A beginner's summary of Python machine learning is super concise.

Here's a very brief summary of Python machine learning. Machine learning / Artificial intelligence (AI) is summarized as what you can do with Python.

Machine learning is a technology that learns based on data prepared in advance and predicts newly presented data based on it. .. </ span> </ strong>

Therefore, preprocessing of data and selection of algorithms (procedures for solving problems) are important.

There are two types of methods.

① Supervised learning → Correct answer information (label) is also available as a set with learning data. Create patterns from existing information to predict unknown data.

(2) Unsupervised learning → A method that does not have correct answer data. Analyze the characteristics of the data and classify the data into several groups.

Examples of libraries that do this are scikit-learn (a library recommended for beginners) and TensorFlow (developed by Google).

[Example of machine learning] Identifying a dog's photo → Judging whether the given data is a dog's photo and finding the correct answer rate. (A learning-recorded model is used as the criterion.) It has evolved and is used for identifying unsolicited emails and authenticating human faces.

In short, in machine learning, model selection and preprocessing (making data easy to learn) are important, and the results will change accordingly.

It is said that knowledge of calculus, linear algebra, matrices, statistics, etc. is required to obtain an essential understanding of such preprocessing. (Calculation using NumPy, matplotlib, etc. → Realization of visualization, etc.)

  • Supplement *

To explain deep learning roughly, it seems to be a technology that further develops from machine learning and learns by setting points to identify by oneself. (Interesting)

Conclusion I was interested in data analysis and started learning Python, but I finally realized that data analysis and machine learning are inseparable. .. ..

In addition, knowledge of mathematics is a must to understand the essence. (I should have studied mathematics properly in high school and college ...)

Alright, I'll do my best! !!

So, I hope it will be helpful for beginners who are interested in data analysis and machine learning and want to know just the outline.

  • I am also a beginner, so there may be mistakes. We plan to make corrections each time, so if you have any mistakes, please let us know.

The end

Addition Currently, I am thinking of studying with reference to this. This is a very helpful article, so I will provide a link.

[Preserved version / for beginners] Recommended study method for those who want to become an AI engineer by themselves https://qiita.com/tani_AI_Academy/items/4da02cb056646ba43b9d

P.S.

I've been studying alone for four months, so I'm feeling the limits of maintaining motivation. I am posting my learning records in the hope that I will be able to break through the current situation and make friends with people in the same situation. I want to do interesting things such as planning a study session and creating a joint portfolio together! !! If you have any questions, please feel free to contact us on Twitter. (Serious wish)

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