Formation Initiale Python Avec Jupyter Et Pycharm

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Formation Initiale Python Avec Jupyter Et Pycharm

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Author : Patrice Rey
language : fr
Publisher: BoD - Books on Demand
Release Date : 2022-02-17

Formation Initiale Python Avec Jupyter Et Pycharm written by Patrice Rey and has been published by BoD - Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-17 with Computers categories.

Python est un langage de programmation (langage de script) permettant de faire de la programmation impérative (écrire une séquence d'instructions), de la programmation fonctionnelle (résoudre des problèmes en fabriquant des fonctions) et de la programmation orientée objet (définir des objets que l'on fait interagir entre eux). Dans la première partie, en utilisant les classeurs Jupyter depuis une distribution Anaconda, nous expliquons clairement et rigoureusement les notions fondamentales relatives à ce langage. Dans une seconde partie, en utilisant l'environnement de développement intégré PyCharm 2021.3 avec Python 3.10, nous apprenons les bases nécessaires pour développer une application fenêtrée PyQt5 pour la plateforme Windows 10, dotée d'une interface graphique, avec des composants hébergés qui répondent à des fonctionnalités programmées. Et nous terminons en développant une application spécifique optimisée pour pouvoir être distribuée auprès d'utilisateurs sous forme d'un exécutable unique grâce aux packages PyInstaller et Auto-Py-to-Exe.

Itc Informatique Tronc Commun Mpsi Formation Python

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Author : patrice rey
language : fr
Publisher: BoD - Books on Demand
Release Date : 2022-08-10

Itc Informatique Tronc Commun Mpsi Formation Python written by patrice rey and has been published by BoD - Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-10 with Computers categories.

Ce livre est conçu comme un manuel d'aide pratique d'informatique à destination des élèves de première et deuxième années des classes préparatoires dans les filières MP, PC, PSI et PT. Il est destiné aux étudiants souhaitant avoir une formation initiale et complète à Python. Python est un langage de programmation (langage de script) permettant de faire de la programmation impérative (écrire une séquence d'instructions), de la programmation fonctionnelle (résoudre des problèmes en fabriquant des fonctions) et de la programmation orientée objet (définir des objets que l'on fait interagir entre eux). Ce langage est très utilisé dans le monde scientifique, les universités, les classes préparatoires et l'enseignement en général car il possède de nombreux avantages. Il est aussi utilisé dans le monde professionnel du développement web avec le framework Django. Les milliers de bibliothèques accessibles gratuitement font de ce langage un outil puissant (Pygame pour la création des jeux en 2D, Blender pour la modélisation en 3D, PIL pour le traitement d'images, Scipy pour les sciences, Matplotlib pour les graphiques, Numpy pour le calcul, etc).

Practical Data Science With Jupyter

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Author : Prateek Gupta
language : en
Publisher: BPB Publications
Release Date : 2021-03-01

Practical Data Science With Jupyter written by Prateek Gupta and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-01 with Computers categories.

Solve business problems with data-driven techniques and easy-to-follow Python examples Ê KEY FEATURESÊÊ _ Essential coverage on statistics and data science techniques. _ Exposure to Jupyter, PyCharm, and use of GitHub. _ Real use-cases, best practices, and smart techniques on the use of data science for data applications. DESCRIPTIONÊÊ This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you willÊ clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms. WHAT YOU WILL LEARN _ Rapid understanding of Python concepts for data science applications. _ Understand and practice how to run data analysis with data science techniques and algorithms. _ Learn feature engineering, dealing with different datasets, and most trending machine learning algorithms. _ Become self-sufficient to perform data science tasks with the best tools and techniques. Ê WHO THIS BOOK IS FORÊÊ This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples. Ê TABLE OF CONTENTS 1. Data Science Fundamentals 2. Installing Software and System Setup 3. Lists and Dictionaries 4. Package, Function, and Loop 5. NumPy Foundation 6. Pandas and DataFrame 7. Interacting with Databases 8. Thinking Statistically in Data Science 9. How to Import Data in Python? 10. Cleaning of Imported Data 11. Data Visualization 12. Data Pre-processing 13. Supervised Machine Learning 14. Unsupervised Machine Learning 15. Handling Time-Series Data 16. Time-Series Methods 17. Case Study-1 18. Case Study-2 19. Case Study-3 20. Case Study-4 21. Python Virtual Environment 22. Introduction to An Advanced Algorithm - CatBoost 23. Revision of All ChaptersÕ Learning

Applied Data Science With Python And Jupyter

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Author : Alex Galea
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31

Applied Data Science With Python And Jupyter written by Alex Galea and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.

Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM, KNN classifiers, and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.

Data Science With Jupyter

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Author : Gupta Prateek
language : en
Publisher: BPB Publications
Release Date : 2019-09-20

Data Science With Jupyter written by Gupta Prateek and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-20 with Computers categories.

Step-by-step guide to practising data science techniques with Jupyter notebooksKey features Acquire Python skills to do independent data science projects Learn the basics of linear algebra and statistical science in Python way Understand how and when they're used in data science Build predictive models, tune their parameters and analyze performance in few steps Cluster, transform, visualize, and extract insights from unlabelled datasets Learn how to use matplotlib and seaborn for data visualization Implement and save machine learning models for real-world business scenarios Description Modern businesses are awash with data, making data driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with just enough knowledge of Python in conjunction with skills to use powerful tool such as Jupyter Notebook in order to succeed in the role of a data scientist. The book starts with a brief introduction to the world of data science and the opportunities you may come across along with an overview of the key topics covered in the book. You will learn how to setup Anaconda installation which comes with Jupyter and preinstalled Python packages. Before diving in to several supervised, unsupervised and other machine learning techniques, you'll learn how to use basic data structures, functions, libraries and packages required to import, clean, visualize and process data. Several machine learning techniques such as regression, classification, clustering, time-series etc have been explained with the use of practical examples and by comparing the performance of various models. By the end of the book, you will come across few case studies to put your knowledge to practice and solve real-life business problems such as building a movie recommendation engine, classifying spam messages, predicting the ability of a borrower to repay loan on time and time series forecasting of housing prices. Remember to practice additional examples provided in the code bundle of the book to master these techniques.Who this book is forThe book is intended for anyone looking for a career in data science, all aspiring data scientists who want to learn the most powerful programming language in Machine Learning or working professionals who want to switch their career in Data Science. While no prior knowledge of Data Science or related technologies is assumed, it will be helpful to have some programming experience.Table of contents1. Data Science Fundamentals2. Installing Software and Setting up3. Lists and Dictionaries4. Function and Packages5. NumPy Foundation6. Pandas and Dataframe7. Interacting with Databases8. Thinking Statistically in Data Science9. How to import data in Python?10. Cleaning of imported data11. Data Visualization12. Data Pre-processing13. Supervised Machine Learning14. Unsupervised Machine Learning15. Handling Time-Series Data16. Time-Series Methods 17. Case Study - 118. Case Study - 219. Case Study - 320. Case Study - 4About the authorPrateek is a Data Enthusiast and loves the data driven technologies. Prateek has total 7 years of experience and currently he is working as a Data Scientist in an MNC. He has worked with finance and retail clients and has developed Machine Learning and Deep Learning solutions for their business. His keen area of interest is in natural language processing and in computer vision. In leisure he writes posts about Data Science with Python in his blog.

Data Science Avec Python

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Author : Patrice Rey
language : fr
Publisher: BoD - Books on Demand
Release Date : 2022-03-12

Data Science Avec Python written by Patrice Rey and has been published by BoD - Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-12 with Computers categories.

Ce livre est destiné aux personnes souhaitant avoir une première immersion dans le domaine de l'analyse de données avec le langage de programmation Python et les librairies dédiées à l'analyse de données que sont NumPy, Pandas, Matplotlib et Seaborn. Dans ce livre, nous verrons principalement comment explorer, manipuler et visualiser des données structurées, c'est-à-dire des tableaux contenant des lignes et des colonnes. La première partie aborde l'utilisation de la librairie NumPy qui est une librairie Python dédiée au calcul scientifique fournissant des fonctions très performantes de calcul, mais aussi des structures de données spécialisées et remarquablement performantes. La seconde partie aborde l'utilisation de la librairie Pandas qui est une librairie Python dédiée à la Data Science. Il s'agit de la librairie Python la plus populaire et la plus performante pour faire de l'analyse de données. Cette librairie Pandas amène avec elle deux nouvelles structures essentielles pour l'analyse de données qui sont les structures Series et DataFrame. La troisième partie aborde l'utilisation de la librairie Matplotlib qui est une librairie graphique très connue en Python car elle est puissante, très simple à prendre en main et chaque élément de la figure peut être configuré finement via un grand nombre de paramètres disponibles. La quatrième partie aborde l'utilisation de la librairie Seaborn qui est la librairie la plus populaire pour la visualisation de graphique. En réalité, cette librairie s'appuie sur Matplotlib et elle apporte des fonctions supplémentaires permettant de générer des graphiques plus travaillés, aux allures plus modernes, et ce de manière très simple. La cinquième partie aborde la création d'une application autonome pour visualiser des graphiques élaborés avec les librairies Matplotlib et Seaborn. Nous y verrons notamment comment utiliser le composant FigureCanvasQTAgg pour visualiser des graphiques aux allures modernes dans un projet PyCharm avec Python et la librairie des contrôles PyQt5.

