Python
Python, PyGame and Raspberry Pi Game Development
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Title:
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Python, PyGame and Raspberry Pi Game Development
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Authors:
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Sloan Kelly
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Edition:
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1st – 2017
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Publisher:
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Apress
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Pages:
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200
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Language:
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English
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ISBN-10
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1484225163
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ISBN-13
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978-1484225165
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Format:
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PDF
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Size (MB):
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3 | |
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Book Description:
Gain the basics of Python and use PyGame to create fast-paced video games with great graphics and sounds. You’ll also learn about object oriented programming (OOP) as well as design patterns like model-view-controller (MVC) and finite state machines (FSMs). Python, PyGame and Raspberry Pi Game Development teaches you how to use Python and PyGame on your computer. Whether you use Windows, macOS, Linux, or a Raspberry Pi you can unleash the power of Python and PyGame to create great looking games. Included in the text are complete code listings and explanations for “Bricks,” “Snake” and “Invaders”– three fully-working games. These allow you to get started making your own great games. Modify them or build your own exciting titles. Table of Contents: |
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Machine Learning in Python: Essential Techniques for Predictive Analysis
Book Description
Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions.
Table of Contents
Chapter 1 The Two Essential Algorithms for Making Predictions
Chapter 2 Understand the Problem by Understanding the Data
Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data
Chapter 4 Penalized Linear Regression
Chapter 5 Building Predictive Models Using Penalized Linear Methods
Chapter 6 Ensemble Methods
Chapter 7 Building Ensemble Models with Python
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Designing Machine Learning Systems with Python – Free PDF Download
Book Description
There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.
Table of Contents
1. Thinking in Machine Learning
2. Tools and Techniques
3. Turning Data into Information
4. Models – Learning from Information
5. Linear Models
6. Neural Networks
7. Features – How Algorithms See the World
8. Learning with Ensembles
9. Design Strategies and Case Studies
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Building Machine Learning Systems with Python – Free PDF Download
Book Description
Master Machine Learning using a broad set of Python libraries and start building your own Python-based ML systems. Covers classification, regression, feature engineering, and much more guided by practical examples. A scenario-based tutorial to get into the right mind-set of a machine learner (data exploration) and successfully implement this in your new or existing projects.
Table of Contents
1. Getting Started with Python Machine Learning
2. Classifying with Real-world Examples
3. Clustering – Finding Related Posts
4. Topic Modeling
5. Classification – Detecting Poor Answers
6. Classification II – Sentiment Analysis
7. Regression
8. Recommendations
9. Classification – Music Genre Classification
10. Computer Vision
11. Dimensionality Reduction
12. Bigger Data
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Download here (2nd Ed.)
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Data Wrangling with Python: Tips and Tools to Make Your Life Easier
Book Description
How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don’t need to know a thing about the Python programming language to get started.
Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain.
Quickly learn basic Python syntax, data types, and language concepts
Work with both machine-readable and human-consumable data
Scrape websites and APIs to find a bounty of useful information
Clean and format data to eliminate duplicates and errors in your datasets
Learn when to standardize data and when to test and script data cleanup
Explore and analyze your datasets with new Python libraries and techniques
Use Python solutions to automate your entire data-wrangling process
Table of Contents
Chapter 1 Introduction to Python
Chapter 2 Python Basics
Chapter 3 Data Meant to Be Read by Machines
Chapter 4 Working with Excel Files
Chapter 5 PDFs and Problem Solving in Python
Chapter 6 Acquiring and Storing Data
Chapter 7 Data Cleanup: Investigation, Matching, and Formatting
Chapter 8 Data Cleanup: Standardizing and Scripting
Chapter 9 Data Exploration and Analysis
Chapter 10 Presenting Your Data
Chapter 11 Web Scraping: Acquiring and Storing Data from the Web
Chapter 12 Advanced Web Scraping: Screen Scrapers and Spiders
Chapter 13 APIs
Chapter 14 Automation and Scaling
Chapter 15 Conclusion
Download
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Head First Python by Paul Barry – Free PDF Download
Book Description
Ever wished you could learn Python from a book? Head First Python is a complete learning experience for Python that helps you learn the language through a unique method that goes beyond syntax and how-to manuals, helping you understand how to be a great Python programmer. You’ll quickly learn the language’s fundamentals, then move onto persistence, exception handling, web development, SQLite, data wrangling, and Google App Engine. You’ll also learn how to write mobile apps for Android, all thanks to the power that Python gives you.
We think your time is too valuable to waste struggling with new concepts. Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Python uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.
Table of Contents
Chapter 1 Meet Python: Everyone loves lists
Chapter 2 Sharing your Code: Modules of functions
Chapter 3 Files and Exceptions: Dealing with errors
Chapter 4 Persistence: Saving data to files
Chapter 5 Comprehending data: Work that data!
Chapter 6 Custom data Objects: Bundling code with data
Chapter 7 Web Development: Putting it all together
Chapter 9 Manage Your data: Handling input
Chapter 10 Scaling your Webapp: Getting real
Chapter 11 Dealing with Complexity: Data wrangling
Download
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Python Machine Learning – Free PDF Download
Book Description
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable.
Table of Contents
1. Giving Computers the Ability to Learn from Data
2. Training Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using Scikit-learn
4. Building Good Training Sets – Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning to Sentiment Analysis
9. Embedding a Machine Learning Model into a Web Application
10. Predicting Continuous Target Variables with Regression Analysis
11. Working with Unlabeled Data – Clustering Analysis
12. Training Artificial Neural Networks for Image Recognition
13. Parallelizing Neural Network Training with Theano
Download
You can download this book from any of the following links. If any link is dead please feel free to leave a comment.
Download here
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Copyright Disclaimer
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we’ll remove relevant links or contents immediately.
Python Programming for Arduino
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Title:
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Python Programming for Arduino
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Authors:
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Pratik Desai
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Edition:
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2015
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Publisher:
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Packt Publishing
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Pages:
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576
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Language:
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English
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ISBN-10
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1783285931
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ISBN-13
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978-1783285938
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Format:
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PDF
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Size (MB):
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11 | |
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Book Description:
The future belongs to applications and services that involve connected devices, requiring physical components to communicate with web-level applications. Arduino combined with the popular open source software platform Python can be used to develop the next level of advanced Internet of Things (IoT) projects with graphical user interfaces and Internet-connected applications. Starting with designing hardware prototypes using Arduino, this book will then show you everything you need to know to be able to develop complex cloud applications. You will delve into domain-specific topics with incremental complexity, ending with real-world projects. You will quickly learn to develop user interfaces, plots, remote access, messaging protocols, and cloud connectivity. Each successive topic, accompanied by plenty of examples, will help you develop your cutting-edge hardware applications. Table of Contents: Copyright Disclaimer: |
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