| Python For Data Science: 3 Books in 1 - The Ultimate Beginners’ Guide & a Comprehensive Guide of Tips and Tricks & Advanced and Effective Strategies of Using Python Data Science Theories|
by Ethan Williams
$0.00, 485 pages, 5.0 out of 5.0 (1 reviews), #121 in Python Computer Programming
Python For Data Science - The Ultimate Beginners’ Guide to Learning Python Data Science Step by Step
According to a report published by LinkedIn, data science is one of the fastest growing tech fields within the past 7 years. The need for companies to have a better understanding of data generated via their business has motivated a lot of interest in the field. However, there is a gap to be breached as the supply of competent data scientists is way lower than the demand. This makes data science a very in-demand skill, with generous compensation for the few that possess the relevant portfolio.
Remember, data science leverages the exceptional processing and data manipulation capacity of computers? To do this, the data scientist must communicate the task in a clear and logical manner to the computer.
The outline of this book is detailed below, and it is a guide for maximizing your use of this book depending on your level in programming. On this note, I wish you Godspeed as you journey through this book to becoming a data scientist with Python.
This book is a comprehensive guide for beginners to learn Python Programming, especially its application for Data Science.
Python For Data Science - Comprehensive Guide of Tips and Tricks using Python Data Science Theories
Python for Data Science is a comprehensive guide about how to perform data science with Python. This book is for students, researchers, and developers who are technically-minded, and have a wide background in writing code as well as using numerical and computational tools. However, many of you may don't wish to learn Python, but instead wish to learn the language in hopes of utilizing it as a means for computational and data-intensive science.
The aim of this book is not meant to serve as a kind of introduction to Python or even programming in general; we presume readers will get their hands on this book already possess ample amount of knowledge in the Python language, which includes assigning variables, defining functions, controlling a program's flow, calling methods of objects, and other basic operations. Rather, the book was put together to assist users of Python to understand how to use the data science stack of Python – with libraries including NumPy, pandas, Matplotlib and other such tools – with the aim of effectively manipulating, storing, and getting data insight.
In this book, we'll cover a variety of topics, including several libraries, such as NumPy that offers the ndarray for efficient manipulation and storage of dense data arrays in Python. Then you'll be able to learn how to manipulate data using Pandas, a library that offers the DataFrame object for efficient manipulation and storage of columnar/label data in Python.
We are confident that you will make fine a data scientists going forward!
Python For Data Science - Advanced and Effective Strategies of Using Python Data Science Theories
Years ago, when the concept of data science was first introduced, it only meant gathering statistical data and cleaning data sets. It was simply just the science of collecting and presenting data. Together with technology evolution, however, paired with the increasing number of information we now have and continuously acquire, data science means so much more.
Business intelligence is the process of collecting, integrating, and analyzing data for managers and executive officers with the primary goal of using this data for business decisions. If you look at the definition, it is very similar to what data science does as well. It acquires information, processes it, analyzes it, and also presents the data to relevant people to make smart decisions.
What this guide intends to do is go in-depth on some of the more popular advanced data science theories using Python, as well as an overview of machine learning, algorithms, and an in-depth tutorial on using SciPy to optimize your data so, let’s dive in.
| Deep Learning With Python: 3 Books in 1- The ultimate beginners step by step guide & Comprehensive Guide of Tips and Tricks & Advanced and Effective Strategies of Using Deep Learning with Python|
by Ethan Williams
$0.00, pages, 0.0 out of 5.0 (0 reviews), #86 in Python Computer Programming
Deep Learning with Python - The ultimate beginners guide to Learn Deep Learning with Python Step by Step
You have made a perfect choice to consider learning Python and most importantly to develop your skills in the programming world. A good choice comes with good tidings since you have you are looking at a highly comprehensive beginners’ guidebook that will provide you with all the necessary steps and tips to get started. Deep Learning with Python - The ultimate beginners guide to Learn Deep Learning with Python Step by Step is packed with basic beginners’ concepts, detailed examples and extra reminder exercises.
Newbies are totally welcome to dive in! You do not need any experience with programming whatsoever. Just have a notepad ready because taking short notes helps and get ready to play around with the samples and do a whole lot of coding!
The program was developed in December 1989 by Guido van Rossum. Guido’s passion and hobby was to write and learn new codes that were available during his time. It is documented that he developed the python programming language while interacting and learning the ABC programming language.
This is one of the best languages that you can choose to begin learning and at the end have a successful career in it. In summary, since the programming language was open-sourced, we expect a lot of advancements and developments on the language that will make it simpler and easier to use over the coming years.
