Описание: The job of a data scientist is one of the most lucrative jobs out there today - it involves analyzing large amounts of data, and gathering actionable business insights from it using a variety of tools. This book empowers you to conduct data analysis and perform efficient machine learning using Python.
Автор: Agarwal, Dr Basant, Baka, Benjamin Название: Hands-On Data Structures and Algorithms with Python 2 ed ISBN: 1788995570 ISBN-13(EAN): 9781788995573 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data structures help us to organize and align the data in a very efficient way. This book will surely help you to learn important and essential data structures through Python implementation for better understanding of the concepts.
Описание: This anthology contains first-hand accounts from those involved in the conflicts of ancient China. Many of these poems are translated into English for the first time; they invoke powerful, terrible images of ancient warfare, beautifully brought to life. The poetry within this book spans more than sixteen centuries and includes the work of 50 poets.
Описание: Frank Kane`s Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you`ll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly .
Автор: Yang Название: Optimization Techniques and Applications with Examples ISBN: 1119490545 ISBN-13(EAN): 9781119490548 Издательство: Wiley Рейтинг: Цена: 16466.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences
Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author--a noted expert in the field--covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. In addition, the book discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization, evolutionary computation, nature-inspired algorithms and many other topics.
Designed as a practical resource, all topics are explained in detail with step-by-step examples to show how each method works. The book's exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining. This important resource:
Offers an accessible and state-of-the-art introduction to the main optimization techniques
Contains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques
Presents a balance of theory, algorithms, and implementation
Includes more than 100 worked examples with step-by-step explanations
Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.
This book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Written with a data science audience in mind, the book explores both scraping and the larger context of web technologies in which it operates, to ensure full understanding. The authors recommend web scraping as a powerful tool for any data scientist's arsenal, as many data science projects start by obtaining an appropriate data set.
Starting with a brief overview on scraping and real-life use cases, the authors explore the core concepts of HTTP, HTML, and CSS to provide a solid foundation. Along with a quick Python primer, they cover Selenium for JavaScript-heavy sites, and web crawling in detail. The book finishes with a recap of best practices and a collection of examples that bring together everything you've learned and illustrate various data science use cases.
What You'll Learn
Leverage well-established best practices and commonly-used Python packages
Handle today's web, including JavaScript, cookies, and common web scraping mitigation techniques
Understand the managerial and legal concerns regarding web scraping
Who This Book is For A data science oriented audience that is probably already familiar with Python or another programming language or analytical toolkit (R, SAS, SPSS, etc). Students or instructors in university courses may also benefit. Readers unfamiliar with Python will appreciate a quick Python primer in chapter 1 to catch up with the basics and provide pointers to other guides as well.
Learn to effectively manage data and execute data science projects from start to finish using Python
Key Features:
Understand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modeling
Build a strong data science foundation with the best data science tools available in Python
Add value to yourself, your organization, and society by extracting actionable insights from raw data
Book Description:
Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.
The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.
As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.
By the end of the book, you should be able to comfortably use Python for basic data science projects and should have skills to execute the data science process on any data source.
What You Will Learn:
Use Python data science packages effectively
Clean and prepare data for data science work, including feature engineering and feature selection
Data modelling, including classic statistical models (e.g., t-tests), and essential machine learning (ML) algorithms, such as random forests and boosted models
Evaluate model performance
Compare and understand different ML methods
Interact with Excel spreadsheets through Python
Create automated data science reports through Python
Get to grips with text analytics techniques
Who this book is for:
The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor's, Master's, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science.
The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
Описание: 55% OFF for Bookstores for the next 3 days only
Do you want to understand machine learning, but it all looks too daunting and complex? Afraid to open the 'pandora's box' and waste hours searching for answers?
Your customers will have no more secrets about the future development of artificial intelligence
Written with the beginner in mind, this powerful guide breaks down everything you need to know about machine learning and Python in a simple, easy-to-understand way. So many other books make machine learning look impossible to understand and even harder to master - but now you can familiarize yourself with this incredible technology like never before
With a detailed and concise overview of the fundamentals, along with the challenges and limitations currently being tackled by the pros, inside this comprehensive guide, you will
Learn the Fundamentals of Machine Learning which Are Being Developed and Advanced with Python
Master the Nuances of 12 of the Most Popular and Widely-Used Machine Learning Algorithms, in a Language that Requires No Prior Background in Python
Discover the Details of the Supervised, Unsupervised, and Reinforcement Algorithms, which Serve as the Skeleton of Hundreds of Machine Learning Algorithms Being Developed Every Day
Become Familiar with Data Science Technology, an Umbrella Term Used for the Cutting-Edge Technologies of Today
Dive Into the Functioning of Scikit-Learn Library and Develop Machine Learning Models, with a Detailed Walkthrough and Open Source Database using Illustrations and actual Python Code
Understand the Entire Process of Creating Neural Network Models on TensorFlow, Using Open Source Data Sets and real Python Code
Uncover the Secrets of the Most Critical Aspect of Developing a Machine Learning Model - Data Pre-Processing and Training/Testing Subsets
With a wealth of tips and tricks, along with invaluable advice guaranteed to help you with your machine learning journey, this book is a powerful and revolutionary tool for creating, developing, and using machine learning. From understanding the Python language to creating data sets and building neural networks, now you can become the master of machine learning with this incredible guide
So what are you waiting for?
Buy it NOW and let your customers become addicted to this incredible book
Описание: Delphi Cookbook -Third Edition is a comprehensive recipe-based guide which will help you to develop your skills while working with the real business scenarios from the very beginning. This book teaches you to design and develop cross-platform applications, deploy them on the cloud platform, and leverage the extensive toolkit that Delphi provides.
Are you looking for a super-fast computer programming course?
Would you like to learn the Python Programming Language in 7 days?
Do you want to increase your business thanks to basic acquaintance with web applications?
Ten keep reading
★Python Crash course★ will introduce you to Pyhton language and discover the world of data science, machine learning and artificial intelligence.
You will also learn all the best tricks of writing codes.
The following list is just a tiny fraction of what you will learn:
The basics of Python programming
Differences among programming languages: Vba, SQL, R, Python
4 reason why Python is fundamental for Data Science
Introduction to some Python libraries, including NumPy, Pandas, Matplotlib.
Python design patterns
Business application of Python Data Analysis
Optimal tools and techniques for data mining
Analysis of popular Python projects templates
Game creation with Pyhton
Even if you have never written a programming code before, you will quickly grasp the basics thanks to visual charts and guidelines for coding.
Examples and step-by-step guides will guide you during the code-writing learning process.
Therefore, if you really wish to find a course to learn Python in 7 days, learn and master its language, please click the BUY NOW button.
Автор: Porcu Valentina Название: Python for Data Mining Quick Syntax Reference ISBN: 1484241126 ISBN-13(EAN): 9781484241127 Издательство: Springer Рейтинг: Цена: 4611.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
?Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.
Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them.
The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning.
What You'll Learn
Install Python and choose a development environmentUnderstand the basic concepts of object-oriented programmingImport, open, and edit filesReview the differences between Python 2.x and 3.xWho This Book Is For
Programmers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.
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