MachineLearning: Get started with ML ver. 1

Last Updated on March 23, 2022 by shibatau

I. What shall we learn?

I began to learn Machine Learning again and I’m rewriting my old posts related to ML. Also I’m learning the basics of ML from new tutorials. Let me introduce some ones easy to understand for beginners of ML like me. In this post we will learn the basics from the following post:

Machine Learning Tutorial For Complete Beginners | Learn Machine Learning with Python

II. Types of Machine Learning

Machine learning algorithms mainly fall into the three categories.

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

III. K-Nearest Neighbour Algorithm

KNN is a supervised learning algorithm and belongs to a group of lazy learners. It stores the training data, arranges the data during the training phase, and find the closest neighbors during the inference phase.

This is not related to ML but I tried creating a scatter plot similar to the plot showed in the tutorial linked above but I couldn’t because there are some wrong data in the table. There must be typos or such kinds of simple mistakes there. So, I have created new sample data for a scatter plot similar to the one in the tutorial.

You cann see the scripts here:

https://colab.research.google.com/drive/1f-SMi2z0gaTA_uTrV6guYQFNUzmLn_oH?usp=sharing

To be continued.

About shibatau

I was born and grown up in Kyoto. I studied western philosophy at the University and specialized in analytic philosophy, especially Ludwig Wittgenstein at the postgraduate school. I'm interested in new technology, especially machine learning and have been learning R language for two years and began to learn Python last summer. Listening toParamore, Sia, Amazarashi and MIyuki Nakajima. Favorite movies I've recently seen: "FREEHELD". Favorite actors and actresses: Anthony Hopkins, Denzel Washington, Ellen Page, Meryl Streep, Mia Wasikowska and Robert DeNiro. Favorite books: Fyodor Mikhailovich Dostoyevsky, "The Karamazov Brothers", Shinran, "Lamentations of Divergences". Favorite phrase: Salvation by Faith. Twitter: @shibatau

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