How to Build a Machine Learning Model Pipeline in Python — Supervised vs. Unsupervised vs. Reinforcement Learning The easiest way to distinguish a supervised learning and unsupervised learning is to see whether the data is labelled or not. Supervised learning learns a function to make prediction of a defined label based on the input data. …

A Project Based Approach to Get Started in Data Science — As someone who don’t hold a degree in data science, I am truly passionate about this field and decided to experiment on building my own curriculum to learn data science in spare time. …

Comprehensive Data Exploration Process with One-Click — Exploratory Data Analysis, also known as EDA, has become an increasingly hot topic in data science. Just as the name suggests, it is the process of trial and error in an uncertain space, with the goal of finding insights. It usually happens at the early stage of the data science…

A Swift Approach to Spot Trending Topics in 10 Minutes — 1. Install Twint and Import Libraries Twint is a powerful yet straightforward Python package that allows scraping Twitter posts. GitHub — twintproject/twint: An advanced Twitter scraping & OSINT tool written in Python that… An advanced Twitter scraping & OSINT tool written in Python that doesn’t use Twitter’s API, allowing you to scrape a…github.com

How to Build Your First Deep Neural Network — What is Deep Learning? Deep learning is a sub-category of machine learning models that uses neural networks. In a nutshell, neural networks connect multiple layers of nodes and each node can be considered as a mini machine learning model. The output of the model then feeds as the input of the subsequent node.

An Interactive Guide to the What/Why/How of Power — What is Statistical Power? Statistical Power is a concept in hypothesis testing that calculates the probability of detecting a positive effect when the effect is actually positive. In my previous post, we walkthrough the procedures of conducting a hypothesis testing. …

A Beginner Friendly Guide to Implementation and Comparison — In my previous post “Top Machine Learning Algorithms for Classification”, we walked through common classification algorithms. Now let’s dive into the other category of supervised learning — regression, where the output variable is continuous and numeric. Mainly, how to implement and compare four common types of regression models: Linear Regression

Step-by-Step Guide to Recommender System — Recommender system has become a rising topic as we demand more customized contents push to our daily feeds. I guess we are all familiar with the recommended videos on YouTube, and we are all — more than once — the victims of late-night Netflix binge watching. There are two popular…

From EDA to Feature Engineering to Model Evaluation — Linear regression is a typical regression algorithm that is responsible for numeric prediction. It is distinct to classification models — such as decision tree, support vector machine or neural network. …