Beginning Data Science With Python And Jupyter

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Author : Alex Galea
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-06-05

Beginning Data Science With Python And Jupyter written by Alex Galea and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-05 with Computers categories.

Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. What you will learn Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests Plan a machine learning classification strategy and train classification, models Use validation curves and dimensionality reduction to tune and enhance your models Discover how you can use web scraping to gather and parse your own bespoke datasets Scrape tabular data from web pages and transform them into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.

Python For Data Analysis

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Author : Guido Van Smit
language : en
Publisher:
Release Date : 2019-11-27

Python For Data Analysis written by Guido Van Smit and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-27 with categories.

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in PythonPython for Data Analysis represents now one of the most interesting and useful applications among all the possible applications of Machine Learning and Artificial Intelligence. This guidebook is the ultimate guide to learning insights and strategies to help you grow your business, save time, resources, and energy or if you are looking for a new job, but it requires a solid background in terms of processes and technologies involved. It will walk you through the entire program from A to Z and offers a straightforward approach to Python with plenty of opportunities for hands-on learning and improving your skills. Inside, you'll find: What is and how Data Analysis works Essentials Python Libraries: NumPy, Pandas, IPython and Jupyter Data Types in Python Text Analysis in Python 3 Analyze and manipulate regular and irregular time series data Practical applications to put into use today And so much more! Installation and Setup If you're ready to fully grasp Python for Data Analysis, this book is the perfect guide to help you!

Machine Learning Concepts With Python And The Jupyter Notebook Environment

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Author : Nikita Silaparasetty
language : en
Publisher: Apress
Release Date : 2020-10-06

Machine Learning Concepts With Python And The Jupyter Notebook Environment written by Nikita Silaparasetty and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-06 with Computers categories.

Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebook instead of a text editor or a regular IDE. You’ll start by learning how to use Jupyter Notebooks to improve the way you program with Python. After getting a good grounding in working with Python in Jupyter Notebooks, you’ll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Notebooks allow you to apply concepts from earlier in the book. Those who are new to machine learning can dive in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier. What You Will Learn Program in Python and TensorFlow Tackle basic machine learning obstacles Develop in the Jupyter Notebooks environment Who This Book Is For Ideal for Machine Learning and Deep Learning enthusiasts who are interested in programming with Python using Tensorflow 2.0 in the Jupyter Notebook Application. Some basic knowledge of Machine Learning concepts and Python Programming (using Python version 3) is helpful.

Python

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Author : Brady Ellison
language : en
Publisher:
Release Date :

Python written by Brady Ellison and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

THIS BOOK INCLUDES : Python for Beginners: A crash course to learn Python Programming in 1 Week Python for Data Analysis: A Beginners Guide to Master the Fundamentals of Data Science and Data Analysis by Using Pandas, Numpy and Ipython Python Machine Learning: A Step by Step Beginner’s Guide to Learn Machine Learning Using Python Here's what you'll learn through this book: Python for Beginners In this book You will learn: Getting started with the basics Statements, Comments, Variables, Index Data Types: Strings and Numbers Data Types: List and Tuple Data Types: Set and Dictionary Operators Functions Loops Python Practice Projects and much more Python for Data Analysis In this book You will learn: Data Science/Analysis and its applications IPython and Jupyter - an introduction to the basic tools and how to navigate and use them. You will also learn about its importance in a data scientist’s ecosystem. Pandas - a powerful data management Python library that lets you do interesting things with data. You will learn all the basics you need to get started. NumPy - a powerful numerical library for Python. You will learn more about its advantages. Python Machine Learning The Topics Covered Include: Machine learning fundamentals How to set up the development environment How to use Python libraries and modules like Scikit-learn, TensorFlow, Matplotlib, and NumPy How to explore data How to solve regression and classification problems Decision trees k-means clustering Feed-forward and recurrent neural networks Get your copy now!