Data Science with Jupyter (notice n° 76153)
[ vue normale ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 04290cam a2200277zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88938825 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250108000715.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250108s2019 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789388511377 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88938825 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Gupta, Prateek |
| 245 01 - TITLE STATEMENT | |
| Title | Data Science with Jupyter |
| Remainder of title | Master Data Science skills with easy-to-follow Python examples |
| Statement of responsibility, etc. | ['Gupta, Prateek'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | BPB Publications |
| Date of production, publication, distribution, manufacture, or copyright notice | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Gupta, Prateek |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88938825">https://international.scholarvox.com/netsen/book/88938825</a> |
| Electronic format type | text/html |
| Host name | |
Pas d'exemplaire disponible.
Réseaux sociaux