Learning Data Mining With Python

Author: Robert Layton
Editor: Packt Publishing Ltd
ISBN: 178712956X
File Size: 55,62 MB
Format: PDF, Mobi
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Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models. About This Book Use a wide variety of Python libraries for practical data mining purposes. Learn how to find, manipulate, analyze, and visualize data using Python. Step-by-step instructions on data mining techniques with Python that have real-world applications. Who This Book Is For If you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected. What You Will Learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Perform object detection in images using Deep Neural Networks Find meaningful insights from your data through intuitive visualizations Compute on big data, including real-time data from the internet In Detail This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations. Style and approach This book will be your comprehensive guide to learning the various data mining techniques and implementing them in Python. A variety of real-world datasets is used to explain data mining techniques in a very crisp and easy to understand manner.

Machine Learning With Python

Author: ML and AI Academy
Editor:
ISBN: 9781801878906
File Size: 18,77 MB
Format: PDF, Docs
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Statistics Data Mining And Machine Learning In Astronomy

Author: Željko Ivezić
Editor: Princeton University Press
ISBN: 0691197059
File Size: 55,26 MB
Format: PDF, ePub, Mobi
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Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest. An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date. Fully revised and expanded Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from astronomical surveys Uses a freely available Python codebase throughout Ideal for graduate students, advanced undergraduates, and working astronomers

Data Mining With Python

Author: Saimadhu Polamuri
Editor:
ISBN:
File Size: 73,90 MB
Format: PDF, Docs
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"In this course, you will discover the key concepts of data mining and learn how to apply different data mining techniques to find the valuable insights hidden in real-world data. You will also tackle some notorious data mining problems to get a concrete understanding of these techniques. We begin by introducing you to the important data mining concepts and the Python libraries used for data mining. You will understand the process of cleaning data and the steps involved in filtering out noise and ensuring that the data available can be used for accurate analysis. You will also build your first intelligent application that makes predictions from data. Then you will learn about the classification and regression techniques such as logistic regression, k-NN classifier, and SVM, and implement them in real-world scenarios such as predicting house prices and the number of TV show viewers. By the end of this course, you will be able to apply the concepts of classification and regression using Python and implement them in a real-world setting."--Resource description page.

Python Real World Data Science

Author: Dusty Phillips
Editor: Packt Publishing Ltd
ISBN: 1786468417
File Size: 16,48 MB
Format: PDF, Mobi
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Unleash the power of Python and its robust data science capabilities About This Book Unleash the power of Python 3 objects Learn to use powerful Python libraries for effective data processing and analysis Harness the power of Python to analyze data and create insightful predictive models Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Who This Book Is For Entry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Python's data science capabilities for doing real-world data analysis. What You Will Learn Install and setup Python Implement objects in Python by creating classes and defining methods Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis Create effective visualizations for presenting your data using Matplotlib Process and analyze data using the time series capabilities of pandas Interact with different kind of database systems, such as file, disk format, Mongo, and Redis Apply data mining concepts to real-world problems Compute on big data, including real-time data from the Internet Explore how to use different machine learning models to ask different questions of your data In Detail The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it's time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls. Style and approach This course includes all the resources that will help you jump into the data science field with Python and learn how to make sense of data. The aim is to create a smooth learning path that will teach you how to get started with powerful Python libraries and perform various data science techniques in depth.

Hands On Data Science And Python Machine Learning

Author: Frank Kane
Editor: Packt Publishing Ltd
ISBN: 1787280225
File Size: 62,95 MB
Format: PDF, ePub, Docs
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This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.

Learn By Examples A Quick Guide To Data Mining With Rapidminer And Weka

Author: Eric Goh
Editor: SVBook Pte. Ltd.
ISBN:
File Size: 80,10 MB
Format: PDF, ePub
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This book aim to equip the reader with RaidMiner and Weka and Data Mining basics. There will be many examples and explanations that are straight to the point. You will be walked through data mining process from data preparation to data analysis (descriptive statistics) and data visualization to prediction modeling (machine learning) using Weka and RapidMiner. Content Covered: - Introduction (What is data science, what is data mining, CRISP DM Model, what is text mining, three types of analytics, big data) - Getting Started (INstall Weka and RapidMiner) - Prediction and Classification (Prediction and Classification) - Machine Learning Basics (Kmeans Clustering, Decision Tree, Naive Bayes, KNN, Neural Network) - Data Mining with Weka (Data Understanding using Weka, Data Preparation using Weka, Model Building and Evaluation using Weka) - Data Mining with RapidMiner (Data Understanding using RapidMiner, Data Preparation using RapidMiner, Model Building and Evaluation using RapidMiner) - Conclusion We will be using opensource tools, hence, you don't have to worry about buying any softwares. The book is designed for non-programmers only. It will gives you a head start into Weka and RapidMiner, with a touch on data mining. This book has been taught at Udemy and EMHAcademy.com. Use the following Coupon to get the Udemy Course at $11.99: https://www.udemy.com/data-mining-with-rapidminer/?couponCode=EBOOKSPECIAL https://www.udemy.com/learn-machine-learning-with-weka/?couponCode=EBOOKSPECIAL

