- We will send in 10–14 business days.
/images/books/13788826/9786205657232.jpg)
- Author: Jorge Gómez
- Publisher: Our Knowledge Publishing
- ISBN-10: 6205657236
- ISBN-13: 9786205657232
- Format: 15.2 x 22.9 x 0.5 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Teaching Machine Learning Programming in Pandas and Jupyter-Lab (e-book) (used book) | bookbook.eu
Reviews
Description
This book is designed for those who wish to enter the world of machine learning, i.e. beginner mode. Initially, data manipulation through dataframes in the Python programming language, embedded in the Jupyter-lab and Pandas framework, will be addressed. Then data will be extracted from csv files to manage them in dataframes. Indexing, selection and allocation, indexing in Pandas, tag-based selection, conditional selection, data allocation, summary functions, maps and grouping and sorting. Finally, we will proceed to program basic supervised learning models such as linear regression with a single variable, linear regression with multiple variables, saving and loading the training model, data management with dummy variables, separation of the training and test data sets.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 16d.01:16:00
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Jorge Gómez
- Publisher: Our Knowledge Publishing
- ISBN-10: 6205657236
- ISBN-13: 9786205657232
- Format: 15.2 x 22.9 x 0.5 cm, softcover
- Language: English English
This book is designed for those who wish to enter the world of machine learning, i.e. beginner mode. Initially, data manipulation through dataframes in the Python programming language, embedded in the Jupyter-lab and Pandas framework, will be addressed. Then data will be extracted from csv files to manage them in dataframes. Indexing, selection and allocation, indexing in Pandas, tag-based selection, conditional selection, data allocation, summary functions, maps and grouping and sorting. Finally, we will proceed to program basic supervised learning models such as linear regression with a single variable, linear regression with multiple variables, saving and loading the training model, data management with dummy variables, separation of the training and test data sets.
Reviews