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. It can be either classifying data into a category (classification problem) or…

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. I would like to share my experience and hope to bring some insights if you want to…

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…

Step-by-Step Guide to Build Your First YouTube Analytics App — For the majority of the time, data science/data analytics projects end up as delivering a static report or dashboard, which tremendously downgrades the efforts and thoughts being put into the process. Alternatively, web app is a great way to demonstrate your data analytics work, which can be further expanded as…

A curated list to get your hands on MLOps, Deep Learning, XAI — ChatGPT has become a recent hype in Artificial Intelligence and Machine Learning, as this chatbot gives fast responses and unexpectedly human-like conversation with high accuracy. As an AI and ML tool itself, what does it say about the current trends in Machine Learning? …

Linear Algebra for Machine Learning — Covariance Matrix, Eigenvector and Principal Component — In my previous article, we have talked about applying linear algebra for data representation in machine learning algorithms, but the application of linear algebra in ML is much broader than that. How is Linear Algebra Applied for Machine Learning? Starting from using matrix and vector for data representationtowardsdatascience.com

Starting from using matrix and vector for data representation — Truth be told, the role of linear algebra in machine learning has been perplexing me, as mostly we learn these concepts (e.g. vector, matrix) in a math background while discarding their applications in the machine learning context. In fact, linear algebra has several foundational use cases in machine learning, including…

Case Study: Dynamic EDA for Qatar World Cup Teams — This article will introduce the tool, Plotly [1], that brings data visualization and exploratory data analysis (EDA) to the next level. You can use this open source graphing library to make your notebook more aesthetic and interactive, regardless if you are a Python or R user. To install Plotly, use…

On the differences between these models, and how you should use them — What is Time Series? Time series is a unique type of problem in machine learning where the time component plays a critical role in the model predictions. As observations are dependent on adjacent observations, this violates the assumption that observations are independent to each other followed by most conventional machine learning models. Common use…