, Python Posts, Scikit-Learn /

In this tutorial, you’ll learn how to use the OneHotEncoder class in Scikit-Learn to one hot encode your categorical data in sklearn. One-hot encoding is a process by which categorical data (such as nominal data) are converted into numerical features of a dataset. This is often a required preprocessing step since machine learning models require

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Hyper-parameter Tuning with GridSearchCV in Sklearn

4 Comments / Machine Learning, Python Posts, Scikit-Learn /

In this tutorial, you’ll learn how to use GridSearchCV for hyper-parameter tuning in machine learning. In machine learning, you train models on a dataset and select the best performing model. One of the tools available to you in your search for the best model is Scikit-Learn’s GridSearchCV class. By the end of this tutorial, you’ll

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