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The Statistics and Machine Learning with R Workshop

The Statistics and Machine Learning with R Workshop

By : Liu Peng
4.6 (5)
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The Statistics and Machine Learning with R Workshop

The Statistics and Machine Learning with R Workshop

4.6 (5)
By: Liu Peng

Overview of this book

The Statistics and Machine Learning with R Workshop is a comprehensive resource packed with insights into statistics and machine learning, along with a deep dive into R libraries. The learning experience is further enhanced by practical examples and hands-on exercises that provide explanations of key concepts. Starting with the fundamentals, you’ll explore the complete model development process, covering everything from data pre-processing to model development. In addition to machine learning, you’ll also delve into R's statistical capabilities, learning to manipulate various data types and tackle complex mathematical challenges from algebra and calculus to probability and Bayesian statistics. You’ll discover linear regression techniques and more advanced statistical methodologies to hone your skills and advance your career. By the end of this book, you'll have a robust foundational understanding of statistics and machine learning. You’ll also be proficient in using R's extensive libraries for tasks such as data processing and model training and be well-equipped to leverage the full potential of R in your future projects.
Table of Contents (20 chapters)
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1
Part 1:Statistics Essentials
8
Part 2:Fundamentals of Linear Algebra and Calculus in R
12
Part 3:Fundamentals of Mathematical Statistics in R

Summary

In this chapter, we delved into the world of logistic regression, its theoretical underpinnings, and its practical applications. We started by exploring the fundamental construct of logistic regression and its comparison with linear regression. We then introduced the concept of the sigmoid transformation, a crucial element in logistic regression, which ensures the output of our model is bounded between 0 and 1. This section helped us better understand the advantages of logistic regression for binary classification tasks.

Next, we delved into the concept of log odds and odds ratio, two critical components of the logistic regression model. Understanding these allowed us to comprehend the real-world implications of the model’s predictions and to interpret its parameters effectively. The chapter then introduced the CEL, the cost function used in logistic regression. Specifically, we discussed how this loss function ensures our model learns to predict accurate probabilities...

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The Statistics and Machine Learning with R Workshop
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