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.
History
Date: Aug 18, 2022
Pre-Action: Completed the Python course on Kaggle as a means towards the end of becoming a Machine Learning engineer.
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.)
Key:
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
Takeaway
Turns out basic python is the only requirement to dive into ML using this course :).
Explain-Like-I’m-Blank:
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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