Deep Learning with Python - Comprehensive Guide of Tips and Tricks using Deep Learning with Python Theories
This book is designed to help you use Python for deep learning, including how to build and run deep learning models using Keras. This book also includes deep learning techniques, sample code, and technical content.
The mathematical foundations of deep learning are subtle: but the average user doesn't need to fully understand the mathematical details to pick up the keyboard and start programming. Practically speaking, deep learning is not complicated, but the results are very objective. Teach you how to use deep learning: this is the purpose of this book.
Deep Learning with Python - Advanced and Effective Strategies of Using Deep Learning with Python Theories
This book discusses the intricacies of the internal workings of a deep learning model. It addresses the techniques and methods that can not only boost the productivity of your machine learning architectural skills, but also introduces new concepts. Implemented correctly, these can set your deep learning model a league apart from all other models. This book not only focuses on theoretical and conceptual realms of such knowledge, but also gives equal importance to putting this information to the test. We do this by including some common practical examples and demonstrations that you would normally build deep learning for, hence giving you the best of both worlds. The main features of this book include:
•Refreshing the fundamentals of a deep learning model and neural networks and connecting them with the advanced knowledge laid out in this book, reinforcing the reader’s prior knowledge and transforming it into an expert-level understanding.
•Emphasizing those tasks that are commonly demanded from deep learning models and breathing new life into them by introducing new techniques, methods, and elements that enable the model to drastically improve the performance of deep learning models on such tasks.
•No usage of mathematical notations in the examples detailed in this book so that the concepts can be readily assimilated and mastered by programmers that do not have a mathematical background, hence prioritizing clarity of concepts.
•Keeping this requirement in mind, the examples use Numpy code throughout as it best represents what the code actually means and its purpose.
If you want to learn advanced strategies for Python this is the book for you.
| Python Data Analytics: 3 books in 1 - The Ultimate Guide to Learn Python Data Analytics & Comprehensive Guide of Tips and Tricks & Advanced and Effective Strategies of Using Python Data Analytics|
by Ethan Williams
$0.00, pages, 0.0 out of 5.0 (0 reviews), #97 in Python Computer Programming
PYTHON DATA ANALYTICS - The Ultimate Guide to Learn Python Data Analytics
Have you ever thought about data analytics? Are you looking for an excellent tool to use in your data analysis? Well, you have come to the right place. Python is one of the best tools that you can use for your data analysis for several reasons;
Flexibility & Ease of learning If you are trying something creative that no one has ever done before, then Python is the best way to go. It also ideal for any developer that is looking for a program that will allow them to script websites and applications. The best thing that I love about Python is its readability and simplicity, which goes a long way in boosting a gradual and relatively low learning curve.
It is open source This means that Python is an open-source program that also has built a valuable community-based model. It is designed to run on different OS ranging from Windows to Linux environments. The good thing with this language is that you can easily port it to a wide range of platforms.
It is well-supported Did you know that anything that could go wrong goes wrong? Think about it, if you are using something that you did not have to pay for, will you get the help that you need quickly? Well, the truth is a definite-NO!
PYTHON DATA ANALYTICS - Comprehensive Guide of Tips and Tricks using Python Data Analytics Theories
Have you always wondered what it is that you can do with the vast volumes of data that you have collected? Is there some way to make it easier to visualize the data to understand it better? If you answered yes to these questions, you have come to the right place.
Data can be collected from different sources and devices, and it is important to understand and analyze that data. The data collected has a lot of information that will need to be uncovered to make better decisions in the future. Before you look at the different types of data analytics, it is important that you understand what big data isThis book will help you learn more about how you can do this.
Throughout the book, you will gather information about:
•What is data, and the different forms of data
•An introduction to big data, big data analytics and data science
•An in-depth analysis and understanding of big data analytics
•The differences between big data, data science and data analytics
•An introduction to Python
•How to work with functions, strings and data structures
•Understanding data mining
•What data integration is
•How to work on predictive analytics
•Developing a simple linear regression, multiple regression, and classification algorithm in Python
This book will help you learn more about data analytics and what you can use it for. So, what are you waiting for? Grab a copy of this book to get started today.
PYTHON DATA ANALYTICS - Advanced and Effective Strategies of Using Python Data Analytics
Does your business have large volumes of data that nobody knows how to use? Do you collect data from various sources to perform the analysis? Have you always wondered what you should do with incorrect data sets? If you answered yes, then you have come to the right place.
You will learn the different processes and steps you must take to analyze different types of data. In this book, you will learn more about:
•What is data analytics, and why is it important?
