Machine Learning
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7 Powerful Steps to Build a CNN Image Classifier in PyTorch
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Introduction CNN for Image Classification is an important application of deep learning that enables computers to recognize and classify objects in images. In this project, we build a Convolutional…
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How to perform Feature Selection in Machine Learning – Comparing SHAP, Feature Importance and RFECV
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Feature Selection in Machine Learning: Comparing SHAP, Feature Importance and RFECV Feature selection is an important step in machine learning because having more features does not automatically mean having…
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Mastering Exploratory Data Analysis (EDA) with Python and Pandas: An E-Commerce Case Study
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Data in its raw form is rarely ready for decision-making. It arrives messy, incomplete, and full of hidden anomalies. Before building predictive machine learning models or deploying dashboards, data…
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Finding The Best Supervised and Unsupervised Machine Learning Models
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Machine learning models involves choosing the right approach for a particular problem and evaluating whether the selected model performs reliably. As part of my Data Science internship at Valentius…
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What is Supervised Machine Learning?
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Machine learning is one of the most important areas of modern artificial intelligence (AI). It allows computers to learn patterns from data and make predictions or decisions without being…
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Supervised Learning: Comparing 5 Machine Learning Classification Algorithms
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Introduction Supervised Learning is one of the most widely used approaches in machine learning for solving classification and prediction problems. In this project, I explored supervised learning by comparing…
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What is Machine learning? Regression from scratch
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Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data, identify patterns, and make predictions without being explicitly programmed. By utilizing algorithms rooted…
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Tuned Ensemble Pipeline – From Baseline Model to an Optimized Machine Learning Solution
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Milestone: Tuned Ensemble Pipeline 🚀 Excited to share that I’ve completed the “Tuned Ensemble Pipeline” as part of my internship at @Valentius Kryptix! In this project, I: Built a…
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Comparing Ensemble Learning Benchmarks: Random Forest vs Gradient Boosting vs XGBoost Model/Algorithms
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Random Forest primarily reduces variance by training multiple decision trees independently on different bootstrap samples and combining their predictions. This makes the model more stable and less sensitive to…
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Machine Learning Model Comparison: Supervised and Unsupervised Learning on the Titanic Dataset
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Introduction Machine Learning offers several approaches for solving prediction and pattern-discovery problems. Selecting the right algorithm is important because different algorithms can behave differently on the same dataset. In…