Posts - Page 4 of 5
Data types, variables and operators in Python
In this post, we are going to discover all you need to know about data types, operators, and variables in python. I’ll be using jupyter notebook, feel free to use any text editor or IDE of your choice but I do highly recommend to use jupyter especially if you are interested in data science. I also suggest to type the codes and run it on your system and see your results. This tutorial post is entirely written in Jupyter and exported as a markdown file to Jekyll.
Introduction to Jupyter notebook
Jupyter notebook formally called ipython, is a web application that runs code in the browser with addition of comments, visualizations, paragraph, equations, title, links, figures, and LaTex by using the markdown language which is very useful while writing code that requires explanation.
Python, The programming language of Machine Learning
When I say python folk will think that I am talking about the snake. No, ain’t talking about the snake, I am talking about Python the programming language. Python is an open-source high-level programming language(a programming language is a language computers can understand) created by Guido van Rossum and was first released to the public in 1991. Its main features are code readability by using whitespace instead of the curly braces, dynamic typing, and automatic memory management.
The requirements to get started in Machine Intelligence
Machine learning is a cross-disciplinary field that includes computer science, mathematics, and sometimes domain knowledge. We can’t just focus on one part and expect to be great at it, and no you don’t have to be a master at everything to get started as it is said jack of all trades, master of none. You need to have a basic understanding of these topics
Why deep learning now?
As a refresh from my previous blog, deep learning is a subset of machine learning which learns by using layers of neurons like the ones found in our brain to output an expected result. The layers are organized in a way that can break down the input into different layers of abstraction. The more the layers we have, the deeper the neural network.