•The different types of data analytics
•Different algorithms used to perform data analytics
•Identifying different sources of data and mining the required information
•Preparing the data
•Visualizing the data
•An introduction to Python
•Using Python to clean and manipulate data
•Developing a simple predictive model in Python
You will learn all this and more in the book. So, what are you waiting for? Grab a copy of this book now.
| Machine Learning With Python: 3 Books in 1 - The Ultimate Beginners Guide & a Comprehensive Guide of Tips and Tricks & Advanced and Effective Strategies Using Machine Learning with Python|
by Ethan Williams
$0.00, 462 pages, 0.0 out of 5.0 (0 reviews), #109 in Machine Theory (Books)
MACHINE LEARNING WITH PYTHON - The Ultimate Beginners Guide to Learn Machine Learning with Python Step by Step
We live in a world of data deluge where gigabytes of data are generated daily. It is possible that this data might not be very useful for our daily applications. Major setbacks in the use of such data may be due to the presence of loopholes in data links previously generated or the data might be too vast for the limited human mind. Machine learning in this book presents some of the solutions to the problems above. Being an introductory guide, expect to learn the various basics involved in Machine Learning and Python.
This book provides an insight into the new world of big data, then behooves you to learn more about Machine Learning. You will be able to get answers to the following questions:
•What is Machine Learning and what does it entail?
•How can I apply machine learning to have a glimpse into the new world, power my enterprise or find out how the Internet thinks about my academic research work?
Be ready to learn all that it takes to be an expert in the field of Machine Learning!
MACHINE LEARNING WITH PYTHON - Comprehensive Guide of Tips and Tricks of using Machine Learning Theories with Python
Machine learning is a branch of artificial intelligence that designs algorithms that improve their performance based on empirical data. Machine learning is one of the most active and exciting fields of computer science today, mainly because of its many application options ranging from pattern recognition and in-depth data analysis to robotics, computational vision, bioinformatics, and computational linguistics. Machine learning is above all a discipline that can contribute to many domains and has very challenging applications. This is the area where most publications in academia are concerned with artificial intelligence, and all major companies, such as Google, Facebook or Microsoft, apply machine learning methods in their applications. This book covers the theory, principles and tricks to machine learning and provides an overview of its applications in Python.
In the field of data science, it comes quite natural that you should learn Python. If you're wondering why Python is the answer, the answer is that there are already ready packages (statistical and numerical) for analyzing data such as PyBrain, NumPy, and MySQL. Machine learning integrates computers and statistics that allow computers to learn new tasks. There are Python modules - such as Scikit-learn, Tensorflow, and Theano - that support machine learning so that you can do cool things such as spam detection and fingerprint identification. So these are some of the concepts that you will master reading this book.
MACHINE LEARNING WITH PYTHON - Advanced and Effective Strategies Using Machine Learning with Python Theories
Are you eager to use advanced Machine Learning methods with Python? Are you looking forward to automating simple things using the power of the keyboard, but you have no idea how to achieve it?
Machine learning is a vast field and expanding at supersonic speed. Python evolution is an ongoing process and lives up to the hype. The field goes beyond robotics and data finance to finance applications.
When you use machine learning and python programming in the right way, they have the capability of changing the lives of people around the world. In this advanced book, we are going to break down the advanced features of this new technology to advance your skills as an IT enthusiast. You will discover:
•How we classify machine learning algorithms
•How we can apply machine learning in different areas
•Understanding the artificial neural networks
•The use of convoluted neural networks
•Building predictive models
•Autoencoders in ML and Python
•K-Means techniques and Natural Language Processing
•The art of feature engineering
•The ensemble methods
| 80% Python in 20 Minutes: all you need to start reading and writing Python|
by Weiran Ye
$0.00, 41 pages, 0.0 out of 5.0 (0 reviews), #5 in Python Computer Programming
Being a programmer for many years, when I started to use Python, I wish there was a Python book that
instead, a Python book that
If that resonates with you, this book is for you!
This book assumes you know how to write code in one of the programming languages other than Python, such as Java,
| NODE.JS AND HTML FOR BEGINNERS: 2 BOOKS IN 1 - Learn Coding Fast! NODE.JS AND HTML Crash Course, A QuickStart Guide, Tutorial Book by Program Examples, In Easy Steps!|
by TAM SEL
$0.00, 192 pages, 4.0 out of 5.0 (1 reviews), #5 in HTML Programming
YOU WILL SAVE 33% WITH THIS OFFER.
This Books Absolutely For Beginners:
“NODE.JS FOR BEGINNERS” covers all essential NODE.JS language knowledge. You can learn complete primary skills of NODE.JS programming fast and easily. The book includes practical examples for beginners and includes Interview questions & answers for the college exam, the engineer certification, and the job interview.
"HTML FOR BEGINNERS " covers all essential HTML language knowledge. You can learn complete primary skills of HTML programming fast and easily.
