Introduction As a Python programmer, dealing with data is a crucial aspect of your day-to-day work. However, as much as we strive to ensure the consistency and accuracy of our data sets, mishaps can occur when working with strings that must be converted to floats for further computation. The process of converting strings to floats […]
Introduction In Python, strings and bytes are two distinct data types that are used to represent text and binary data respectively. Strings in Python are sequences of Unicode characters wrapped inside quotes (either single quotes or double quotes). They can contain any printable character including spaces, digits, and special symbols like punctuation marks. Bytes, on […]
Introduction In this blog post we’re going to take a deep dive into support vector classification (SVC) in Python. We will start by looking at the basics of SVC and how it works, before moving on to discuss some of its most important features and parameters. Finally, I’ll show you how easy it is to […]
Introduction Learning how to skip a value in a Python list is an important skill to have as it can help you write more efficient and effective code. Skipping values in lists can be done in several different ways, depending on the specific situation. Learning how to skip a value in a Python list is […]
If you are reading this, then you are probably well aware of OpenAI’s large language models, such as GPT-3 and its dialogue interface, ChatGPT. You may have also heard of concerns from teachers and employers over heavy use of Large Language Models (LLMs) to generate content for assignments, such as generating an essay that was […]
Data Science, Machine Learning, Tutorials
Introduction In this blog post we’ll be discussing nonlinear regression. In particular, we’ll analyze “polynomial regression”, which is one of the main processes to quickly create a non-linear regression model by expanding the existing data set. Nonlinear regression allows us to […]
Data Science, Machine Learning, Python Basics
Introduction In this blog post, we’ll be exploring Linear Regression in Machine Learning with Python. There are many potential applications for linear regression, especially for your business, including: Sales forecasting: Linear regression can be used to predict future sales based on historical data, such as product pricing, marketing expenses, and consumer demographics. Inventory management: Linear […]
Data Science, Machine Learning
Regression analysis is a commonly used statistical technique for predicting the relationship between a dependent variable and one or more independent variables. In the field of machine learning, regression algorithms are used to make predictions about continuous variables, such as housing prices, student scores, or medical outcomes. Python, being one of the most widely used […]
Analyzing Taylor Swift’s Songs¶ To celebrate Taylor’s new album which has 10 of the top 10 Billboard charts (first time to ever happen), let’s explore Taylor’s discography with the Spotify API. Get credentials from Spotify API¶ Go to your Spotify Dashboard at https://developer.spotify.com/dashboard/ and create a new application, then grab the […]
Data Science, Machine Learning
Deep Learning without labels – Self-Supervised Learning¶ In this blog post we’ll discuss Self-Supervised Learning! Classical supervised learning suffers from four main problems: Fully labelled datasets are expensive or not available at all. There is a large amount of unlabeled datasets which cannot be leveraged by Supervised Learning. Difficuly […]
