Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific libraries
Key Features
Compute complex mathematical problems using programming logic with the help of step-by-step recipes
Learn how to utilize Python's libraries for computation, mathematical modeling, and statistics
Discover simple yet effective techniques for solving mathematical equations and apply them in real-world statistics
Book Description
Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.
The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.
By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
What you will learn
Get familiar with basic packages, tools, and libraries in Python for solving mathematical problems
Explore various techniques that will help you to solve computational mathematical problems
Understand the core concepts of applied mathematics and how you can apply them in computer science
Discover how to choose the most suitable package, tool, or technique to solve a certain problem
Implement basic mathematical plotting, change plot styles, and add labels to the plots using Matplotlib
Get to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methods
Who this book is for
This book is for professional programmers and students looking to solve mathematical problems computationally using Python. Advanced mathematics knowledge is not a requirement, but a basic knowledge of mathematics will help you to get the most out of this book. The book assumes familiarity with Python concepts of data structures.
Автор: Ward Stephen Название: Python Data Analytics: The Ultimate Guide To Get Started With Data Analysis Using Python, NumPy and Pandas ISBN: 1801096813 ISBN-13(EAN): 9781801096812 Издательство: Неизвестно Рейтинг: Цена: 18380.00 T Наличие на складе: Нет в наличии. Описание: Unlock the programming skills you need to prepare for a lucrative career in Data Science with this comprehensive introduction to Python programming for data analytics
Are you completely new to programming and want to learn how to code, but don't know where to begin? Are you looking to upgrade your data wrangling skills to future-proof your career and break into Data Science and Analytics?
If you answered yes to any of the questions above, then keep reading...
Data analysis has become a huge industry with tons of career potential and will remain relevant far into the foreseeable future.
With the exponential growth and explosion of new data and the focus on using data to improve customer experiences and carry out research, data analysts will be needed to process and make sense of large amounts of information, with Python being the language of choice because of its versatility.
In this guide, you're going to be shown everything you need to break into the world of Data Analysis with Python. Filled with tutorials for powerful libraries and practical, hands-on exercises, you're going to learn how to aggregate, munge, analyze and visualize data in Python.
Here's a sample of what you're going to discover in Python Data Analytics
Why Python is the perfect language to learn if you want to break into Big Data and data analytics
Core statistical models and computation methods you need to know about as a budding data analyst
How to master the CSV library for reading, writing and handling tabular data
Using the Xlrd library to extract data from Microsoft Excel files
How to convert text to speech using the powerful Win32.com library
How to use the NumPy library to carry out fundamental and basic scientific and technical computing
How to use the SciPy library to carry out advanced scientific and highly technical computing
Surefire ways to manipulate the easy-to-use data structures of the Pandas framework for high-performance data analysis
How to plot complex data, create figures and visualize data using the Python Matplotlib library
...and tons more
If you're completely new to programming and have never written a single line of code, but want to get started, this guide is perfect for as a crash guide to getting up to speed with programming in general.
Whether you're a programmer looking to switch into an exciting new field with lots of potential for the future, or a regular data analyst looking to acquire the skills needed to remain relevant in a fast-changing world, this guide will teach you how to master powerful libraries used in the real-world by experienced data scientists.
Автор: Manohar Swamynathan Название: Mastering Machine Learning with Python in Six Steps ISBN: 1484249461 ISBN-13(EAN): 9781484249468 Издательство: Springer Рейтинг: Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.What You'll LearnUnderstand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNNWho This Book Is ForPython developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
Автор: Subasi, Abdulhamit Название: Practical Machine Learning For Data Analysis Using Python ISBN: 0128213795 ISBN-13(EAN): 9780128213797 Издательство: Elsevier Science Рейтинг: Цена: 110030.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.
