This is a data science project practice book. It was initially written for my Big Data course to help students to run a quick data analytical project and to understand 1. the data analytical process, the typical tasks and the methods, techniques and the algorithms need to accomplish these tasks. During convid19, the unicersity has adopted on-line teaching. So the students can not access to the university labs and HPC facilities. Gaining an experience of doing a data science project becomes individual students self-learning in isolation. This book aimed to help them to read through it and follow instructions to complete the sample propject by themslef. However, it is required by many other students who want to know about data analytics, machine learning and particularly practical issues, to gain experience and confidence of doing data analysis. So it is aimed for beginners and have no much knowledge of data Science. the format for this book is bookdown::gitbook.
overfitting - Relation between underfitting vs high bias and
4.4. Model Selection, Underfitting, and Overfitting — Dive into
6.1 Predictive Data Analysis (PDA) Do A Data Science Project in 10 Days
Chapter 5 Data Preparasion Do A Data Science Project in 10 Days
Overfitting and underfitting in machine learning
How to Handle Overfitting and Underfitting in Machine Learning
Underfitting & Overfitting — The Thwarts of Machine Learning
7.4 PCA Analysis Do A Data Science Project in 10 Days
2.3 Bootsup your RStudio Do A Data Science Project in 10 Days
Overfitting and Underfitting. In Machine Leaning, model
Overfitting vs. Underfitting: What Is the Difference?
Identify the Problems of Overfitting and Underfitting - Improve
Supervised Learning
4.1 Load Data Do A Data Science Project in 10 Days