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Machine Learning (ML) and Artificial Intelligence (AI) are integral to various industries today, driving significant business results across healthcare, e-commerce, government, transportation, social media, financial services, manufacturing, and more. As ML continues to evolve, the role of a Machine Learning professional becomes crucial, focusing on developing intelligent algorithms that learn from and adapt to data to make accurate predictions. This book offers a hands-on approach to quickly learn ML, emphasizing both theoretical and practical aspects. The first half covers data analysis and visualization in detail, while the second half delves into machine learning and statistical models for data science. Each chapter provides a theoretical framework alongside practical examples to illustrate the techniques discussed. Purchasing this book grants you instant access to related learning materials, including references, PDFs, Python codes, and exercises, all available at no extra cost on the publisher's website. You can download the ML datasets used in the book or access them via the Resources/Datasets folder. Additionally, a short course on Python programming in the second chapter is particularly beneficial for beginners, ensuring you have everything you need to start your learning journey with just a computer and internet access.

Are you looking for a hands-on approach to learn Python coding and Data Analysis quickly? This book serves as a comprehensive guide to fundamental concepts, theory, examples, and hands-on projects related to Data Analysis using Python. It is designed for anyone interested in Data Analysis, featuring crucial definitions, theoretical explanations, and tutorials. Beginners need not worry, as the book provides clear explanations and additional resources throughout. The content is centered around Python, the most widely used programming language today, with dedicated chapters on its application. You'll explore popular libraries for data analysis such as Pandas, Numpy, and Seaborn. The book is structured into 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). Each module includes hands-on projects using real-world datasets. All solutions in the book have been extensively tested for clarity and ease of understanding, making them valuable references as you learn new skills. The primary goal is to help beginners overcome initial obstacles in learning Python and Data Analysis. By isolating concepts, the book allows newcomers to gradually build competency before tackling projects. Ready to start analyzing your data with Python? Click the BUY button to begin your journey!