ML Diaries: Day 1

Complete Machine Learning & Data Science Bootcamp 2022

— a daily log of my learning and projects built as I take up Machine Learning. Welcome to The Mind Palace by Dayo :)

ML Diaries is simply a daily log of my learning and projects built as I take up Machine Learning. Stories on The Mind Palace, this blog, will still continue every week.


Date: Aug 18, 2022

Pre-Action: Completed the Python course on Kaggle as a means towards the end of becoming a Machine Learning engineer.

Yay, I got my certificate! Now onto actual stuff.

Goal: Search for a course or resource to fine-tune Python in the context of machine learning.

The Good Stuff

Action: Found something even better!

Bought the ‘Complete Machine Learning & Data Science Bootcamp 2022’ course on Udemy by Andrei Neagoie and Daniel Bourke (because ‘hey, I know Daniel Bourke from Youtube’).

Dived in and completed sections 1–2 (in probably 2 hours or a little above that).

‘Played around’ with Teachable Machine and ML Playground (where I know nothing about the algorithms there but notice that how they classify data differently is cool and interesting, hmm.)


y-axis = car prices (from low prices up to the highest prices)

x-axis = car brands (from the lowest brands through the mid ranges to the highest brands)

orange = unlikely to purchase; purple = likely to purchase

Using the K Nearest Neighbours Algorithm (no idea what they mean bro)
Using the Perceptron Algorithm
Using the Decision Tree Model


Turns out basic python is the only requirement to dive into ML using this course :).


Machine Learning is simply teaching a computer how to make sense of data (input) to give correct predictions (output). The made-out sense = a model (also called an algorithm), which can then be applied to other data to give correct predictions.

The sense-making process is called ‘training’the data and the data used to get the right formula/model (i.e. train the model) is called the training data.

‘Being a pro is on the other end of being a tabula rasa’.

Good luck to me.

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