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Profound Python Data Science
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Book Synopsis Profound Python Data Science by : Önder Teker
Download or read book Profound Python Data Science written by Önder Teker and published by Godoro. This book was released on 2023-11-26 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers data science topics in Python language. Libraries such as Numpy, Matplotlib, Pandas, Scipy are explained in detail. In addition to data science, the book contains the usage of many libraries for developers of Python. The basic knowledge needed to use Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision features are covered. The book contains tools for data analysis and business intelligence.
Book Synopsis Python for Data Science by : William Wizner
Download or read book Python for Data Science written by William Wizner and published by . This book was released on 2020-07-15 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to learn Data analysis and Deep learning with Python coding and programming?Are you interested in practical applications on Machine learning and Artificial intelligence.?If yes, then keep reading... Many companies spend a lot of time collecting data and trying to use them to learn more about their customers and learning how to gain a competitive edge over others. Just gathering the data is not going to be enough to make it happen. Instead, we need to be able to take that data, and that data is usually pretty messy and needs some work and analyze it so that we are better able to handle all that comes with it. Data has always been relevant, but today, because of the growth in the internet and other sources, there is an unprecedented amount of data to work through. In the past, companies were able to manually go through the data they had and maybe use a few business intelligence tools to learn more about the customer and to make smart decisions. But nowadays this is nearly impossible. Evolving technologies are going to enable some cost savings for us, and smarter storage spaces to help us store some of this critical data. However, currently, in almost any industry and company, there is a huge need for skilled and knowledgeable data scientists. They are some of the highest-paid IT professionals right now, mainly because they can provide such a good value for the companies they work for, and because there is such a shortage in these professionals. This book covers: What is Data Analysis? The Basics of the Python Language Using Pandas Working with Python for Data Science Indexing and Selecting Arrays ...And Much More! So, ready to get started? Click "Buy Now"!
Book Synopsis Advanced Data Analytics Using Python by : Sayan Mukhopadhyay
Download or read book Advanced Data Analytics Using Python written by Sayan Mukhopadhyay and published by Apress. This book was released on 2018-03-29 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.
Book Synopsis Python for Data Analysis by : Jason Scratch
Download or read book Python for Data Analysis written by Jason Scratch and published by . This book was released on 2021-02-14 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: 55% discount for bookstores! Now at $34,95 instead of 44,95!Are you interested in seeing what machine learning is to be able to help you to get more out of your business?
Download or read book Python Data Science written by James Bolt and published by James Bolt. This book was released on 2021-04-19 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: **55% OFF for Bookstores!! LAST DAYS*** PYTHON DATA SCIENCE Your Customers Never Stop to Use this Awesome Book! Here's the Perfect Solution if You Want to Become the Master of Data Science and Learn Python Step-by-Step Would you like to: Learn a super competitive skill? Become irreplaceable in the future job market? Upgrade yourself to the ultimate data whizz? If so, then keep reading! Data science is one of the emerging technologies that is set to radically transform the job market. With applications in almost every industry, data science experts will have no shortage of great job offers. But, the whole field may seem a little intimidating if your background is not specific to data science. This book is here to guide you through the field of data science from the very beginning. You will learn the fundamental skills and tools to support your learning process. If you're a beginner, this is the book to help you easily understand the basics of data science. To understand data science, you also need a good understanding of how Python helps you design and implement these projects. This guidebook is going to explain how we can get all of this done. Here just a little preview of what you'll find inside this book: A thorough and simple explanation of data science and the way it works Basics of data science and fundamental skills you need to get started Data science libraries you need to learn to become a data whizz A blueprint for the most used frameworks for Python data science How to process and understand the data and design your own projects AND SO MUCH MORE! Buy it Now and let your customers get addicted to this amazing book!
Book Synopsis Profound Python Libraries by : Önder Teker
Download or read book Profound Python Libraries written by Önder Teker and published by Godoro. This book was released on 2022-07-08 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book contains Python libraries used in many applications. Internet, Downloads, JSON, REST are covered. Utilities such as time, random, regular expressions are included. The operating systems & process are explained in detail. File system operations and Pathlib are covered. Some introductions to Big Data & Artificial Intelligence are added. CSV, Samples are explained as a preperation for data science. Visual libraries such as PIL & Matplotlib are included. Speech Recognition is covered. Finally Tk is is explained & a full sample application is supplied.