TABLE OF CONTENTS
Install Node.js on Windows
Node.js First Example
Node.js Multiline expressions
Node.js Package Manager
Node.js Command Line Options
Node.js setInterval(), setTimeout() and clearTimeout()
Node.js Child Process
Node.js File System (FS)
Node.js Assertion Testing
Node.js Web Module
Node.Js Create Connection with MySQL
Node.js MySQL Create Database
Node.js MySQL Insert Records
Node.js MySQL Update Records
Node.js MySQL Delete Records
Node.js MySQL Select Records
Node.js MySQL SELECT Unique Record
Node.js MySQL Drop Table
Node.js vs AngularJS
Node.js vs Python
Node.js vs PHP
Node.js vs Java
Node.js Interview Questions
HTML FOR BEGINNERS
HTML Example with HTML Editor
History of HTML
Features of HTML
How to add CSS
HTML Interview Questions
CSS Interview Questions
I started making these lists for my own use in 2013 when an Amazon policy change gutted all of the web sites that listed free Kindle books. Before the policy change, those sites would get some amount of compensation for directing people to the Amazon web site. After the policy change, they lost all compensation for the month if most of their referrals that month were for free books. So most of those web sites stopped listing the free books.
I use the description "newly free" for these book lists. That means I won't list a book again if I've listed it recently. There are so many books that are permanently free, or frequently free, that it makes it difficult to find "newly free" books manually looking through the Amazon search results. There are just too many repeats every day. But I have included the links to those Amazon searches on the category headers, so you can always click the link to see the full list if you'd like. My process just extracts the "newly free" books from those search results, by keeping a record of books I've listed in the past.
The lists are designed for easy navigation through the books. Clicking on the blurb of any given book will bring the next book in the list up to the top of the screen. This makes it easy to quickly go through them one-by-one. Each category header has an index of the various categories available, and also shows the number of books available in each category. Clicking on the category header itself will bring up the full Amazon search listing of free books for that category.
If you're in Canada, Germany, or the UK, clicking on the "CA" or "DE" or "UK" link in the top right hand corner of the document will change all of the book links from USA to Canada or Germany or the UK. But they may not be free there as they are in the USA. Usually, but not always.
If an author classifies their book as belonging to multiple genres (e.g. fantasy, horror), it will only be listed in the first genre that I process. I process the sub-categories of a major category first, so that the book will be listed in the more specific category.
The rating and # of reviews will be highlighted green to flag ratings above 4.5 stars with more than 50 reviews.
Horizontal bars above the blurbs may be red. You can ignore that. They're just reminders to me that I have already purchased that book.
Three ways to search for free (or Prime) books on Amazon:
Currently, there are about 90,000 free Kindle books, a thousand "Prime Reading" books, and over 1.6 million "Prime Eligible" books. With only a few exceptions, "Kindle Unlimited" means "Prime Eligible". "Prime Reading" offers unlimited loans per month, while "Prime Eligible" is just one loan per month.
From the initial search results, you can drill down into categories and use any of the left navigation bar filters.
Those same searches with a keyword:
Click on the "search" part of the "results for" line and you can change the search term to whatever you desire. Then you can continue as before and use the normal drill-down and/or filter choices.
There are two Reddit groups that regularly have free Kindle books listed:Kindle Freebies
The eReaderIQ web site is a price tracking service for Kindle books. It allows you to set up watch lists for books and authors, and then they will send you an email alert when a book reaches a certain price, or an author has a book with a price reduction.
The BookBub web site will send you email alerts with handpicked recommendations on books, for your selected genres. Some are free, others just have price reductions.
Galaxy Science Fiction was an American digest-size science fiction magazine, published from 1950 to 1980, and they are now offered free online:https://archive.org/details/galaxymagazine?sort=-date
They can be downloaded as mobi files for the Kindle as well as being read online. Used to be one of my favorite SF magazines. I almost always had one with me when I was in high school.
If you're more interested in mainstream books, check to see what options you have from your local library. Many use the Overdrive or Libby sites to loan out Kindle books. If that's not available to you, or your local library has a limited collection, the "Free Library of Philadelphia" has an extensive Overdrive collection, and an out-of-state library card is only $50 per year. Less than half the price of KU.
Project Gutenberg, founded in 1971, is an Internet archive of free public domain books, created via a volunteer effort that digitizes and archives such books. You can download the Kindle format of a book and then transfer the book to your Kindle directly using a USB cable or by emailing it to your Kindle via its Send-to-Kindle email address using your email client or something like the Amazon Send-To-Kindle tool or an eBook management tool like Calibre.
Notifications of new listings will be made in various GoodReads discussion groups (e.g. Amazon Kindle), as well as on the Reddit FreeEBOOKS group. There is also an RSS Feed.