Are you looking for a crash course that will help you learn Python? Do you want to master data science using Python? If yes, then keep reading
Python is one of the most popular programming languages in the word in 2020 and specially for data science. Every day people use it to do cool things like Automation, they use it in Artificial Intelligence, Machine Learning, as well as Building Applications and Websites like Instagram and Dropbox. YouTube, Pinterest, and SurveyMonkey are all built on Python. So if you are looking for a trendy job, like data scientist, Python is for you.
This is a Python guide with 2 Books in 1:
Python crash course
Python for data analysis
Python has seen an explosion in popularity in recent years, driven by several aspects that make it an incredibly versatile and intuitive language.
Moreover, data analysis plays a significant job in numerous parts of your regular day to day existence today. Organizations use information to Understand Their Customer Needs and produce the Best Possible Product or Service. Python Programming Language is one of the best framework with regards to information examination.
Data Scientist is the most requested job of the 21st century and Python is the most popular programming language of the 21st century. So it's pretty obvious that anyone have skills in both Data Science and Python will be in great demand in industry.
You needn't bother with an exhausting and costly reading material. This guide is the best one for every readers.
This guide covers:
The world of data science technologies
Application of machine learning
Data scientist: the sexiest job in the 21st century
Learning Python from scratch
Data analysis with Python
NumPy for numerical data processing
Data visualization with Python
Projects on Python
And much more
Despite its simplicity, Python is also sturdy and robust enough to carry out complex scientific and mathematical tasks. Python has been designed with features that drastically simplify the visualization and analysis of data, and Python is also the go-to choice for the creation of machine learning models and artificial intelligence.
Be it machine learning, data analytics, data processing, web development, enterprise software development or taking the photo of Blackhole: Python is everywhere. Beloved by the data scientists and new generation developers, Pyhton will eat the word
Are you looking for a crash course that will help you learn Python? Do you want to master data science using Python? If yes, then keep reading
Python is one of the most popular programming languages in the word in 2020 and specially for data science. Every day people use it to do cool things like Automation, they use it in Artificial Intelligence, Machine Learning, as well as Building Applications and Websites like Instagram and Dropbox. YouTube, Pinterest, and SurveyMonkey are all built on Python. So if you are looking for a trendy job, like data scientist, Python is for you.
This is a Python guide with 2 Books in 1:
Python crash course
Python for data analysis
Python has seen an explosion in popularity in recent years, driven by several aspects that make it an incredibly versatile and intuitive language.
Moreover, data analysis plays a significant job in numerous parts of your regular day to day existence today. Organizations use information to Understand Their Customer Needs and produce the Best Possible Product or Service. Python Programming Language is one of the best framework with regards to information examination.
Data Scientist is the most requested job of the 21st century and Python is the most popular programming language of the 21st century. So it's pretty obvious that anyone have skills in both Data Science and Python will be in great demand in industry.
You needn't bother with an exhausting and costly reading material. This guide is the best one for every readers.
This guide covers:
The world of data science technologies
Application of machine learning
Data scientist: the sexiest job in the 21st century
Learning Python from scratch
Data analysis with Python
NumPy for numerical data processing
Data visualization with Python
Projects on Python
And much more
Despite its simplicity, Python is also sturdy and robust enough to carry out complex scientific and mathematical tasks. Python has been designed with features that drastically simplify the visualization and analysis of data, and Python is also the go-to choice for the creation of machine learning models and artificial intelligence.
Be it machine learning, data analytics, data processing, web development, enterprise software development or taking the photo of Blackhole: Python is everywhere. Beloved by the data scientists and new generation developers, Pyhton will eat the word
Python is one of the most beloved programming languages in any circle of programmers. Software engineers, hackers, and Data Scientists alike are in love with the versatility that Python has to offer. Besides, the Object-Oriented feature of Python coupled with its flexibility is also one of the major attractions for this language.
That's the reason why Python is a perfect fit with Data Analysis, Machine Learning and Data Science.
Data is the future. The world of technology as we know it is evolving towards an open-source platform where people share ideas freely. This is seen as the first step towards the decentralization of ideas and eliminating unnecessary monopolies. Therefore, the data, tools, and techniques used in the analysis are easily available for anyone to interpret data sets and get relevant explanations.