Mastering Data Mining With Python Find Patterns Hidden In Your Data

Author: Megan Squire
Editor:
ISBN: 9781785889950
File Size: 51,39 MB
Format: PDF, ePub
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Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniquesAbout This Book- Dive deeper into data mining with Python - don't be complacent, sharpen your skills!- From the most common elements of data mining to cutting-edge techniques, we've got you covered for any data-related challenge- Become a more fluent and confident Python data-analyst, in full control of its extensive range of librariesWho This Book Is ForThis book is for data scientists who are already familiar with some basic data mining techniques such as SQL and machine learning, and who are comfortable with Python. If you are ready to learn some more advanced techniques in data mining in order to become a data mining expert, this is the book for you!What You Will Learn - Explore techniques for finding frequent itemsets and association rules in large data sets- Learn identification methods for entity matches across many different types of data- Identify the basics of network mining and how to apply it to real-world data sets- Discover methods for detecting the sentiment of text and for locating named entities in text- Observe multiple techniques for automatically extracting summaries and generating topic models for text- See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set In DetailData mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy - without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding.If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries.In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get.By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics.Style and approach This book will teach you the intricacies in applying data mining using real-world scenarios and will act as a very practical solution to your data mining needs.

Python Machine Learning

Author: Samuel Hack
Editor:
ISBN: 9781801728522
File Size: 67,49 MB
Format: PDF, ePub
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Discover the Incredible World of Machine Learning With This Amazing Guide

A Quick Guide To Data Mining With Weka And Java Using Weka

Author: Eric Goh
Editor: SVBook
ISBN:
File Size: 78,98 MB
Format: PDF, Mobi
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This technical book aim to equip the reader with Weka, Data Mining in a fast and practical way. There will be many examples and explanations that are straight to the point. Contents 1. Introduction (What is data science, what is data mining, CRISP DM Model, what is text mining, three types of analytics, big data) 2. Getting Started (INstall Weka) 3. Prediction and Classification (Prediction and Classification) 4. Machine Learning Basics (KMeans Clustering, Decision Tree, Naive Bayes, KNN, Neural Network) 5. Data Mining with Weka (Data Understanding using Weka, Data Preparation using Weka, Model Building and Evaluation using Weka) 6. Java interact Weka (Use Java to use Weka, in order to develop your own prediction or classification system) 7. Conclusion This book has been taught at Udemy and EMHAcademy.com. Use the following Coupon to get the Udemy Course at $11.99: https://www.udemy.com/machine-learning-with-java-and-weka/?couponCode=SPECIALCOUPON

Python For Data Mining Quick Syntax Reference

Author: Valentina Porcu
Editor: Apress
ISBN: 1484241134
File Size: 48,81 MB
Format: PDF, ePub
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​Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis. Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning. What You'll Learn Install Python and choose a development environment Understand the basic concepts of object-oriented programming Import, open, and edit files Review the differences between Python 2.x and 3.x Who This Book Is For Programmers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.

Python For Data Analysis

Author: Matt Algore
Editor:
ISBN: 9781801689694
File Size: 32,25 MB
Format: PDF, ePub, Mobi
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Python For Data Analysis

Author: Matt Algore
Editor:
ISBN: 9781801583749
File Size: 47,91 MB
Format: PDF, ePub, Docs
Read: 1957
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Talking about the IT world, there are many options when you have to choose language programming to learn and then to use for developing your career, especially if you want to become a Data Scientist. Python is one of the topmost languages and is becoming more and more popular because of plenty of reasons and one of the key reasons is that it is the best language to master if you want to analyze the data or get into the field of data analysis and data sciences. This Handbook will not only give you reasons on why you need to learn data science, but it will also tell you why learning data science with Python training is the better option. In this book you will: - Have a Clear and Exhaustive Explanation About Data Analysis and Why It Is So Important Today in The Business World; organizations of all sizes rely on the insights they extract from the data they have to measure progress, make informed decisions, plan for the future, and so on. Data scientists are the people who process and organize the data with scientific methods, algorithms, and other techniques. - Understand Why Python is Preferred to Use For Data Analysis Over Other Toolsand the reasons why all the benefits of using Python made it the best tool to learn data science. - Find a Step by Step Process to Install Python on Your Computer and a complete analysis of its hundreds of different libraries and frameworks which is a great addition to your development process. There's one library and framework for every need! - Have a Complete and Exhaustive List of Python Application to realize how this tool is flexible if you want to try something creative that's never done before. Due to that, it's possible to build data models, systematize data sets, create ML-powered algorithms, web services, and apply data mining to accomplish different tasks in a brief time for any kind of business organization - Learn How to Carry Out Work More and More Complex and Difficult to be updated on new themes and trends in the sector and carry out small independent jobs to finance your projects. - & Lot More! 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? Python is one of the most valuable and interesting languages for data analysis. Therefore, the popularity of Python is growing day by day, especially in the world of data analysis or data sciences. This Definitive Guide will combine Data Analysis and Python to give you the best information you could find. This guide is perfect to help you build amazing products and help businesses Order Your Copy Now and Start Becoming a Successful Python Expert!

Data Science Using Python And R

Author: Chantal D. Larose
Editor: Wiley
ISBN: 1119526817
File Size: 78,16 MB
Format: PDF, ePub, Mobi
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Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

Python Data Science

Author: John Yoon
Editor:
ISBN:
File Size: 22,92 MB
Format: PDF, ePub, Docs
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Python, an integration with Excel, R and machine learning, is used to collect, process, visualize and report for data sciences and data mining. This book covers major concepts of data mining, with Python codes of MapReduce, Abstractions, Aggregations, Classifications, Clustering, Visualizations, and more in various data formats.

Python For Data Science The Ultimate Beginner S Guide To Learn Data Science Analysis And Machine Learning From Scratch With Step By Step Exe

Author: John Russel
Editor: Python Programming
ISBN: 9781913922443
File Size: 21,55 MB
Format: PDF, Mobi
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Looking for methods that you can use to make yourself more competitive in your industry? Or are you worried about what your customers may think about your products or services? The truth is...In the modern world, data has become fundamental, and companies are finding new ways to use the insights provided by data to improve their bottom line and customer experience. This is sometimes a challenge. There is so much data that figuring out what steps to take, and what is found in that data is not always as easy as we would like. The good news is that working with data science can help you learn more about your customers and your industry, with the use of a simple coding language, and give you the insights and predictions that you need to see some great improvements with your business. And that's what you'll learn in Python for Data Science. Python for Data Science is going to spend some time looking at all of the neat things that we can do with data science, so you can finally beat out the competition and increase your bottom line, all while helping out the customer. You will learn: Why Data Science is so Important in our Fast-Paced World of Today 5 Practical Applications of Data Science Best Data Science Library that will help us to Get our Work Done with Python A Simple Technique to Set Up our Virtual Environment Proven Strategies to Get High-Quality Data Tips and Tricks to Get your Data Organized Data Analytics and Why It is so Important What Machine Learning is all About and How it Fit into your Data Science Projects Learning how data science works and how to complete your own data analysis in the process is going to be very important to the amount of success that you are going to see. Even if you're completely new to data science or you've done some programming before and are looking to switch to an exciting new career track in Data Science, Python for Data Science will teach you all the practical techniques used by real-world data scientists and analysts to solve problems. Would You Like to Know More? Get this Book Now to Master Python for Data Science!

Machine Learning Cookbook With Python

Author: Rehan Guha
Editor: BPB Publications
ISBN: 9389898005
File Size: 25,90 MB
Format: PDF, Kindle
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A Cookbook that will help you implement Machine Learning algorithms and techniques by building real-world projects KEY FEATURES Learn how to handle an entire Machine Learning Pipeline supported with adequate mathematics. Create Predictive Models and choose the right model for various types of Datasets. Learn the art of tuning a model to improve accuracy as per Business requirements. Get familiar with concepts related to Data Analytics with Visualization, Data Science and Machine Learning. DESCRIPTION Machine Learning does not have to be intimidating at all. This book focuses on the concepts of Machine Learning and Data Analytics with mathematical explanations and programming examples. All the codes are written in Python as it is one of the most popular programming languages used for Data Science and Machine Learning. Here I have leveraged multiple libraries like NumPy, Pandas, scikit-learn, etc. to ease our task and not reinvent the wheel. There are five projects in total, each addressing a unique problem. With the recipes in this cookbook, one will learn how to solve Machine Learning problems for real-time data and perform Data Analysis and Analytics, Classification, and beyond. The datasets used are also unique and will help one to think, understand the problem and proceed towards the goal. The book is not saturated with Mathematics, but mostly all the Mathematical concepts are covered for the important topics. Every chapter typically starts with some theory and prerequisites, and then it gradually dives into the implementation of the same concept using Python, keeping a project in the background. WHAT WILL YOU LEARN Understand the working of the O.S.E.M.N. framework in Data Science. Get familiar with the end-to-end implementation of Machine Learning Pipeline. Learn how to implement Machine Learning algorithms and concepts using Python. Learn how to build a Predictive Model for a Business case. WHO THIS BOOK IS FOR This cookbook is meant for anybody who is passionate enough to get into the World of Machine Learning and has a preliminary understanding of the Basics of Linear Algebra, Calculus, Probability, and Statistics. This book also serves as a reference guidebook for intermediate Machine Learning practitioners. TABLE OF CONTENTS 1. Boston Crime 2. World Happiness Report 3. Iris Species 4. Credit Card Fraud Detection 5. Heart Disease UCI

Soft Computing And Machine Learning With Python

Author: Zoran Gacovski
Editor: Arcler Press
ISBN: 9781773615004
File Size: 63,55 MB
Format: PDF, ePub, Mobi
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Soft Computing and Machine Learning with Python examines various aspects of machine learning with python with a detailed information on soft computing. It includes four different sections, where section 1 and 2 are dedicated towards soft computing theory and machine learning techniques and on the other hand section 3 and 4 are dedicated to the details of python language and machine learning with python. Provides the reader with the insights into the development of python and machine learning, so as to understand the classification multigraph models of secondary RNA structure using graph-theoretic descriptors.

Mastering Pycharm

Author: Quazi Nafiul Islam
Editor: Packt Publishing Ltd
ISBN: 1783551321
File Size: 40,19 MB
Format: PDF, Mobi
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Use PyCharm with fluid efficiency to write idiomatic python code About This Book Understand how PyCharm works and how you can leverage its strength to develop applications quickly Master PyCharm's editor to get a fast workflow Full of examples and illustrations that focus on the practical aspects of using PyCharm Who This Book Is For If you know PyCharm but want to understand it better and leverage its more powerful but less obvious tool set, this is the book for you. Serving as a launch pad for those who want to master PyCharm and completely harness its best features, it would be helpful if you were familiar with some of Python's most prominent tools such as virtualenv and Python's popular docstring formats such as reStructuredText and EpyType. What You Will Learn Understand the internal workings of the IntelliJ Platform Leverage PyCharm's powerful search tools, and learn which ones are the best for you and your workflow Customize PyCharm's enhanced Python interpreter and its inbuilt terminal Develop web applications quickly and easily with different frameworks such as Flask and Django Understand how code completion works in PyCharm for Python and JavaScript In Detail PyCharm is addictive, with powerful and configurable code completion, superb editing tools, top-notch support, diverse plugins, and a vibrant ecosystem to boot. Learning how PyCharm works and maximising the synergy of its powerful tools will help you to rapidly develop applications. From leveraging the power of the editor to understanding PyCharm's internals, this book will give you a comprehensive view of PyCharm and allow you to make your own choices about which workflow and tools are best for you. You will start by getting comfortable with PyCharm and making it look exactly like you want. You can customize the tools and taskbars to suit individual developers' coding styles. You also learn how to assign keyboard shortcuts. You will master debugging by inserting breakpoints, collecting runtime data, and debugging from the console. You will understand how PyCharm works underneath and how plugins such as Codemap, Vim, Bitbucket, Assets compressor, markdown, bash file, shortcut translator, and .gitignore leverage the power of the IntelliJ platform. You will become comfortable using the VCS interface in PyCharm and see the benefits of using it for some simple tasks as well as some more complex tasks such as partial commits using changelists. You will take an in-depth look at the various tools in PyCharm, improving your workflow drastically. Finally, you will deploy powerful PyCharm tools for Django, Flask, GAE, and Pyramid Development, becoming well acquainted with PyCharm's toolset for web development with popular platforms. Packed with insider tricks, this book will help you boost productivity with PyCharm. Style and approach An easy-to-follow guide with plenty of examples and screenshots. Each topic starts off with the goal of enhancing or changing a part of PyCharm to make it suit your needs.

Practical Machine Learning For Data Analysis Using Python

Author: Abdulhamit Subasi
Editor: Academic Press
ISBN: 0128213809
File Size: 27,80 MB
Format: PDF, ePub, Docs
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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. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features