Do you often hear about artificial intelligence in the news and want to learn more about it?
Do you find a machine’s ability to learn interesting, and would you like to understand how an AI works deep down?
Are you already in the field of machine learning but you struggle to learn how algorithm works? If so, you’re in the right place, Inside Algorithms!
Table of Contents
What is machine learning?
Machine learning is a subfield of artificial intelligence that studies the development of algorithms that can learn patterns from given input data and make predictions based on them.
Machine learning it’s a broad field that also includes processing input data, evaluating model accuracy and managing AI influence on society, ensuring these products aren’t affected by gender, ethnic, and cultural biases.
Why study machine learning?
In today’s world, machine-learning solutions are becoming increasingly important. They are applied in various sectors like natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.
The main purpose of this blog is to provide information to people who want to study the subject.
But my articles could be a starting point for anyone who wants to get a general background to be aware of the technological development of our very fast-paced modern world.
My experience in the field
I’ve been attracted to computers and the internet since I was a child, but I started my journey two years ago when I approached coding by making websites in HTML and CSS (markup languages that underlie the web).
After a year of web development, I started learning Python, a programming language useful in camps such as automation and data analysis.
After I understood Python basics, I dived into the machine learning field. I coded a house prices calculator, a brain cancer recognizer model, and much more.
Inside Algorithms topics
Inside Algorithms blog covers all the processes for developing and using ML models, which is not only it’s training, but also managing and manipulating input data and evaluating the accuracy of the model.
Who can read this blog?
Although machine learning is not an easy topic to understand, I always try to make my explanations understandable to everyone.
In complex explanations, I make sure to never just blurt out the mathematical formula, but to include the logical reasoning behind it to enhance learning for everyone, even people who hate math.
Why follow this blog?
I don’t want to lie to you. I am a person who is passionate about machine learning, not a math and statistics genius with numerous degrees.
That is why whenever I study new things in this field I find myself struggling to understand the math and algorithms, and certainly sources that report the mathematical formula without explanation do not help.
Clear explanations
Yet I do not consider this a problem, but an advantage. In order to understand the concepts I have to make an effort to find simple and clear explanations. These solutions are the ones I also share with you.
Algorithms from scratch
Another thing that helps me fully understand machine learning algorithms is to rewrite them from 0. I forget about all the convenient modern Python libraries and just use math.
Is math necessary to study machine learning?
I don’t do bullshit to keep you in a comfort zone, so I’m going straight to the real answer.
YES, A LOT. Math is the language we use to write instructions to build our machine-learning models and AIs, so it’s fundamental to study this field.
I know it is a bad blow, it was for me too when I received this response. But trust that I will do everything to make the explanations and formulas simple, and in time you will see that it will be easier.