Computing Fundamentals, Introduction to Computers gets you up to speed on basic computing skills, showing you everything you need to know to conquer entry-level computing courses. Written by a Microsoft Office Master Instructor, this useful guide walks you step-by-step through the most important concepts and skills you need to be proficient on the computer, using nontechnical, easy-to-understand language. You'll start at the very beginning, getting acquainted with the actual, physical machine, then progress through the most common software at your own pace. You'll learn how to navigate Windows 8.1, how to access and get around on the Internet, and how to stay connected with email. Clear instruction guides you through Microsoft Office 2013, helping you create documents in Word, spreadsheets in Excel, and presentations in PowerPoint. You'll even learn how to keep your information secure with special guidance on security and privacy.
Maybe you're preparing for a compulsory computing course, brushing up for a new job, or just curious about how a computer can make your life easier. If you're an absolute beginner, this is your complete guide to learning the essential skills you need:
With clear explanations and step-by-step instruction, Computing Fundamentals, Introduction to Computers will have you up and running in no time.
This book will offer you an insight into the Hidden Markov Models as well as the Bayesian Networks. Additionally, by reading this book, you will also learn algorithms such as Markov Chain Sampling.
Furthermore, this book will also teach you how Markov Models are very relevant when a decision problem is associated with a risk that continues over time, when the timing of occurrences is vital as well as when events occur more than once. This book highlights several applications of Markov Models.Â
Lastly, after purchasing this book, you will need to put in a lot of effort and time for you to reap the maximum benefits.
Welcome to computer science in the 21st century. Did you ever wonder how computers represent DNA? How they can download a web page containing population data and analyze it to spot trends? Or how they can change the colors in a color photograph? If so, this book is for you. By the time you're done, you'll know how to do all of that and a lot more. And Python makes it easy and fun.Computers are used in every part of science from ecology to particle physics. This introduction to computer science continually reinforces those ties by using real-world science problems as examples. Anyone who has taken a high school science class will be able to follow along as the book introduces the basics of programming, then goes on to show readers how to work with databases, download data from the web automatically, build graphical interfaces, and most importantly, how to think like a professional programmer. Topics covered include: Basic elements of programming from arithmetic to loops and if statements. Using functions and modules to organize programs. Using lists, sets, and dictionaries to organize data. Designing algorithms systematically. Debugging things when they go wrong. Creating and querying databases. Building graphical interfaces to make programs easier to use. Object-oriented programming and programming patterns.
The phrase âMachine Learningâ refers to the automatic detection of meaningful data by computing systems. In the last few decades, it has become a common tool in almost any task that needs to understand data from large data sets. One of the biggest application of machine learning technology is the search engine. Search engines learn how to provide the best results based on historic, trending, and relative data sets. When you look at anti-spam software, it learns how to filter email messages. Going to credit cards, transactions are secured by software that knows when fraudulent activities are going on. We currently have digital cameras that detect faces, personal assistant applications that are intelligent enough to learn voice commands. These are all applications based on machine learning!
Cars are becoming equipped with accident prevention systems that are powered by machine learning algorithms. Machine learning is also widely used in scientific fields like bioinformatics and astronomy. In contrast to traditional computing, and due to the complexity of patterns that need to be detected, it is hard for a programmer to provide a fine-detailed specification on the execution of these tasks. So where do we start?
How about key machine learning algorithms? These are algorithms that are used in the real world, and they give a wide spectrum of the different learning techniques. There are also different algorithms that are better suited for big data. The world has become increasingly connected, and as a result, and in many business applications, there is a lot of data and computation needed to learn different concepts.
As you can imagine, the topic of machine learning, depending on the application, can be contained or wildly complex. This book will give you an overview of what machine learning is capable of and some basic algorithms to help you understand the fundamentals of the technology.
Finally, how will the employment landscape going to be affected by machine learning in the near future? In later chapters of this book, we will talk about the skills that a you will need to have to work in a profession related to machine learning, and how each field might be affected by the age of computerization. The future is changing very quickly and professionals will need to adapt to ever-evolving technology if they want to stand a chance in keeping up with the joneses.
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
Youâll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas MÃ¼ller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, youâll learn:
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