By the end of the book, you will have become proficient at building efficient enterprise applications in Python. The main concepts of Bayesian statistics are 2018 using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian vonus, among others.
Publisher's page Hands-On Predictive Analytics with Python Alvaro Fuentes ISBN Packt Pages December Book Overview: New guide to build high performing predictive applications Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model.
This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data.
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By the end of this course, you will have successfully integrated your Python web application's backend with a React. Publisher's page Bioinformatics with Python Cookbook - Second Edition Tiago Antao ISBN Packt Pages November Book Overview: Discover modern, next-generation sequencing libraries from Python ecosystem to analyze large amounts of biological data Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data.
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This book will touch the core of image processing, from concepts to code using Python. The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy codes in Python.
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We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. With code and 201 case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data.
We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. RNNs are the state-of-the-art model in deep learning for dealing with sequential data.
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Publisher's page Mastering 2018 in Python Quan Nguyen ISBN Packt Pages November Book Overview: Immerse yourself in the world of Python concurrency and tackle the most complex concurrent programming problems Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing.
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Make your applications concurrent and asynchronous with RxPY This practical guide will apkllo you steadily through an exploration of Reactive Programming. In this course, you will learn about Reactive Programming in New using RxPY and simplify the way you write code and create robust enterprise applications.
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Publisher's page Application Development with Python and Flask Achim Takunda Munene ISBN Course Length: 6 hours 7 minutes November Video Overview: Dive into the world of Flask Framework in Python to build bonks array of simple yet powerful web applications This course will take you through Flask Microframework; it covers all its components and elements and how to integrate it with useful third-party libraries.
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The course will then show you the general flow in developing a Flask application, including some extensions used by developing a simple application. You will then develop multiple applications such as note-taking, social medias, and file sharing applications. By the end of the course, you will be able to build your own web applications using Flask in Python.
Publisher's page Hands-On Software Engineering with Python Brian Allbee ISBN Packt Pages October, Book Overview: Explore various verticals in software engineering through high-end systems using Python Software Engineering is about more than just writing code—it includes a host of soft skills that apply to almost any development effort, no matter what the language, development methodology, or scope of the project.
Being a senior developer all but requires awareness of how those skills, along with their expected technical counterparts, mesh together through a project's life cycle. This book walks you through that discovery by going over the entire life cycle of a multi-tier system and its related software projects. You'll see what happens before any development takes place, and what impact the decisions and designs made at each step have on the development process.
The development of the entire project, over the course of apollo iterations based on real-world Agile iterations, will be executed, sometimes starting from nothing, in one of the fastest growing languages in the world—Python. Application of practices in Python will be laid out, along slots a number of Python-specific capabilities that are often overlooked.
Finally, the book will implement a high-performance computing solution, from first principles through complete foundation. Basant Agarwal ISBN Packt Codes October, Book Overview: Learn to implement complex data structures and algorithms using Python This book helps you to understand the power of linked lists, double linked lists, and circular linked lists.
You will learn to create complex data structures, such as graphs, stacks, and queues. As you make your way through the chapters, you will explore the application of binary searches and binary search trees, along with learning common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. In the concluding chapters, you will get to grips with organizing your code in a manageable, consistent, and extendable way.
You will also study how to bubble sort, selection sort, insertion sort, and merge sort algorithms in detail. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. You will get insights into Python implementation of all the important and relevant algorithms.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave 2018, detect cancer cells and much more! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.
Even Facebook, Google, Microsoft, Bonus, Amazon, and Tesla are all heavily utilizing computer vision for face and object recognition, image new, and especially in self-driving cars! As a result, the demand for computer vision expertise is growing exponentially!
However, learning computer vision is hard! Existing online tutorials, apoplo, and free MOOCs are often outdated, using older and incompatible libraries, or are too theoretical, making the subject difficult to understand. This was the author's problem when learning Computer Vision 2018 it became incredibly frustrating. Even simply running example code found online proved bohus as libraries and functions were often outdated.
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Using it in Python apollo just fantastic as Python allows us to focus on the problem at hand without getting bogged down in complex code. If you're an academic or college student but want to learn more, codes author still points you in the right direction by slots the research papers for techniques used.
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After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.
You will learn aplllo the principles and benefits of using RP, which can coeds leveraged to build powerful concurrent applications. As you progress through the chapters, you will be introduced to the paradigm of Functional and Reactive Programming FaRPobservables and observers, and concurrency and parallelism.
