MASTER PYTHON DATA SCIENCE With AI Virtual Tutoring* : From Fundamentals to Advanced Applications Edition 2024
- Length: 161 pages
- Edition: 1
- Language: English
- Publication Date: 2024-07-21
- ISBN-10: B0D9V4LKTT
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Discover “Master Python: Data Science – From Fundamentals to Advanced Applications with AI Virtual Tutoring,” the essential guide for professionals and enthusiasts who wish to master data science with Python. This innovative manual, written by Diego Rodrigues, an author with over 140 titles published in six languages, combines high-quality content with the advanced technology of IAGO, a virtual tutor developed and hosted on the OpenAI platform.
The book begins with a comprehensive introduction to data science, highlighting the importance of the field and the crucial role Python plays. Next, it covers the fundamentals of Python, including basic syntax, data structures, and control flow, laying a solid foundation for subsequent chapters.
You will learn essential data manipulation and cleaning techniques using libraries like Pandas and NumPy, ensuring your data is ready for analysis. Then, you will explore exploratory data analysis (EDA) with tools like Matplotlib and Seaborn to discover valuable patterns and insights. Data visualization is deepened with the use of Plotly to create interactive charts and Dash to develop dynamic dashboards.
The book progresses to machine learning, introducing basic concepts and types of learning, followed by data preparation and model implementation with Scikit-Learn. Linear and polynomial regression techniques are explained in detail, along with model performance evaluation.
You will also delve into advanced machine learning with chapters on classification, clustering, and dimensionality reduction. Natural language processing (NLP) techniques are covered, using libraries like NLTK and SpaCy. The deep learning section covers everything from basic neural networks to advanced applications with TensorFlow and Keras, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
The book also explores big data, teaching how to work with large volumes of data using Hadoop and Spark with Python. It concludes with a comprehensive guide on conducting a data science project from start to finish and discusses ethics and responsibility in data science, addressing best practices and regulations.
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