Barker, Douglas S. Blank, Jed Brown, Allen B. Downey, Timothy George, Lindsey J. Heagy, Kyle T. Mandli, Jason K. Moore, David Lippert, Kyle E. Niemeyer, Ryan R. Watkins, Richard H. This handbook is for any educator teaching a topic that includes data analysis or computation in order to support learning.
It is not just for educators teaching courses in engineering or science, but also data journalism, business and quantitative economics, data-based decision sciences and policy, quantitative health sciences, and digital humanities. It aims to provide an entry point, and a broad overview of Jupyter in education. Whether you are already using Jupyter to teach, you have found learning materials built on Jupyter that piqued your curiosity, or have never heard of Jupyter, the material in this open book can empower you to use this technology in your teaching.
Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality.
Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly.
Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!
Machine earning ML is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence AI , machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit SDK allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings.
Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Source code examples are provided for readers to download and use in their own projects. This book helps readers: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming Develop skills in data acquisition and modeling, classification, and regression.
Compare traditional vs. ML approaches, and machine learning on handsets vs. Author : Dan Toomey Publisher: Packt Publishing Ltd ISBN: Category: Computers Page: View: Read Now » Learn how to write code, mathematics, graphics, and output, all in a single document, as well as in a web browser using Project Jupyter About This Book Learn to write, execute, and comment your live code and formulae all under one roof using this unique guide This one-stop solution on Project Jupyter will teach you everything you need to know to perform scientific computation with ease This easy-to-follow, highly practical guide lets you forget your worries in scientific application development by leveraging big data tools such as Apache Spark, Python, R etc Who This Book Is For This book caters to all developers, students, or educators who want to execute code, see output, and comment all in the same document, in the browser.
What You Will Learn Install and run the Jupyter Notebook system on your machine Implement programming languages such as R, Python, Julia, and JavaScript with Jupyter Notebook Use interactive widgets to manipulate and visualize data in real time Start sharing your Notebook with colleagues Invite your colleagues to work with you in the same Notebook Organize your Notebook using Jupyter namespaces Access big data in Jupyter In Detail Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents.
Next we'll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system.
Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Style and approach This comprehensive practical guide will teach you how to work with the Jupyter Notebook system. It demonstrates the integration of various programming languages with Jupyter Notebook through hands-on examples in every chapter. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.
Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world.
We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. What you will learn Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests Plan a machine learning classification strategy and train classification, models Use validation curves and dimensionality reduction to tune and enhance your models Discover how you can use web scraping to gather and parse your own bespoke datasets Scrape tabular data from web pages and transform them into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science.
You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start. As a result, relevant technical expertise and analytical skills are required to do such tasks. The book starts with a brief introduction to the world of data science and the opportunities you may come across along with an overview of the key topics covered in the book.
You will learn how to setup Anaconda installation which comes with Jupyter and preinstalled Python packages. Several machine learning techniques such as regression, classification, clustering, time-series etc have been explained with the use of practical examples and by comparing the performance of various models. By the end of the book, you will come across few case studies to put your knowledge to practice and solve real-life business problems such as building a movie recommendation engine, classifying spam messages, predicting the ability of a borrower to repay loan on time and time series forecasting of housing prices.
Remember to practice additional examples provided in the code bundle of the book to master these techniques. Audience The book is intended for anyone looking for a career in data science, all aspiring data scientists who want to learn the most powerful programming language in Machine Learning or working professionals who want to switch their career in Data Science.
While no prior knowledge of Data Science or related technologies is assumed, it will be helpful to have some programming experience. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts.
This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies.
This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms.
Each chapter contains easy-to-follow Python examples. Data Science Fundamentals 2. Installing Software and System Setup 3.
Lists and Dictionaries 4. Package, Function, and Loop 5. NumPy Foundation 6. Pandas and DataFrame 7. Interacting with Databases 8. Thinking Statistically in Data Science 9. How to Import Data in Python? Cleaning of Imported Data Data Visualization Data Pre-processing Supervised Machine Learning Unsupervised Machine Learning Handling Time-Series Data This site comply with DMCA digital copyright.
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