Book Synopsis Data Science Programming All-in-One For Dummies by : John Paul Mueller
Download or read book Data Science Programming All-in-One For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2020-01-09 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!
Book Synopsis Data Science with Python by : Rohan Chopra
Download or read book Data Science with Python written by Rohan Chopra and published by Packt Publishing Ltd. This book was released on 2019-07-19 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event. Key FeaturesExplore the depths of data science, from data collection through to visualizationLearn pandas, scikit-learn, and Matplotlib in detailStudy various data science algorithms using real-world datasetsBook Description Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book. What you will learnPre-process data to make it ready to use for machine learningCreate data visualizations with MatplotlibUse scikit-learn to perform dimension reduction using principal component analysis (PCA)Solve classification and regression problemsGet predictions using the XGBoost libraryProcess images and create machine learning models to decode them Process human language for prediction and classificationUse TensorBoard to monitor training metrics in real timeFind the best hyperparameters for your model with AutoMLWho this book is for Data Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
Book Synopsis Machine Learning with Python Cookbook by : Chris Albon
Download or read book Machine Learning with Python Cookbook written by Chris Albon and published by "O'Reilly Media, Inc.". This book was released on 2018-03-09 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models
Book Synopsis Deep Learning with Python by : Francois Chollet
Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Book Synopsis Deep Learning for Data Architects by : Shekhar Khandelwal
Download or read book Deep Learning for Data Architects written by Shekhar Khandelwal and published by BPB Publications. This book was released on 2023-08-16 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide to building and deploying deep learning models with Python KEY FEATURES ● Acquire the skills to perform exploratory data analysis, uncover insights, and preprocess data for deep learning tasks. ● Build and train various types of neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). ● Gain hands-on experience by working on practical projects and applying deep learning techniques to real-world problems. DESCRIPTION “Deep Learning for Data Architects” is a comprehensive guide that bridges the gap between data architecture and deep learning. It provides a solid foundation in Python for data science and serves as a launchpad into the world of AI and deep learning. The book begins by addressing the challenges of transforming raw data into actionable insights. It provides a practical understanding of data handling and covers the construction of neural network-based predictive models. The book then explores specialized networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The book delves into the theory and practical aspects of these networks and offers Python code implementations for each. The final chapter of the book introduces Transformers, a revolutionary model that has had a significant impact on natural language processing (NLP). This chapter provides you with a thorough understanding of how Transformers work and includes Python code implementations. By the end of the book, you will be able to use deep learning to solve real-world problems. WHAT YOU WILL LEARN ● Develop a comprehensive understanding of neural networks' key concepts and principles. ● Gain proficiency in Python as you code and implement major deep-learning algorithms from scratch. ● Build and implement predictive models using various neural networks ● Learn how to use Transformers for complex NLP tasks ● Explore techniques to enhance the performance of your deep learning models. WHO THIS BOOK IS FOR This book is for anyone who is interested in a career in emerging technologies, such as artificial intelligence (AI), data analytics, machine learning, deep learning, and data science. It is a comprehensive guide that covers the fundamentals of these technologies, as well as the skills and knowledge that you need to succeed in this field. TABLE OF CONTENTS 1. Python for Data Science 2. Real-World Challenges for Data Professionals in Converting Data Into Insights 3. Build a Neural Network-Based Predictive Model 4. Convolutional Neural Networks 5. Optical Character Recognition 6. Object Detection 7. Image Segmentation 8. Recurrent Neural Networks 9. Generative Adversarial Networks 10. Transformers
Book Synopsis Classic Computer Science Problems in Java by : David Kopec
Download or read book Classic Computer Science Problems in Java written by David Kopec and published by Simon and Schuster. This book was released on 2020-12-21 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Whatever software development problem you’re facing, odds are someone has already uncovered a solution. This book collects the most useful solutions devised, guiding you through a variety of challenges and tried-and-true problem-solving techniques. The principles and algorithms presented here are guaranteed to save you countless hours in project after project. About the book Classic Computer Science Problems in Java is a master class in computer programming designed around 55 exercises that have been used in computer science classrooms for years. You’ll work through hands-on examples as you explore core algorithms, constraint problems, AI applications, and much more. What's inside Recursion, memoization, and bit manipulation Search, graph, and genetic algorithms Constraint-satisfaction problems K-means clustering, neural networks, and adversarial search About the reader For intermediate Java programmers. About the author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. Table of Contents 1 Small problems 2 Search problems 3 Constraint-satisfaction problems 4 Graph problems 5 Genetic algorithms 6 K-means clustering 7 Fairly simple neural networks 8 Adversarial search 9 Miscellaneous problems 10 Interview with Brian Goetz
