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Python Coding and Python for Data Analysis for Beginners with Hands-On Projects  Are you looking for a hands-on approach to learn Python coding and Python for Data Analysis fast? Do you need to start learning Python coding and Data Analysis from Scratch? This book is for you. This book works as guide to present fundamental concepts, theory, examples and hands-on project related to Data Analysis using Python. This book is for anyone interested in Data Analysis. It contains crucial definitions, theoretical explanations, presentation of tools, as well as direct examples and tutorials. If you feel uncomfortable with any of those topics, do not worry. An explanation for beginners will always be performed along this book, as well as further sources. The language utilized on this book is Python, and there are some chapters dedicated to the language applied. The book will achieve this by not only having an in-depth theoretical and analytical explanation of all concepts but also including 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 analysis such as Pandas, Numpy, Seaborn, etc . While we will focus more on the techniques normally used in basic Python Programming, we will also explain, in-details, all the Data Analysis Libraries used in any data science project. What this book offers...  You will learn all about python in three modules, one for Python Coding, one for Data Manipulation and Preprocessing (Numpy and Pandas), and a final one for Data Visualization (Pandas, Matplotlib, Seaborn and Bokeh). All three modules will contain 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 a new skill. What this book aims to do...  This book is written with one goal in mind – to help beginners overcome their initial obstacles to learning Python and Python for data analysis. A lot of times, newbies tend to feel intimidated by coding and data. The goal of this book is to isolate the different concepts so that beginners can gradually gain competency in the fundamentals of Python before working on a project at the end of the chapter. Beginners in Python coding and Data Science does not have to be scary or frustrating when you take one step at a time. Ready to start practicing and analyze your data using Python? Click the BUY button now to download this book Topics Covered: Introduction to Data Analysis? Python for Data Analysis - Basics Python for Data Analysis - Advanced . IPython and Jupyter Notebooks Numpy for Numerical Data Processing Pandas for Data Manipulation Data Visualization ..and more... Click the BUY button and download the book now to start learning and coding Python for Data Analysis.

Python Machine Learning for BeginnersMachine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that's right. Based on a significant amount of data and evidence, it's obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it's healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing and salesYou name it. The list goes on. There's no doubt that ML is going to play a decisive role in every domain in the future. But what does a Machine Learning professional do?A Machine Learning specialist develops intelligent algorithms that learn from data and also adapt to the data quickly. Then, these high-end algorithms make accurate predictions. Python Machine Learning for Beginners presents you with a hands-on approach to learn ML fast. How Is This Book Different?AI Publishing strongly believes in learning by doing methodology. With this in mind, we have crafted this book with care. You will find that the emphasis on the theoretical aspects of machine learning is equal to the emphasis on the practical aspects of the subject matter. You'll learn about data analysis and visualization in great detail in the first half of the book. Then, in the second half, you'll learn about machine learning and statistical models for data science. Each chapter presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques. When you buy this book, your learning journey becomes so much easier. The reason is you get instant access to all the related learning material presented with this book--references, PDFs, Python codes, and exercises--on the publisher's website. All this material is available to you at no extra cost. You can download the ML datasets used in this book at runtime, or you can access them via the Resources/Datasets folder. You'll also find the short course on Python programming in the second chapter immensely useful, especially if you are new to Python. Since this book gives you access to all the Python codes and datasets, you only need access to a computer with the internet to get started