Machine Learning Weekly [small steps] S1E01
Hi everyone from sunny Lviv,
I have an idea to make my own “Machine Learning Weekly Newsletter”, but for now, I am not much experienced to say that something is worth reading and it will make you some kind of professional.
Anyway, I decided to learn Machine Learning and the best way to master something is to practice it and to talk about it — the first part I will do at the office and the second you will see here in “Machine Learning Weekly”.
So, without further ado, let’s get started.
Motivation and fun
䷉ How Japanese farmer made an ML system to sort cucumbers on his parent’s farm — the story of how ML could be applicable in some interesting projects that you could not even imagine.
📼 Predictive Maintenance & Monitoring using Machine Learning: Demo & Case study — the talk about how Machine Learning and Predictive Maintenance could make the taste of beer better. You will know the basics of the study and have fun, because if you don’t happy with what you do what is the point of doing it?
Tech Articles
䷉ Data Science vs Machine Learning vs Artificial intelligence [Difficult stuff in simple words] — here I am trying to explain the difference between those popular buzzwords, really hope it will help freshers to understand what is what and not get lost before even started.
䷉ What Machine Learning can do and what can not? — trying to explain in simple words what machine actually can do and what can not. It is really important to know what is in your powers before starting to work on any project.
Video courses
📹 AI For Everyone Offered By deeplearning.ai and Andrew Ng — it is a good starting point if you just making your first steps in this field of study.
Books
📕 Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming by Eric Matthes — my first book on the way of learning Python. I have previous experience in Java for 5+ years. After all that I’ve seen before in programming, this book looks too simple for me. Anyway, it is really practical which I love a lot and have a few projects, so I can’t say that time I spent reading it was wasted.
📗 Data Science from Scratch: First Principles with Python by Joel Grus
📘 Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz — this book could give you a pretty good example what data could be extracted from Big Data gathered from millions of people and could guess that AI could and companies from FAANG could know about you more that you do.
