Glossary of common Machine Learning, Statistics and Data Science terms

Glossary of common Machine Learning, Statistics and Data Science terms – Analytics Vidhya Analytics Vidhya is used by many people as their first source of knowledge. Hence, we created a glossary of common Machine Learning and Statistics terms commonly used in the industry. In the coming days, we will add more terms related to data Read more about Glossary of common Machine Learning, Statistics and Data Science terms[…]

Feature Engineering vs Feature Selection

Feature Engineering vs Feature Selection All machine learning workflows depend on feature engineering and feature selection. However, they are often erroneously equated by the data science and machine learning communities. Although they share some overlap, these two ideas have different objectives. Knowing these distinct goals can tremendously improve your data science workflow and pipelines.

Data Science Primer

Data Science and Machine Learning Primer | EliteDataScience.com (7 Chapters) Gentle introduction to data science and machine learning. We start with a bird’s-eye-view of the entire ML workflow and then walk you through exploring your dataset, cleaning it, engineering features, choosing the best ML algorithm, and of course training a kickass model.

How to Prevent Overfitting in Machine Learning

Overfitting in Machine Learning: What It Is and How to Prevent It Did you know that there’s one mistake… …that thousands of data science beginners unknowingly commit? And that this mistake can single-handedly ruin your machine learning model? No, that’s not an exaggeration. We’re talking about one of the trickiest obstacles in applied machine learning: Read more about How to Prevent Overfitting in Machine Learning[…]

Strengths and Weaknesses of Modern Machine Learning Algorithms

Modern Machine Learning Algorithms: Strengths and Weaknesses In this guide, we’ll take a practical, concise tour through modern machine learning algorithms. While other such lists exist, they don’t really explain the practical tradeoffs of each algorithm, which we hope to do here. We’ll discuss the advantages and disadvantages of each algorithm based on our experience.