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Python Data Analysis for Newbies: Numpy/pandas/matplotlib/scikit-learn/keras
Чтиво | Автор: LeeAndro | Добавлено: 13-09-2020, 23:56 | Просмотров (4) | Комментариев (0) | Жалоба |
Python Data Analysis for Newbies: Numpy/pandas/matplotlib/scikit-learn/keras
English | 2020 | ASIN : B08HRJ4ZXX | 113 Pages | PDF, EPUB, AZW3 | 8.14 MB

Thank you for picking up this book.


This book is a bner's introduction to data analysis using Python programming. This book is written for the following readers.

1) Interested in machine learning and deep learning

2) Interested in programming with Python.

3) Interested in data analysis.

4) Interested in using Numpy/Pandas/Matplotlib/ScikitLearn.

5) Not interested in building machine learning environments.

6) Not interested in spending a lot of money for learning.

7) Vaguely worried about the new corona epid and the future.

Many of my friends and acquaintances have started data analysis with a vengeance, only to be satisfied with the day-long process of setting up an environment, and then, after doing MNIST (handwritten numeric image data sets) and iris classification tutorials, they get busy with their day jobs and abandon it for a while.

This book uses the free Python execution environment provided by Google to run the tested source code in the book, allowing you to learn by doing programming with zero to set up your own environment.

This book focuses on the bare minimum of knowledge needed to get a bner into serious data analysis in Python. Our goal is that by the end of the book, readers will have reached the following five goals.

1) To build and train deep learning models and machine learning models from arbitrary data to be trained and predicted using deep learning libraries (keras) and machine learning libraries (scikit-learn).

2) To use Pandas instead of Excel for large scale data processing.

3) To manipulate multidimensional arrays using Numpy.

4) To draw graphs freely using Matplotlib.

5) To perform simple data analysis on the spread of new coronaviruses.

■■■■■Contents■■■■■

1. Introduction

2. Disclaimer

3. Trademarks and registered trademarks

4. Feedback

5. Jupyter Notebook

6. GPU environment ーGoogle Colaboratory

7. Minimal MNIST deeper learning

8. Python

■Hello Pythonic World!

9. Pandas

■The significance of Pandas

■DataFrame Type

■Visualization with just Pandas

■Table joins with a specific key

■CSV file input/output (Colab compatible version)

■Getting any cell or cell range

■Retrieve only the values of the cells that meet specific conditions.

■Impact of the new corona on stock prices as seen in Pandas

10. Numpy

■The significance of Numpy

■Numpy Array Generation Function

■Tips for Jupyter Notebook

■Numpy.ndarray's Indexing/Slicing

■Image processing CIFAR10 in Numpy

■Broadcast of Numpy.ndarray

■Calculating the inner product

11. Matplotlib

■Legend, Title, X-axis and Y-axis labels

■Bar Charts

■Series Color Specification

■Histogram

■Scatter Plot

■Stack Plot

■Pie Charts 

■3D Graph

12. Scikit-Learn

■SVM:Support Vector Machine

■RandomForest

■XGBOOST(Classification)

■XGBOOST(Regression)

13. Keras

■LeNet

■VGG

■Data link for the new Corona (covid-19)

■Kaggle(English)

■SIGNATE(English/Japanese)

■■■■■■■■■■



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