Bringing machine learning to a practical level in one month # 1 (Starting edition)

Introduction

I received a one-month free research period from Company, so I started studying machine learning, which I had been interested in for some time. Since it's a big deal, I'll keep a record of my learning. Today, the first day, it was a disorganized reference to the literature and learning methods for learning machine learning.

List of past articles

Goal setting

We have set the goals for one month as follows.

  1. You will be able to propose solutions to new problems using a machine learning approach.
  2. You will be able to make proposals to replace the work that people have done manually with computer work.
  3. Put into practice what you have proposed, or create a project that can be put into practice.
  4. Solve simple problems that are actually present and use them as the result of independent research.

Advance preparation

First of all, in order to spend this short month meaningfully, I prepared some books instead of the run-up before the free study period.

The book I bought (not read)

The book I bought and read

Finally free research start

And today, which is the first day, I went through various sites and slides at random to decide what kind of course to study.

Seen slide

The site I saw

Stanford University Machine Learning Online Course

Machine Learning - Stanford University | Coursera

Finished WEEK 1

From tomorrow, we will be holding this Stanford machine learning course. It seems that there is up to WEEK 11, and even if you do one in the morning and one in the afternoon, it will end in a week, so I would like to finish this course before Christmas and get a feel for it.

Then, when I try to move my hand, I don't want to be context-switched because I'm stumbling or taking time in a non-essential place, so I'll review the machine learning environment in Python and create the strongest environment tomorrow. I thought, but I have to create an Octave environment as well.

What to do tomorrow

appendix

Introduction to Machine Learning Theory Reading Memo

Overview

Introduction to Machine Learning Theory for IT Engineers Technical Review Company Etsuji Nakai

Classification of machine learning algorithms

Review term

Review analysis tool

Further reference books

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