Bias-Variance Tradeoff
cm1The article examines the trade-off between bias and variance in machine learning. It provides strategies and examples to achieve optimal model performance by minimizing errors and improving accuracy.
Understanding Classification Metrics: Part 1
This blog post introduces the essential metrics for evaluating classification models, including accuracy and confusion matrix, with practical examples to illustrate their importance.
Understanding Classification Metrics: Part 2
In this second part of the series, I provide a detailed analysis of evaluation metrics for classification models, covering precision, recall, and F1-score to help improve machine-learning models.
Understanding Classification Metrics: Part 3
In my final series on classification metrics, I cover advanced evaluation metrics such as ROC-AUC, precision-recall curves, and specificity metrics. this post offers practical insights for evaluating model performance.
What is Perceptron
In this blog post, I explore the basics of the Perceptron, a foundational component of neural networks. I discuss how it works, its mathematical formulation, and its role in machine learning and foundation of Neural Networks.
Handling Missing Data: A Detailed Guide (Part 1)
In this series, I cover the significance of addressing missing data, types of missing data, and initial strategies to manage them, providing a solid foundation for data preprocessing.
Handling Missing Data: A Detailed Guide (Part 2)
In this blog post, I discuss advanced techniques for handling missing data in datasets, including imputation methods and practical strategies for different data types that can improve data preprocessing skills.
Best Practices for Data Cleaning: Polishing Data for Optimal Results
In this blog post, I outline essential best practices for data cleaning, covering techniques to identify and correct errors, remove inconsistencies, and prepare datasets for accurate analysis and modeling.
Hands-On Data Cleaning Using Pandas: Transforming Phone Data for Analysis
In this practical guide, I demonstrate how to use Pandas for data cleaning, focusing on transforming phone data. using best practices data wrangling with Pandas.