Understanding Linear Regression With Python Medium

Everyone has their toolbox of favorite programs that they install on their machines whenever they get a new one. Working with Python, I've built up my own essential toolkit. Here are the libraries and programs I reach for whenever I get a new machine. Jupyter is a way of creating interactive notebooks that blend text, graphics, and code. This is a unique form of programming. It's taken the scientific programming world by storm.

It's so easy to run and re-run snippets of code. While it's not a Python-specific tool, supporting other languages, Python is one of the open-source languages of choice for scientific computing, including stats. Jupyter notebooks were originally part of IPython, which enhances the interactive Python environment. I mainly use IPython for experimentation and Jupyter notebooks when I want to save my results. This is not a specific Python tool, but Mamba is useful for setting up my environment on a new machine. While Python is included on many systems, on Linux systems, it's mainly used for supporting scripts and other functions of the OS itself, and not meant for programming projects.

If I want to install packages, I'll have to either use my package manager or set up a virtual environment. Mamba allows me to easily set up custom environments with the packages I want and switch back and forth. This makes it much less likely for me to mess up my system Python environment. Learn the theory, math, and engineering behind machine learning with these highly recommended free books. Most of the time, you learn better by building things, as is common in frontend development. I remember when I first started coding, I spent a month reading about UI/UX, HTML, and CSS, but I still couldn’t design a simple interface.

That’s because this kind of learning requires practice, projects, and hands-on experience. Machine learning is different. In this field, having a deep understanding of the theory is more rewarding. It’s not just about applying simple rules like in other areas. If you don’t understand what’s happening under the hood, it’s easy to hit roadblocks or make mistakes in your models. That’s why I strongly recommend reading high-quality books on machine learning.

This article is part of our new series where we highlight FREE but absolutely worth-it books. If you are a serious learner and want to strengthen your foundation, this list is for you. Let's start with the first recommendation. Understanding Machine Learning: From Theory to Algorithms introduces machine learning in a rigorous but principled manner, starting from the core question of how to convert experience (training data) into expertise (predictive models). It builds from foundational theoretical ideas through to practical algorithmic paradigms. It gives an extensive account of the mathematics behind learning, addresses both the statistical and computational complexity of learning tasks, and covers algorithmic methods such as stochastic gradient descent, neural networks, structured output learning...

It’s perfect for anyone who wants to go beyond using black-box models and really understand why algorithms behave the way they do.

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