Book Synopsis Python for Data Science by : Mik Arduino
Download or read book Python for Data Science written by Mik Arduino and published by . This book was released on 2019-11-15 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are tired of reading without understanding and want to learn the value of big data and artificial intelligence simply and quickly, then keep reading... Today 91% of Python programmers are not quite prepared. This is what the IT companies say, according to a recent survey. Do you know why? The reason is that they have no notions of artificial intelligence, so future programmers will no longer find work without having knowledge of this particular field that is constantly evolving. Artificial intelligence has made great strides over the years, and by 2024 it is estimated that 77% of programmers will have to be experts in this field to implement it in the various programming languages. So if you are such a programmer or aspirant and ignore the importance that Python data analysis and artificial intelligence have in our future, then you will be cut off from the business world. The solution? You need to learn these things and, above all, do it in a clear, simple, and practical way. The goal of Python for Data Science is to give you an advanced level training on Python, artificial intelligence, and deep machine learning as quickly as possible. What are some points you will learn in this book? - Artificial Intelligence: How Does it Work? How is it Used? - The Key Elements of Machine Learning - Machine Learning vs Deep Learning - Data Science vs Business Intelligence - The Data Science Lifecycle - The Value of Big Data Explained to a Child - 4 Tips for Data Cleaning and Organizing Your Data - Python Data Analysis 360° - 6 Different Machine Learning Algorithms - How to Handle Data Visualizations Python for Data Science is perfect for those who already look to the future and want to ensure a job for the next 20 years by beating the competition, even if you know nothing about computer codes and you have never turned on a computer in your life. Would You Like to Know More? Download now to find out about Python for Data Science. Scroll to the top of the page and hit the Buy Now button.
Book Synopsis Applied Deep Learning with Python by : Alex Galea
Download or read book Applied Deep Learning with Python written by Alex Galea and published by Packt Publishing Ltd. This book was released on 2018-08-31 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don’t have a data science background Covers the key foundational concepts you’ll need to know when building deep learning systems Full of step-by-step exercises and activities to help build the skills that you need for the real-world Book Description Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work. We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. By guiding you through a trained neural network, we’ll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a tailored machine learning classification strategy Build, train and enhance your own models to solve unique problems Work with production-ready frameworks like Tensorflow and Keras Explain how neural networks operate in clear and simple terms Understand how to deploy your predictions to the web Who this book is for If you're a Python programmer stepping into the world of data science, this is the ideal way to get started.
Book Synopsis Deep Learning with Python by : Mike Krebbs
Download or read book Deep Learning with Python written by Mike Krebbs and published by Createspace Independent Publishing Platform. This book was released on 2018-01-02 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***** Buy now (Will soon return to $47.99 + Special Offer Below) ***** Free Kindle eBook for customers who purchase the print book from Amazon Are you thinking of learning more about Deep Learning From Scratch by using Python and TensorFlow? The overall aim of this book is to give you an application of deep learning techniques with python. Deep Learning is a type of artificial intelligence and machine learning that has become extremely important in the past few years. Deep Learning allows us to teach machines how to complete complex tasks without explicitly programming them to do so. As a result people with the ability to teach machines using deep learning are in extremely high demand. It is also leading to them getting huge increases in salaries. Deep Learning is revolutionizing the world around us and hence the need to understand and learn it becomes significant. In this book we shall cover what is deep learning, how you can get started with deep learning and what deep learning can do for you. By the end of this book you should be able to know what is deep learning and the tools technology and trends driving the artificial intelligence revolution. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images, which detail all-important deep learning concepts and their applications. This Is a Practical Guide Book This book will help you explore exactly the most important deep learning techniques by using python and real data. It is a step-by-step book. You will build our Deep Learning Models by using Python Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach data science, but are too afraid of complex math to start Newbies in computer science techniques and machine learning Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on data science What's Inside This Great Book? Introduction Deep Learning Techniques Applications Next Steps Practical Sentiment Analysis using TensorFlow with Neural Networks Performing Sequence Classification with RNNs Implementing Sequence Classification Using RNNs in TensorFlow Glossary of Some Useful Terms in Deep Learning Sources & References Bonus Chapter: Anaconda Setup & Python Crash Course Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: f you want to smash Data Science from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in data science and further learning will be required beyond this book to master all aspects of data science. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. I will also be happy to help you if you send us an email at [email protected].
