Learn to code with Python

A project-filled introduction to coding that shows kids how to build programs by making cool games.Scratch, the colorful drag-and-drop programming language, is used by millions of first-time learners worldwide. Scratch 3 features an updated interface, new programming blocks, and the ability to run on tablets and smartphones, so you can learn how to code on the go. In Scratch 3 Programming Playground, you'll learn to code by making cool games. Get ready to destroy asteroids, shoot hoops, and slice and dice fruit! Each game includes easy-to-follow instructions with full-color images, review questions, and creative coding challenges to make the game your own. Want to add more levels or a cheat code? No problem, just write some code. You'll learn to make games like:Maze Runner: escape the maze!Snaaaaaake: gobble apples and avoid your own tailAsteroid Breaker: smash space rocksFruit Slicer: a Fruit Ninja cloneBrick Breaker: a remake of Breakout, the brick-breaking classicPlatformer: a game

This book introduces artificial neural networks to students and professionals. It covers the theory and applications in statistical learning methods with concrete Python code examples.

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.

Learning to program isn't just learning the details of a programming language: to become a good programmer you have to become expert at debugging, testing, writing clear code and generally unsticking yourself when you get stuck, while to do well in a programming course you have to learn to score highly in coursework and exams. Featuring tips, stories and explanations of key terms, this book teaches these skills explicitly. Examples in Python, Java and Haskell are included, helping you to gain transferable programming skills whichever language you are learning. Intended for students in Higher or Further Education studying early programming courses, it will help you succeed in, and get the most out of, your course, and support you in developing the software engineering habits that lead to good programs.

Practice lowercase and uppercase letters and learn to form words with this exciting game from Scholastic Early Learners! An innovative, reading-focused learning game complete with QR-code guidance. Pr

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.

Master the usage of s-parameters in signal integrity applications and gain full understanding of your simulation and measurement environment with this rigorous and practical guide. Solve specific signal integrity problems including calculation of the s-parameters of a network, linear simulation of circuits, de-embedding, and virtual probing, all with expert guidance. Learn about the interconnectedness of s-parameters, frequency responses, filters, and waveforms. This invaluable resource for signal integrity engineers is supplemented with the open-source software SignalIntegrity, a Python package for scripting solutions to signal integrity problems.

The world of machine learning is changing all the time. It is so amazing the idea that we are able to take a computer and let it learn as it goes. Without having to write out all of the codes that we need for every situation out there or every input that the user may pick, we are able to write out codes in machine learning, even with Python, in order to let the computer or device learn and make decisions on its own.This guidebook is going to take a closer look at how Python machine learning is able to work, as well as how you can use some of the tools and techniques that come with this process for your own needs. When you are interested in learning more about what machine learning is all about, as well as how you can use a part of the coding from Python inside of this process, then this guidebook is the tool for you Some of the topics that we will explore when we go through this guidebook will include: Understanding some of the basics of machine learning;Some of the different parts tha

If you want to ensure a bright future for your child thank to coding, then keep reading...Hey you, father or mother that are reading these lines, let me introduce myself. I'm Raymond Deep, expert programmer since 1999 and father of two wonderful kids.Do you know that the median annual wage for computer programmers was $86,550 in 2019? I want, like you, give a chance to my childs and, you know, coding is the skill that each one wants on the market.Children want to have fun and I was looking for, without ever being satisfied, a product that would explain step by step, without the intervention of a parent and in a funny way, how to start writing the first line of code.Searching on internet, I discovered an image-based programming language that is perfect for kids to learn the logic of programming, how a programmer has to think: SCRATCH 3.This language/tool is fantastic and, because I'm an experienced programmer, I saw the big potential and I started to study all details and features of th

Discover the Most Comprehensible Beginner's Guide to Coding for Children, Packed with Fun Coding Activities and Games All Kids Will LoveDear friend, Do you have a little smartypants running around your home? Would you like to ensure the brightest possible future for your child? If so, then this book is a perfect choice for both of you.This bundle is an excellent choice for all children who are interested in the world of computers, programming, and coding. It is specially made for kids aged from 8 to 12 that have no prior knowledge of coding.Here is what this bundle can teach your child: Game-based learning - there's no better way for kids to learn than through playing and fun activities that will capture your child's attention.40+ fun coding activities and games - this bundle is packed with more than 40 fun activities that will introduce coding to your child and help them grasp the basic skills from a very young age.Easy-to-follow guidance - Straightforward directions and tips keep you

Learning to code has never been easier than with this innovative visual guide to computer programming for beginners.Coding skills are in high demand and the need for programmers is still growing. Howe

Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud. You'll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you'll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You'll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. By the end of Data Science with the Raspberry Pi, you'll understand that many applications are now de