Linear and Logistic Regressions with Python for Beginners with Hands-On Projects
Are you looking for a hands-on approach to learn Regression fast? Or perhaps you have just completed a Data Science or Python course and are looking for data science models?
Do you need to start learning Logistic and Linear Regression from Scratch?
This book is for you.
This book will give you the chance to have a fundamental understanding of regression analysis, which is needed for any data scientist or machine learning engineer.
The book will achieve this by not only having an in-depth theoretical and analytical explanation of all concepts but also including dozens of hands-on, real-life projects that will help you understand the concepts better.
We will start by digging into Python programming as all the projects are developed using it, and it is currently the most used programming language in the world. We will also explore the most-famous libraries for data science such as Pandas, SciPy, Sklearn, and Statsmodel.
Then, we will start seeing how we can preprocess, prepare, and visualize the data, as these steps are crucial for any data science project and can take up to 80 percent of the project time. While we will focus more on the techniques normally used in regression analysis, we will also explain, in-details, all the techniques used in any data science project.
What this book offers…
You will learn all about regression analysis in three modules, one for simple linear regression, one for multiple regression, and a final one for logistic regression. All three modules will contain many hands-on projects using real-world datasets.
Clear and Easy to Understand Solutions
All solutions in this book are extensively tested by a group of beta readers. The solutions provided are simplified as much as possible so that they can serve as examples for you to refer to when you are learning new skills.
What this book aims to do…
This book is written with one goal in mind – to help beginners overcome their initial obstacles to learning data science and Artificial Intelligence.
A lot of times, newbies tend to feel intimidated by Data Science and AI.
The goal of this book is to isolate the different concepts so that beginners can gradually gain competency in the fundamentals of regression before working on a project at the end of the chapter.
Beginners in Data Science does not have to be scary or frustrating when you take one step at a time.
Ready to start practicing and building your Regression Models? Click the BUY button now to download this book
- What is Regression and When to Use It?
- Using Python for Regression Analysis
- Data Preparation
- Simple Linear Regression
- Correlation Analysis
- Multiple Linear Regression
- Hands-On Project
- ..and more…
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