Book Synopsis Python for Data Analysis by : Paul Jamsey
Download or read book Python for Data Analysis written by Paul Jamsey and published by . This book was released on 2020-02-26 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you interested in learning more about your competition, and how they can benefit from some of your products and services? Are you interested in seeing what deep learning, machine learning, and data analysis are all about and how they are going to be able to help you to get more out of your business and make good decisions about the future of your company? Would you like to see how all of this is going to come together and make you more profitable than ever? This guidebook is going to be the perfect companion and tool for your needs. You will find that we will talk about all of the topics that you need to know when it comes to working with data analysis and data science in no time and it will not take long before we actually use some of these projects and processes on our own as well. There are so many benefits that come with working in data science, data analysis, and deep learning, and finding time to it it all in and making it work can seem complicated. This guidebook is going to be the tool that you need to get this all under control. Some of the topics that we are going to discuss in this topic and will ensure that we can get this process down includes: What is deep learning How to conduct a data analysis The different Python libraries that you are able to use for deep learning. Understanding some of the math behind neural networks. The basics of working with the TensorFlow library that can help you with your deep learning project. How to handle the Keras library for your needs. The PyTorch library and how this library is going to be able to help us out with machine learning and deep learning. Looking more at machine learning and how we are able to fit this into some of the data analysis that we are talking about. How deep learning is going to be helpful when it is time to handle your own predictive analysis. Deep learning, machine learning, and data analysis are important parts of many businesses today. These topics and processes are going to help us to really explore the industry, the customers, the competition and more that are going to come out when we want to help our business succeed and when we want to figure out what steps we need to take in order to get ahead of the competition. When you are ready to learn more about data analysis and deep learning, make sure to check out this guidebook to help you get started.
Book Synopsis Python Programming: The Easiest Python Crash Course to Go Deep Through the Main Applications as Web Development, Data Analysis, and Data S by : Alan Grid
Download or read book Python Programming: The Easiest Python Crash Course to Go Deep Through the Main Applications as Web Development, Data Analysis, and Data S written by Alan Grid and published by Computer Science. This book was released on 2020-10-06 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why Python has been proclaimed by the most Professional Techs as the best Scripting Language ? Do you want to learn Coding from scratch? This Book is probably what you looking for . Keep reading to discover more about it! Python is presumably the easiest-to-learn and nicest-to-use programming language in widespread use. Python code is clear to read and write, and it is short without being cryptic. It is a very powerful language, which means that we can generally write far fewer lines of Python code than would be needed for an equivalent application written in, say, C++ or Java. Python is typically typed in an implicit and dynamic format; hence, there is no requirement to declare variables. These types are enforced, and the variables are sensitive to cases. There is no definite array of characters used to terminate statements in Python. Any statement which expects a level of indentation is concluded using a colon sign. Multiple variables can also be used on a single line. This book covers the following topics: The 7 main Features of Python Why you should use Python What is the best Python web app framework and why Data Types in Python Conditional Statements Why is Python so popular in Machine Learning ...And much more! In Python Programming, the English language is mainly used in coding many keywords. The mastery of these keywords means knowledge of the fundamental aspects of python programming. However, before delving into these primary keywords, you have to understand the basic concepts associated with Python. These concepts are necessary to understand every other aspect of the scripting language. By reading this book, you're off to a great start. It is designed to ease your way into Python programming world. So, Ready to Become a Master of Python? Click "Buy Now" and Get the Book!