The goal of this 4-in-1 bundle is simple: explaining everything you need to know to Master Python. With a special emphasis on the main steps that are needed to correctly implement Data Analysis and Machine Learning algorithms,
In manuscript one, Python for Beginners, you will learn:
How to install Python
What are the different Python Data Types and Variables
Basic Operators of Python Language
Data Structures and Functions
Conditional and Loops in Python
And Much More
In manuscript two, Python Advanced Guide, you will master:
Object-Oriented Programming (OOP), Inheritance and Polymorphism
Essential Programming Tools
Exception Handling
Working with Files
And Much More
In manuscript three, Python for Data Analysis, you will learn:
What Data Analysis is all about and why businesses are investing in this sector
The 5 steps of a Data Analysis
The 7 Python libraries that make Python one of the best choices for Data Analysis
Pandas, Jupyter and PyTorch
And Much More
In manuscript four, Applications to Data Science, you will understand:
How Data Visualization and Matplotlib can help you to understand the data you are working with.
Neural Networks
Decision Trees
What industries are using data to improve their business with 14 real-world applications
And So Much More
Where most books about Python programming are theoretical and have few or little practical examples, this book provides lots of simple, step-by-step examples and illustrations that are used to underline key concepts and help improve your understanding.
Furthermore, topics are carefully selected to give you broad exposure to Python, while not overwhelming you with too much information. Also, the outputs of ALL the examples are provided immediately so you do not have to wait till you have access to your computer to test the examples.
Even if you have never coded before, this is the perfect guide because it breaks down complex concepts into simple steps and in a concise and simple way that fits well with beginners. Regardless of your previous experience, you will learn the steps of Data Analysis, how to implement them, and the most important real-world applications.
Would you like to know more?Scroll Up and Click the BUY NOW Button to Get Your Copy
Python is one of the most beloved programming languages in any circle of programmers. Software engineers, hackers, and Data Scientists alike are in love with the versatility that Python has to offer. Besides, the Object-Oriented feature of Python coupled with its flexibility is also one of the major attractions for this language.
That's the reason why Python is a perfect fit with Data Analysis, Machine Learning and Data Science.
Data is the future. The world of technology as we know it is evolving towards an open-source platform where people share ideas freely. This is seen as the first step towards the decentralization of ideas and eliminating unnecessary monopolies. Therefore, the data, tools, and techniques used in the analysis are easily available for anyone to interpret data sets and get relevant explanations.
The goal of this 4-in-1 bundle is simple: explaining everything you need to know to Master Python. With a special emphasis on the main steps that are needed to correctly implement Data Analysis and Machine Learning algorithms,
In manuscript one, Python for Beginners, you will learn:
How to install Python
What are the different Python Data Types and Variables
Basic Operators of Python Language
Data Structures and Functions
Conditional and Loops in Python
And Much More
In manuscript two, Python Advanced Guide, you will master:
Object-Oriented Programming (OOP), Inheritance and Polymorphism
Essential Programming Tools
Exception Handling
Working with Files
And Much More
In manuscript three, Python for Data Analysis, you will learn:
What Data Analysis is all about and why businesses are investing in this sector
The 5 steps of a Data Analysis
The 7 Python libraries that make Python one of the best choices for Data Analysis
Pandas, Jupyter and PyTorch
And Much More
In manuscript four, Applications to Data Science, you will understand:
How Data Visualization and Matplotlib can help you to understand the data you are working with.
Neural Networks
Decision Trees
What industries are using data to improve their business with 14 real-world applications
And So Much More
Where most books about Python programming are theoretical and have few or little practical examples, this book provides lots of simple, step-by-step examples and illustrations that are used to underline key concepts and help improve your understanding.
