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Auteur Prateek GUPTA |
Documents disponibles écrits par cet auteur (2)
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Practical Data Science with Jupyter : Explore Data Cleaning, Pre-processing, Data Wrangling, Feature Engineering and Machine Learning using Python and Jupyter (English Edition) / Prateek GUPTA / BPB Publications (2021)
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Titre : Practical Data Science with Jupyter : Explore Data Cleaning, Pre-processing, Data Wrangling, Feature Engineering and Machine Learning using Python and Jupyter (English Edition) Type de document : e-book Auteurs : Prateek GUPTA Editeur : BPB Publications Année de publication : 2021 ISBN/ISSN/EAN : 9789389898064 Note générale : copyrighted Langues : Anglais (eng) Résumé : Solve business problems with data-driven techniques and easy-to-follow Python examplesKey FeaturesEssential 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.DescriptionThis 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 will 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.Who this book is forThis 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.Table of Contents1. Data Science Fundamentals2. Installing Software and System Setup3. Lists and Dictionaries4. Package, Function, and Loop5. 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 Methods17. Case Study-118. Case Study-219. Case Study-320. Case Study-421. Python Virtual Environment22. Introduction to An Advanced Algorithm - CatBoost23. Revision of All Chapters’ LearningAbout the Author Prateek Gupta is a Data Enthusiast and loves data-driven technologies. Prateek has completed his B.Tech in Computer Science & Engineering and he is currently working as a Data Scientist in an IT company. Prateek has a total 9 years of experience in the software industry, and currently, he is working in the computer vision area. Prateek has implemented various end-to-end Data Science projects for fishing, winery, and ecommerce clients. His implemented object detection and recognition models and product recommendation engines have solved many business problems of various clients. His keen area of interest is in natural language processing and computer vision. In his leisure time, he writes posts about artificial intelligence in his blog.Blog links: http://dsbyprateekg.blogspot.com/LinkedIn Profile: https://www.linkedin.com/in/prateek-gupta-64203354/ Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88939034 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=564484 Data Science with Jupyter : Master Data Science skills with easy-to-follow Python examples / Prateek GUPTA / BPB Publications (2019)
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Titre : Data Science with Jupyter : Master Data Science skills with easy-to-follow Python examples Type de document : e-book Auteurs : Prateek GUPTA Editeur : BPB Publications Année de publication : 2019 ISBN/ISSN/EAN : 9789388511377 Note générale : copyrighted Langues : Anglais (eng) Résumé : Step-by-step guide to practising data science techniques with Jupyter notebooks Key FeaturesAcquire Python skills to do independent data science projectsLearn the basics of linear algebra and statistical science in Python wayUnderstand how and when they're used in data scienceBuild predictive models, tune their parameters and analyze performance in few stepsCluster, transform, visualize, and extract insights from unlabelled datasetsLearn how to use matplotlib and seaborn for data visualizationImplement and save machine learning models for real-world business scenarios DescriptionModern 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.AudienceThe 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 Contents Data Science Fundamentals Installing Software and Setting up Lists and Dictionaries Function and Packages NumPy Foundation Pandas and Dataframe Interacting with Databases Thinking Statistically in Data Science How to import data in Python? Cleaning of imported data Data Visualization Data Pre-processing Supervised Machine Learning Unsupervised Machine Learning Handling Time-Series Data Time-Series Methods Case Study – 1 Case Study – 2 Case Study – 3 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. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88938825 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=564431
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