Learning record so far

at first

I am studying acclaim because I want to become an AI engineer from inexperienced

Simple profile

--Science college graduate Arasa --Plant production equipment design / development / maintenance

What you can do: You can do both hard and soft Electric circuit, control circuit, chemical engineering, PLC, touch panel, construction supervision

Programming is de amateur

Learning record

--June 2019 Start studying Python basics --June-August 2019: Learn through online courses and youtube, participate in learning competitions such as Kaggle's Titanic --September-October 2019 Study with O'Reilly books, etc. --October-December 2019 Coursera How to Win a Data Science Competition --January 2020 Learning with "Kaggle-Winning Data Analysis Technology" and "Deep Learning from Zero" --February-March 2020: Participating in Kaggle's basketball competition was canceled at Corona and depressed --April-May 2020 Participating in Kaggle's cell competition ← Now here

It's been almost a year since I wrote it again. Tears that do not feel growth

Study time

June-December 2019: Approximately 500 hours Average: 2 hours / day What kind of life do working people who exceed 100 hours a month live?

Teaching materials

udemy

・ [Kikagaku style] Artificial intelligence / machine learning De-black box course --Beginner level-- ・ [Kikagaku style] Artificial intelligence / machine learning De-black box course --Intermediate-- ・ [[Data analysis starting from zero] Introduction to Python data science learned from business cases](https://www.udemy.com/share/101XSEBkcaclpVQ34=0 /) ← Recommended ・ Introduction to Python 3 taught by active Silicon Valley engineers + application + American Silicon Valley style code style youtube -Able Programming ← Recommended

progate Python course

Reference book ・ Introduction to data analysis by python ・ Data Science Cookbook 2nd Edition by Ipython ・ Data analysis technology that wins with Kaggle ← Recommended ・ Deep learning made from scratch ・ Theory and practice by expert data scientists

coursera How to Win a Data Science Competition: Learn from Top Kagglers The teachers are Russian and all in English. The lecture is quite good because there is a google translate teacher even in English, Programming challenges are difficult. The final task is to participate in the Kaggle competition and get a certain score or higher, Because it is necessary to describe the kernel as well as EDA, data leak countermeasures, ensembles, etc. and score between students. It's pretty hard. This is a good course where you can learn all about Kaggle. (Maybe it was easier to understand after doing Kaggle for a couple of months)

kaggle

Reflection

・ I should have done output on blogs etc. ・ Three months behind the target ・ Morning is weak. Do your best in the morning ・ No results have been achieved yet. Kaggle solo medal

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