Exploring The Python Library Ecosystem
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Exploring the Python Library Ecosystem
"Exploring the Python Library Ecosystem: A Comprehensive Guide" is your essential companion on a journey through the rich and diverse world of Python libraries. Whether you're a beginner looking to harness the power of Python for the first time or an experienced developer seeking to expand your toolkit, this comprehensive guide offers valuable insights and hands-on knowledge to help you make the most of Python's extensive library ecosystem. Inside this book, you'll delve into the heart of Python development, uncovering a vast array of libraries and modules that can streamline your projects, boost productivity, and supercharge your code. From data science and web development to software engineering and beyond, Python libraries play a pivotal role in nearly every aspect of modern programming. Key highlights include: A curated selection of essential Python libraries for various programming domains. Practical examples and use cases to illustrate how each library can be applied. Hands-on tutorials to help you master the fundamentals of using Python libraries effectively. Tips and best practices for optimizing your development workflow. Guidance on selecting the right libraries for your specific projects. Expert insights into the Python library ecosystem's latest trends and updates. "Exploring the Python Library Ecosystem" empowers you to harness the full potential of Python by tapping into the wealth of resources available within its library ecosystem. Whether you're a data scientist, web developer, or software engineer, this book provides the knowledge and tools you need to excel in your Python programming endeavors.
Python Projects
A guide to completing Python projects for those ready to take their skills to the next level Python Projects is the ultimate resource for the Python programmer with basic skills who is ready to move beyond tutorials and start building projects. The preeminent guide to bridge the gap between learning and doing, this book walks readers through the "where" and "how" of real-world Python programming with practical, actionable instruction. With a focus on real-world functionality, Python Projects details the ways that Python can be used to complete daily tasks and bring efficiency to businesses and individuals alike. Python Projects is written specifically for those who know the Python syntax and lay of the land, but may still be intimidated by larger, more complex projects. The book provides a walk-through of the basic set-up for an application and the building and packaging for a library, and explains in detail the functionalities related to the projects. Topics include: *How to maximize the power of the standard library modules *Where to get third party libraries, and the best practices for utilization *Creating, packaging, and reusing libraries within and across projects *Building multi-layered functionality including networks, data, and user interfaces *Setting up development environments and using virtualenv, pip, and more Written by veteran Python trainers, the book is structured for easy navigation and logical progression that makes it ideal for individual, classroom, or corporate training. For Python developers looking to apply their skills to real-world challenges, Python Projects is a goldmine of information and expert insight.
Machine Learning and Deep Learning Using Python and TensorFlow
Author: Venkata Reddy Konasani
language: en
Publisher: McGraw Hill Professional
Release Date: 2021-04-29
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory