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  Python for Data Analysis

  Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. * Use the IPython shell and Jupyter notebook for exploratory computing * Learn basic and advanced features in NumPy (Numerical Python) * Get started with data analysis tools in the pandas library * Use flexible tools to load, clean, transform, merge, and reshape data * Create informative visualizations with matplotlib * Apply the pandas groupby facility to slice, dice, and summarize datasets * Analyze and manipulate regular and irregular time series data * Learn how to solve real-world data analysis problems with thorough, detailed examples

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  Python for Data Science: The Ultimate Guide for Beginners. Machine Learning Tools, Concepts and Introduction. Python Programming Crash Course. , Hörbuch, Digital, ungekürzt, 181min

  Are you looking for an ultimate python step-by step guide in an efficient way? Do you want to implement a variety of supervised and unsupervised learning algorithms and techniques quickly and accurately?If you cannot wait to explore the fundamental concepts and entire process on python data science, listen to this audiobook!You will start by learning the basics of working with Python and the wide variety of data science packages and extensions. You will be guided on how to setup you work environment before diving into the world of data science. In each section you will learn a great deal of theory backed up by practical examples that contain well-explained Python code. Once you have the fundamentals down, you will get to the core of data science learning algorithms and techniques that are industry-standard in this field.Studying data science and working with supervised and unsupervised algorithms, as well as neural networks, doesn’t have to be as complicated as it sounds. Explore the world of data science using clear, simple, real-world examples and enjoy the power and versatility of Python and machine learning algorithms!You will explore:How to install Python and setup a scientific distribution.The most popular Python packages and library used in data science and machine learning, such as Scikit-learn, Numpy, Matplotlib, and Pandas.Data munging with pandas and how to import and prepare your dataset for preprocessing and exploration.How to further prepare your data for the data science pipeline by fully understanding concepts such as data exploration, dimensionality reduction, and outlier detection.How to implement supervised and unsupervised machine learning algorithms such as regression algorithms, the Naïve Bayes classifier, K-nearest neighbors, support vector machines, decision trees, and K-means clustering.Neural networks and how to work with feedforward and recurrent networ ungekürzt. Language: English. Narrator: Russell Newton. Audio sample: https://samples.audible.de/bk/acx0/185209/bk_acx0_185209_sample.mp3. Digital audiobook in aax.

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  Python for Data Analysis: A Step by Step Guide to Master the Basics of Data Analysis in Python Using Pandas, Numpy and Ipython (Data Science) , Hörbuch, Digital, ungekürzt, 181min

  If you want to learn more about data analysis or how to master it with the Python programming language, then keep reading.Everyone talks about data today. You have probably come across the term “data” more times than you can remember in one day. Data, as a concept, is so wide. One thing that is true about data is that it can be used to tell a story.Many tools can be used for data analysis. For this reason, the ultimate choice often becomes a challenge for most people. To set you on the right path, the first step is to decide which language you want to learn, then build from there. With Python for Data Analysis, you will learn about the main steps that are needed to correctly implement data analysis and the procedures to help you extract the right insights from the right data. Some of the topics that we will discuss inside include:What data analysis is all about and why businesses are investing in this sectorThe five steps of a data analysisThe seven Python libraries that make Python one of the best choices for data analysisHow data visualization and Matplotlib can help you to understand the data you are working with.Some of the main industries that are using data to improve their business with 14 real-world applicationsWhile most books focus on how to implement advanced predictive models, this book takes the time to explain the basic concepts and all the necessary steps to correctly implement data analysis, including data visualization and providing simple coding scripts. Even if you never used data analysis, learning it is easier than it looks. This practical guide provides all the knowledge you need in a simple and practical way. Regardless of your previous experience, you will learn the steps of data analysis, how to implement them in Python, and the most important real-world applications.If you want to know more about data analysis and how to implemen ungekürzt. Language: English. Narrator: Russell Newton. Audio sample: https://samples.audible.de/bk/acx0/185733/bk_acx0_185733_sample.mp3. Digital audiobook in aax.

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  Python for Data Science: The Ultimate Guide to Learn Data Analysis, Visualization and Mining with Pandas, NumPy, IPython and Scikit-Learn , Hörbuch, Digital, ungekürzt, 226min

  This audiobook gives you the perfect foray into data analysis. We discuss data analysis in Python in a way that will benefit you irrespective of your expertise level in Python. At the beginner level, you will appreciate the simple but elaborate approach we use to introduce you to basic Python concepts necessary for data analysis. With this knowledge, you can establish your foundation in data analysis, and build on that over time as you become accustomed to more complex subjects.For intermediate and expert users, you can also benefit from this book by using it as a reminder of some of the key points that define data science. When you dwell in a field for a long time, it is easy to take some things for granted. This happens to many programmers and developers. This audiobook reminds you of the basic building principles that have helped you become one of the best data analysts in your field.Python libraries are some of the most important features in Python programming. The libraries help you perform tasks that would have otherwise been impossible to perform, or cumbersome. We discuss the major Python libraries you will use all the time, and highlight the main ones relevant to data analysis so you can get the distinction.Take note that data science is not an isolated subject. Most of the disciplines that involve Python programming depend on data, so you can expect to use the knowledge learned in this audiobook in other fields, too. For example, when you advance into machine learning, your ability to perform exceptional data analysis will be required to help you build and train relevant machine learning models. Therefore, this audiobook will not just get you ready for data analysis, it will prepare you for various fields in Python programming, including artificial intelligence, deep learning, and machine learning.Besides discussing the main Python libraries, we investigate the major data analysis libraries like Pandas and Matplotlib in-depth ungekürzt. Language: English. Narrator: Rick Stevens. Audio sample: https://samples.audible.de/bk/acx0/196950/bk_acx0_196950_sample.mp3. Digital audiobook in aax.

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  • Price: 9.95 EUR excl. shipping
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  Python for Data Analysis: A Complete Guide for Beginners, Including Python Statistics and Big Data Analysis , Hörbuch, Digital, ungekürzt, 185min

  Are you struggling to figure out which product you should introduce to the market? Or, on how to reach your customers and get them to pay attention to you rather than the competition? Worried about the amount of waste that is going on in your business and how much it is costing you, or many other business problems? Then completing a good data analysis with the help of the Python language may be the right choice for you. Many parts need to come together when working on data analysis. Some of the topics that we need to explore when it comes to working on a Python data analysis will include:What the Python language is all about and how we are able to utilize it to get a lot of coding doneA look at the data analysis and how we can benefit, no matter what industry we areHow Python is able to work well with the data analysis and why it is the number one language to help you handle thisA look at some of the steps that we are able to utilize when it comes to our data analysis so we can get it all done the right wayHow to install and use the NumPy library, one of the best extensions with Python, to help us get our data analysis doneHow to work with the Pandas and IPython extensions so that we are able to get things done with your analysisThe practical uses of the data analysis to help you get it doneA look at the Matplotlib library to help you create some of your own visuals with your data when the analysis is doneHow to work with data visuals and how they are so important to your work  If you want to learn more about Python for data analysis, then buy this audiobook to get started. ungekürzt. Language: English. Narrator: D. Wolf. Audio sample: https://samples.audible.de/bk/acx0/198450/bk_acx0_198450_sample.mp3. Digital audiobook in aax.

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  Python 3 Crashkurs (eBook, PDF)

  Ihr Weg zum Python-Profi! US-Bestseller Lernen Sie Python programmieren wie die Profis Komplett aktualisiert auf Python 3 "Python Crashkurs" ist eine kompakte und gründliche Einführung, die es Ihnen nach kurzer Zeit ermöglicht, Python-Programme zu schreiben, die für Sie Probleme lösen oder Ihnen erlauben, Aufgaben mit dem Computer zu erledigen. In der ersten Hälfte des Buches werden Sie mit grundlegenden Programmierkonzepten wie Listen, Wörterbücher, Klassen und Schleifen vertraut gemacht. Sie erlernen das Schreiben von sauberem und lesbarem Code mit Übungen zu jedem Thema. Sie erfahren auch, wie Sie Ihre Programme interaktiv machen und Ihren Code testen, bevor Sie ihn einem Projekt hinzufügen. Danach werden Sie Ihr neues Wissen in drei komplexen Projekten in die Praxis umsetzen: ein durch "Space Invaders" inspiriertes Arcade-Spiel, eine Datenvisualisierung mit Pythons superpraktischen Bibliotheken und eine einfache Web-App, die Sie online bereitstellen können. Während der Arbeit mit dem "Python Crashkurs" lernen Sie, wie Sie: - leistungsstarke Python-Bibliotheken und Tools richtig einsetzen - einschließlich matplotlib, NumPy und Pygal - 2D-Spiele programmieren, die auf Tastendrücke und Mausklicks reagieren, und die schwieriger werden, je weiter das Spiel fortschreitet - mit Daten arbeiten, um interaktive Visualisierungen zu generieren - Web-Apps erstellen und anpassen können, um diese sicher online zu deployen - mit Fehlern umgehen, die häufig beim Programmieren auftreten Dieses Buch wird Ihnen effektiv helfen, Python zu erlernen und eigene Programme damit zu entwickeln. Warum länger warten? Fangen Sie an!

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  Python Machine Learning: A Step by Step Beginner's Guide to Learn Machine Learning Using Python (eBook, ePUB)

  Get your copy now Ready to discover the Machine Learning world? Machine learning paves the path into the future and it's powered by Python. All industries can benefit from machine learning and artificial intelligence whether we're talking about private businesses, healthcare, infrastructure, banking, or social media. What exactly does it do for us and what does a machine learning specialist do? Machine learning professionals create and implement special algorithms that can learn from existing data to make an accurate prediction on new never before seen data. Python Machine Learning presents you a step-by-step guide on how to create machine learning models that lead to valuable results. The book focuses on machine learning theory as much as practical examples. You will learn how to analyse data, use visualization methods, implement regression and classification models, and how to harness the power of neural networks. By purchasing this book, your machine learning journey becomes a lot easier. While a minimal level of Python programming is recommended, the algorithms and techniques are explained in such a way that you don't need to be intimidated by mathematics. The Topics Covered Include:Machine learning fundamentalsHow to set up the development environmentHow to use Python libraries and modules like Scikit-learn, TensorFlow, Matplotlib, and NumPyHow to explore dataHow to solve regression and classification problemsDecision treesk-means clusteringFeed-forward and recurrent neural networks Get your copy now

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  • Price: 9.49 EUR excl. shipping
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  Machine Learning mit Python und Keras, TensorFlow 2 und Scikit-learn

  Datenanalyse mit ausgereiften statistischen Modellen des Machine Learnings Anwendung der wichtigsten Algorithmen und Python-Bibliotheken wie NumPy, SciPy, Scikit-learn, Keras, TensorFlow 2, Pandas und Matplotlib Best Practices zur Optimierung Ihrer Machine-Learning-Algorithmen Mit diesem Buch erhalten Sie eine umfassende Einführung in die Grundlagen und den effektiven Einsatz von Machine-Learning- und Deep-Learning-Algorithmen und wenden diese anhand zahlreicher Beispiele praktisch an. Dafür setzen Sie ein breites Spektrum leistungsfähiger Python-Bibliotheken ein, insbesondere Keras, TensorFlow 2 und Scikit-learn. Auch die für die praktische Anwendung unverzichtbaren mathematischen Konzepte werden verständlich und anhand zahlreicher Diagramme anschaulich erläutert.Die dritte Auflage dieses Buchs wurde für TensorFlow 2 komplett aktualisiert und berücksichtigt die jüngsten Entwicklungen und Technologien, die für Machine Learning, Neuronale Netze und Deep Learning wichtig sind. Dazu zählen insbesondere die neuen Features der Keras-API, das Synthetisieren neuer Daten mit Generative Adversarial Networks (GANs) sowie die Entscheidungsfindung per Reinforcement Learning.Ein sicherer Umgang mit Python wird vorausgesetzt.Aus dem Inhalt: Trainieren von Lernalgorithmen und Implementierung in PythonGängige Klassifikationsalgorithmen wie Support Vector Machines (SVM), Entscheidungsbäume und Random ForestNatural Language Processing zur Klassifizierung von FilmbewertungenClusteranalyse zum Auffinden verborgener Muster und Strukturen in Ihren DatenDeep-Learning-Verfahren für die BilderkennungDatenkomprimierung durch DimensionsreduktionTraining Neuronaler Netze und GANs mit TensorFlow 2Kombination verschiedener Modelle für das Ensemble LearningEinbettung von Machine-Learning-Modellen in WebanwendungenStimmungsanalyse in Social NetworksModellierung sequenzieller Daten durch rekurrente Neuronale NetzeReinforcement Learning und Implementierung von Q-Learning-Algorithmen

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  • Price: 51.40 EUR excl. shipping
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  Einführung in Machine Learning mit Python (eBook, PDF)

  Machine Learning ist zu einem wichtigen Bestandteil vieler kommerzieller Anwendungen und Forschungsprojekte geworden, von der medizinischen Diagnostik bis hin zur Suche nach Freunden in sozialen Netzwerken. Um Machine-Learning-Anwendungen zu entwickeln, braucht es keine großen Expertenteams: Wenn Sie Python-Grundkenntnisse mitbringen, zeigt Ihnen dieses Praxisbuch, wie Sie Ihre eigenen Machine-Learning-Lösungen erstellen. Mit Python und der scikit-learn-Bibliothek erarbeiten Sie sich alle Schritte, die für eine erfolgreiche Machine-Learning-Anwendung notwendig sind. Die Autoren Andreas Müller und Sarah Guido konzentrieren sich bei der Verwendung von Machine-Learning-Algorithmen auf die praktischen Aspekte statt auf die Mathematik dahinter. Wenn Sie zusätzlich mit den Bibliotheken NumPy und matplotlib vertraut sind, hilft Ihnen dies, noch mehr aus diesem Tutorial herauszuholen. Das Buch zeigt Ihnen: - grundlegende Konzepte und Anwendungen von Machine Learning - Vor- und Nachteile weit verbreiteter maschineller Lernalgorithmen - wie sich die von Machine Learning verarbeiteten Daten repräsentieren lassen und auf welche Aspekte der Daten Sie sich konzentrieren sollten - fortgeschrittene Methoden zur Auswertung von Modellen und zum Optimieren von Parametern - das Konzept von Pipelines, mit denen Modelle verkettet und Arbeitsabläufe gekapselt werden - Arbeitsmethoden für Textdaten, insbesondere textspezifische Verarbeitungstechniken - Möglichkeiten zur Verbesserung Ihrer Fähigkeiten in den Bereichen Machine Learning und Data Science Dieses Buch ist eine fantastische, super praktische Informationsquelle für jeden, der mit Machine Learning in Python starten möchte - ich wünschte nur, es hätte schon existiert, als ich mit scikit-learn anfing! Hanna Wallach, Senior Researcher, Microsoft Research

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  • Price: 28.99 EUR excl. shipping
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  Impractical Python Projects (eBook, ePUB)

  Impractical Python Projects is a collection of fun and educational projects designed to entertain programmers while enhancing their Python skills. It picks up where the complete beginner books leave off, expanding on existing concepts and introducing new tools that you'll use every day. And to keep things interesting, each project includes a zany twist featuring historical incidents, pop culture references, and literary allusions. You'll flex your problem-solving skills and employ Python's many useful libraries to do things like: - Help James Bond crack a high-tech safe with a hill-climbing algorithm - Write haiku poems using Markov Chain Analysis - Use genetic algorithms to breed a race of gigantic rats - Crack the world's most successful military cipher using cryptanalysis - Derive the anagram, "I am Lord Voldemort" using linguistical sieves - Plan your parents' secure retirement with Monte Carlo simulation - Save the sorceress Zatanna from a stabby death using palingrams - Model the Milky Way and calculate our odds of detecting alien civilizations - Help the world's smartest woman win the Monty Hall problem argument - Reveal Jupiter's Great Red Spot using optical stacking - Save the head of Mary, Queen of Scots with steganography - Foil corporate security with invisible electronic ink Simulate volcanoes, map Mars, and more, all while gaining valuable experience using free modules like Tkinter, matplotlib, Cprofile, Pylint, Pygame, Pillow, and Python-Docx. Whether you're looking to pick up some new Python skills or just need a pick-me-up, you'll find endless educational, geeky fun with Impractical Python Projects.

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