Furthermore, topics are carefully selected to give you broad exposure to Python, while not overwhelming you with too much information. Also, the outputs of ALL the examples are provided immediately so you do not have to wait till you have access to your computer to test the examples.
Even if you have never coded before, this is the perfect guide because it breaks down complex concepts into simple steps and in a concise and simple way that fits well with beginners. Regardless of your previous experience, you will learn the steps of Data Analysis, how to implement them, and the most important real-world applications.
Would you like to know more?Scroll Up and Click the BUY NOW Button to Get Your Copy
Автор: Gries Paul, Campbell Jennifer, Montojo Jason Название: Practical Programming: An Introduction to Computer Science Using Python 3.6 ISBN: 1680502689 ISBN-13(EAN): 9781680502688 Издательство: Wiley Рейтинг: Цена: 42230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Classroom-tested by tens of thousands of students, this new edition of the bestselling intro to programming book is for anyone who wants to understand computer science. Learn about design, algorithms, testing, and debugging. Discover the fundamentals of programming with Python 3.6--a language that`s used in millions of devices.
Write algorithms and program in the new field of quantum computing. This book covers major topics such as the physical components of a quantum computer: qubits, entanglement, logic gates, circuits, and how they differ from a traditional computer. Also, Practical Quantum Computing for Developers discusses quantum computing in the cloud using IBM Q Experience including: the composer, quantum scores, experiments, circuits, simulators, real quantum devices, and more. You’ll be able to run experiments in the cloud on a real quantum device.
Furthermore, this book shows you how to do quantum programming using the QISKit (Quantum Information Software Kit), Python SDK, and other APIs such as QASM (Quantum Assembly). You’ll learn to write code using these languages and execute it against simulators (local or remote) or a real quantum computer provided by IBM’s Q Experience. Finally, you’ll learn the current quantum algorithms for entanglement, random number generation, linear search, integer factorization, and others. You’ll peak inside the inner workings of the Bell states for entanglement, Grover’s algorithm for linear search, Shor’s algorithm for integer factorization, and other algorithms in the fields of optimization, and more.
Along the way you’ll also cover game theory with the Magic Square, an example of quantum pseudo-telepathy where parties sharing entangled states can be observed to have some kind of communication between them. In this game Alice and Bob play against a referee. Quantum mechanics allows Alice and Bob to always win!
By the end of this book, you will understand how this emerging technology provides massive parallelism and significant computational speedups over classical computers, and will be prepared to program quantum computers which are expected to replace traditional computers in the data center.
What You Will Learn
Use the Q Experience Composer, the first-of-its-kind web console to create visual programs/experiments and submit them to a quantum simulator or real device on the cloudRun programs remotely using the Q Experience REST API Write algorithms that provide superior performance over their classical counterpartsBuild a Node.js REST client for authenticating, listing remote devices, querying information about quantum processors, and listing or running experiments remotely in the cloudCreate a quantum number generator: The quintessential coin flip with a quantum twistDiscover quantum teleportation: This algorithm demonstrates how the exact state of a qubit (quantum information) can be transmitted from one location to another, with the help of classical communication and quantum entanglement between the sender and receiverPeek into single qubit operations with the classic game of Battleships with a quantum twistHandle the counterfeit coin problem: a classic puzzle that consists of finding a counterfeit coin in a beam balance among eight coins in only two turns
Who This Book Is For
Developers and programmers interested in this new field of computing.
Автор: Ilya Shpigor Название: Practical Video Game Bots ISBN: 1484237358 ISBN-13(EAN): 9781484237359 Издательство: Springer Рейтинг: Цена: 41920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 0. Introduction1. Bots Overview2. Clicker Bots3. In-Game Bots4. Out-Game Bots5. Extra Techniques
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms.
For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.
After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.
This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production.
What You Will Learn
Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using PythonFollow a deep learning project from conception to production using TensorFlowUse NumPy with Kivy to build cross-platform data science applications
Who This Book Is For
Data scientists, machine learning and deep learning engineers, software developers.
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