Python | What you can do with Python
Introduction
- Machine learning
- Data analysis / visualization
- WEB application development
There are three main things you can do with Python. I will explain each one.
Machine learning
- What is machine learning?
- Python in machine learning
- How to learn machine learning
What is machine learning?
The simple explanation of machine learning is to find out from the data which is the best way to complete a task.
It's often confused with AI, but some are. Machine learning is within the big framework of AI. It's like the field of carbonic acid exists within the big framework of juice.
Specifically, using machine learning, you can translate between languages and make recommendations (recommended functions) on EC sites.
Python in machine learning
Python has a wealth of library modules for machine learning. Typically
- NumPy
- scikit-learn
- TensorFlow
And so on. The features of each are as follows.
| Feature | |
|---|---|
| NumPy | Mathematical calculations can be done at high speed |
| scikit-learn | Rich in algorithms and datasets |
| TensorFlow | Google can develop and build neural networks |
3 ways to learn machine learning
- Deep Learning from scratch
- DL4US Matsuo Laboratory, University of Tokyo
- Coursera Machine Learning Stanford University
[__ Make from scratch Deep Learning __](https://www.amazon.co.jp/%E3%82%BC%E3%83%AD%E3%81%8B%E3%82%89%E4%BD % 9C% E3% 82% 8BDeep-Learning-% E2% 80% 95Python% E3% 81% A7% E5% AD% A6% E3% 81% B6% E3% 83% 87% E3% 82% A3% E3% 83% BC% E3% 83% 97% E3% 83% A9% E3% 83% BC% E3% 83% 8B% E3% 83% B3% E3% 82% B0% E3% 81% AE% E7% 90% 86% E8% AB% 96% E3% 81% A8% E5% AE% 9F% E8% A3% 85-% E6% 96% 8E% E8% 97% A4-% E5% BA% B7% E6% AF% 85 / dp / 4873117585 / ref = sr_1_1? __ mk_ja_JP =% E3% 82% AB% E3% 82% BF% E3% 82% AB% E3% 83% 8A & dchild = 1 & keywords =% E3% 82% BC% E3% 83% AD% E3% 81% 8B% E3% 82% 89% E4% BD% 9C% E3% 82% 8BDeep + Learning & qid = 1600215913 & s = books & sr = 1-1)
If you want to learn machine learning, especially deep learning, with books, this is the one for you. It's best to learn Python to some extent before working on it. However, since the book itself is thick, it seems that the frustration rate is high for those who are not good at studying with books.
DL4US Matsuo Laboratory, University of Tokyo
This is the actual content used in the class, created mainly by Professor Yutaka Matsuo of the University of Tokyo, who is the chairman of the Japan Deep Learning Association and an outside director of the SoftBank Group. All can be downloaded free of charge.
This content is the content of the Deep Learning course "DL4US" for engineers. It is designed so that you can learn important knowledge from the basics to build a new model of deep learning and to carry out advanced research and development without requiring advanced mathematical knowledge. --https://weblab.t.u-tokyo.ac.jp/dl4us/ | Matsuo Laboratory DL4US content quoted from the public page
__Cousera Machine Learning Stanford University __
A machine learning course at Stanford University in Coursera, an online course by leading instructors from prestigious universities, Google, IBM and other leading companies. The language is English, but there are Japanese subtitles. You can comprehensively learn machine learning.
This course covers a wide range of machine learning, data mining, and statistical pattern recognition. The topics are: (i) Supervised learning (parametric / nonparametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Machine learning best practices (bias / dispersion theory, machine learning and AI innovation processes). It also draws learning algorithms from numerous case studies and applications into areas such as smart robot construction (perception, control), text comprehension (web search, anti-spam), computer vision, medical informatics, audio, and database mining. You will also learn how to apply it. --quoted from https://www.coursera.org/learn/machine-learning Coursera Machine Learning Stanford (translated at https://www.deepl.com/ja/translator DeepL)
Data analysis / visualization
- What is data analysis / visualization?
- Python for data analysis and visualization
- How to learn data analysis and visualization
What is data analysis / visualization?
Data analysis is to collect numbers and letters, classify, delete, and add to each element to add value to the data. By analyzing data, you can improve trends and problems found from them and use them for business development.
At the same time, by visualizing the data and making it into a graph or table, the essence and value of the data can be further seen.
Python in data analysis and visualization
Python also has a library for working with data. Typically
- Pandas
- SciPy
- Matplotlib Such
| Feature | |
|---|---|
| Pandas | Graphing data, analyzing data, reading data |
| Scipy | Science and Technology Calculation Library (Differential Integral, Statistics, etc.) |
| Matplotlib | Graph drawing library |
WEB application development
- What is a WEB application?
- Python in web applications
What is a WEB application?
A WEB application is an application that runs by linking a server and a client. Simply put, it's an application like Youtube or Twitter where each individual has an account.
Python in web applications
Python frameworks include Django and Flask.
| Feature | |
|---|---|
| Django | Abundant functions, high quality, quick application development |
| Flask | Simple and easy to handle, for small scale |
| Bottle | Equipped with the minimum necessary functions |
Summary
Besides this, Python has clear and easy-to-understand game development and code using Pygame. It is also used in the field of education. Make the most of what you can do with Python and have fun programming!
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