Data Science
K-means Clustering: Intro with Math and Python
Learn to implement K-Means clustering in Python using customer data, with explanations to understand how the algorithm works.
Machine Learning
Data Science
Linear Algebra for Machine Learning: A Python Introduction
Linear algebra provides tools to handle and manipulate complex, high-dimensional data effectively. Key applications include data representation, model training, dimensionality reduction, and neural network operations.
Deep Learning
Machine Learning Programming Skills
Learn foundational machine learning concepts and techniques. This tutorial covers Linear Regression, Logistic Regression, Decision Trees, and K-Nearest Neighbors with practical examples and Python code.
Data Science
Building a Foundation in Data Science
This article illustrates the foundation of data science, covering Basic Data Concepts, Data Collection and Cleaning, and Data Visualization, with illustrations for each section.
Data Science
Regression using Random forest on Custom Dataset
Supervised learning involves training a model on a labeled dataset to predict outcomes. Today, we’ll explore this using a Medical Cost dataset.
Machine Learning
Practical Machine Learning Problems
Machine learning faces real-world challenges like data quality, feature selection, and model deployment. This article explores strategies to overcome these issues.
Machine Learning
Classification using Random Forest
Learn supervised learning with Random Forest using the iris dataset. This tutorial covers data preprocessing, model training, and results visualization using Python.
Data Science
Identifying Outliers in Data: A Comprehensive Guide
Learn about Z-score and IQR methods for detecting outliers in data analysis. Understand their workings, strengths, and weaknesses.
Machine Learning
Machine Learning Libraries
Discover key libraries in ML and Deep Learning models, preprocessing and data visualization.
Machine Learning
Reproducible Machine Learning
In the dynamic realm of machine learning, reproducibility is vital for ensuring consistent, trustworthy results. This guide delves into reproducible machine learning, emphasizing the importance of replicating outcomes through precise data, code, and environment management.