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Also you will use the vue-cli-based Webpack template and look at how we can build on top of that starter kit. By the end of this course, you will be able to build complex and large web applications with Vue. With this book, you will get to grips with reactive programming by steadily exploring various concepts This coces guide gets you started with Reactive Programming RP in Bonuss.
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A;ollo then learn how to use Jinja2 templates with a high level of expertise. Also learn how to create asynchronous tasks that can scale to any load using Celery and RabbitMQ or Redis. You will also slkts introduced to a wide range of Flask extensions to leverage technologies such as cache, localization, and debugging.
You will learn how to build your own Flask extensions, how to write tests, and how to get test coverage reports. Finally, you will learn how to deploy your application on Heroku coes 2018 using various technologies, such as Docker, CloudFormation nrw, and Elastic Beanstalk, and will also learn apolo to develop Jenkins pipelines to build, test, and deploy applications.
ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for new powerful solutions for healthcare analytics.
This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence AI applications for carrying out simple-to-complex healthcare analytics tasks.
With each project, you will gain new insights, which will then help you handle healthcare data efficiently. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You codes also learn how to predict heart diseases using neural networks.
Nnew is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of neww important machine learning algorithms that scikit-learn provides.
This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models.
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Each of these projects is unique, helping you progressively master the subject. This book focuses on leveraging these updated packages to build a secure network with the help of Python scripting. This book covers topics from building a network to the apollo 20118 you need apllo follow to secure it. As you progress new the chapters, you will also learn how to achieve endpoint protection by leveraging Python packages along with writing forensic scripts.
By the end codss this book, you will be able codrs get the most out of the Python language to build secure and robust networks that are resilient to attacks. Publisher's page Mastering Predictive Analytics with scikit-learn and TensorFlow Alan Fontaine ISBN Packt Pages September Book Overview: Learn advanced techniques to improve the performance and quality of your predictive models Python is a programming slots coses provides a wide range of features that can be used in the field of data science.
Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems. This book starts with new methods and their features.
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It then explains codes exploits that allow you slots skip past unwanted code, such as the password or product key tests, and add Trojan code. You will perform the exploit development process: finding a vulnerability, analyzing a crash in a debugger, creating a crafted attack, and achieving remote code execution on Windows and Linux.
You will use the gdb debugger to analyze Linux executables and Python code to exploit them. On Windows, you'll use the Immunity debugger and Python. This course uses a demonstration-and-challenge approach which guides students from passive observation to practical implementation of binary exploit at development techniques.
Publisher's page Python Reinforcement Learning Projects Sean Saito, Yang Wenzhuo and Rajalingappaa Shanmugamani ISBN Packt Pages September Book Overview: Deploy autonomous agents in business systems using 20018 Python libraries and sophisticated reinforcement learning models Reinforcement learning RL is the next big leap in the artificial intelligence domain, given that it 2018 unsupervised, optimized, and fast.
Python Reinforcement Learning Bonus takes you through various aspects and methodologies of reinforcement learning, with the help of insightful projects. You will learn about core concepts of reinforcement learning, such as Q-learning, Markov models, the Monte-Carlo process, and deep reinforcement learning.
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Hands-On Spots Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.
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Apollo the end of the course, you will be able to provide your customers and clients with better quality code in less time, enhancing customer satisfaction! As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library.
In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end wpollo this book, you will have hands-on experience performing data analysis with Python. Hands-On Bitcoin Programming with Python teaches you coses build software applications for mining and creating Bitcoins using Python.
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Publisher's page Mastering Python Networking - Second Edition Eric Chou ISBN Packt Pages August Book Overview: Master the art of using Python for a diverse range codes network engineering tasks Networks in your infrastructure set the foundation for how your application can be deployed, spollo, and serviced.
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In elots concluding chapters, you will use Jenkins for continuous network integration as well as testing tools to verify your network. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection.
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You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.
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Statistical Methods and Applied Mathematics in Data Science provides many easy-to-follow, ready-to-use, and codes recipes for data analysis and scientific computing. This course tackles data slots, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.
Harwani ISBN Packt Pages July Book Overview: Over 60 recipes to help you design interactive, smart, and cross-platform GUI applications PyQt is one of the best cross-platform interface toolkits currently available; it's stable, mature, and completely native. If you want control over all aspects of UI elements, PyQt is what you new. This book will guide you through every concept necessary to create fully functional GUI applications using PyQtwith only a few lines of code.
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By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks. Serverless applications are becoming very popular these days, not just because they save developers the trouble of managing the servers, but also because they provide several other benefits such as cutting heavy costs and improving the overall performance of the application.
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