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

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…

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.

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

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 Step by Step Guide to K-Means Clustering — What is Clustering Algorithm? In a business context: Clustering algorithm is a technique that assists customer segmentation which is a process of classifying similar customers into the same segment. Clustering algorithm helps to better understand customers, in terms of both static demographics and dynamic behaviors. …

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. …

Step-by-Step Guide from Data Preprocessing to Model Evaluation — What is Logistic Regression? Don’t let the name logistic regression tricks you, it usually falls under the category of the classification algorithm instead of regression algorithm. Then, what is a classification model? Simply put, the prediction generated by a classification model would be a categorical value, e.g. …

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…

Take a quick tour around the building blocks of Machine Learning download the high-resolution cheatsheet from the website: Data Analytics | Visual Design Studio Free data Analysis tutorials and downloadable notes for data science lovers.www.visual-design.net 1. Association Rule Mining (ARM) Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction.