Regression Models With Python For Beginners Theory And Applications Of Linear Models And Logistic Model With Python From Scratch
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Regression Models With Python For Beginners: Theory and Applications of Linear Models and Logistic Model with Python from Scratch
Linear and Logistic Regressions with Python for Beginners with Hands-On ProjectsAre 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 SolutionsAll 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 a 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 bookTopics Covered: 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... Click the BUY button and download the book now to start learning and practicing Regression with Python.** MONEY BACK GUARANTEE BY AMAZON **If you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform or contact us by sending an email at [email protected].
Learn Data Science from Scratch
Author: Pratheerth Padman
language: en
Publisher: BPB Publications
Release Date: 2024-02-15
Turn raw data into meaningful solutions KEY FEATURES ● Complete guide to master data science basics. ● Practical and hands-on examples in ML, deep learning, and NLP. ● Drive innovation and improve decision making through the power of data. DESCRIPTION Learn Data Science from Scratch equips you with the essential tools and techniques, from Python libraries to machine learning algorithms, to tackle real-world problems and make informed decisions. This book provides a thorough exploration of essential data science concepts, tools, and techniques. Starting with the fundamentals of data science, you will progress through data collection, web scraping, data exploration and visualization, and data cleaning and pre-processing. You will build the required foundation in statistics and probability before diving into machine learning algorithms, deep learning, natural language processing, recommender systems, and data storage systems. With hands-on examples and practical advice, each chapter offers valuable insights and key takeaways, empowering you to master the art of data-driven decision making. By the end of this book, you will be well-equipped with the essential skills and knowledge to navigate the exciting world of data science. You will be able to collect, analyze, and interpret data, build and evaluate machine learning models, and effectively communicate your findings, making you a valuable asset in any data-driven environment. WHAT YOU WILL LEARN ● Master key data science tools like Python, NumPy, Pandas, and more. ● Build a strong foundation in statistics and probability for data analysis. ● Learn and apply machine learning, from regression to deep learning. ● Expertise in NLP and recommender systems for advanced analytics. ● End-to-end data project from data collection to model deployment, with planning and execution. WHO THIS BOOK IS FOR This book is ideal for beginners with a basic understanding of programming, particularly in Python, and a foundational knowledge of mathematics. It is well-suited for aspiring data scientists and analysts. TABLE OF CONTENTS 1. Unraveling the Data Science Universe: An Introduction 2. Essential Python Libraries and Tools for Data Science 3. Statistics and Probability Essentials for Data Science 4. Data Mining Expedition: Web Scraping and Data Collection Techniques 5. Painting with Data: Exploration and Visualization 6. Data Alchemy: Cleaning and Preprocessing Raw Data 7. Machine Learning Magic: An Introduction to Predictive Modeling 8. Exploring Regression: Linear, Logistic, and Advanced Methods 9. Unveiling Patterns with k-Nearest Neighbors and Naïve Bayes 10. Exploring Tree-Based Models: Decision Trees to Gradient Boosting 11. Support Vector Machines: Simplifying Complexity 12. Dimensionality Reduction: From PCA to Advanced Methods 13. Unlocking Unsupervised Learning 14. The Essence of Neural Networks and Deep Learning 15. Word Play: Text Analytics and Natural Language Processing 16. Crafting Recommender Systems 17. Data Storage Mastery: Databases and Efficient Data Management 18. Data Science in Action: A Comprehensive End-to-end Project
Linear Models with Python
Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. ... It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. -Biometrical Journal Throughout, it gives plenty of insight ... with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosen...I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. -Journal of the Royal Statistical Society Like its widely praised, best-selling companion version, Linear Models with R, this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference and prediction to missing data, factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features: Python is a powerful, open source programming language increasingly being used in data science, machine learning and computer science. Python and R are similar, but R was designed for statistics, while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics, including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection, Shrinkage, Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners. Linear Models with Python explains how to use linear models in physical science, engineering, social science and business applications. It is ideal as a textbook for linear models or linear regression courses.