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Football Statistician | B. Eng (Mechanical) & M. Data Science

Installing R, Performing Data Manipulation, up to Applying Machine Learning with R.

For Data Scientists nowadays, there are many options to choose from in producing statistical visualisation. Let’s say we have Python as the most popular one. With Python we can perform pretty much anything from Machine Learning classification and regression, Deep Learning for computer vision, NLP, up to Audio Analysis. Aside from Python, we can perform Machine Learning algorithms through many other languages, such as JAVA, Scala, Lisp, C++, or C#. However, it is undeniable that R is the second most sought after skill from Data Scientist, at least up to 2021 according to LinkedIn as mentioned in the link below:

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A beginner-friendly guide for audio extraction and classification through Librosa

After providing a comprehensive guide for beginners for Convolutional Neural Network in image classification for cell images to differentiate between cells that are infected by malaria to healthy cells as seen in the link below, this time, I would like to articulate the procedures in extracting and classifying audio that allows our model to classify between a dog’s sound to a cat’s sound with Deep Learning algorithm.

Since this article will be focusing on the mechanism of classifying and extracting audio files by using Librosa, we will use a simple one-layer Perceptron Neural Network model, however, if you are interested…

A beginner’s guide for Image Classification and Convolutional Neural Network (CNN)

In this project, we will go through a dataset provided by the US’ National Institutes of Health for 27,558 different cell images from 150 patients that have been infected by parasites that cause Malaria called Plasmodium falciparum and mixed with cell images from 50 healthy patients, which can be downloaded through the link here. Our task is to build a Machine Learning / Deep Learning algorithm that is able to classify whether the detected cell is infected or uninfected by the parasite.

In this project, we are going to apply a Deep Learning algorithm, specifically a Convolutional Neural Network (CNN)…

Introduction of demographic filtering, content based filtering, and collaborative filtering in practical way

In this article, let’s discuss a project that articulates on how Machine Learning algorithm recommend what is the next movie that you might want to watch by using the Recommender System. This approach not only can be implemented for movie contents, but also for other digital objects chosen distinctively for each user, for instance, books, web pages, music, messages, products, dating preference, and of course, movies that have been widely executed by several companies to improve their customer experience within their digital platforms.

There are three types of recommender systems that will be implemented in this project, which are:

  • Demographic…

A step-by-step of theory and implementation of PCA

Principal Component Analysis is one of the most popular methods in increasing Machine Learning algorithm’s performance that crunch large number of data and features. However, sometimes PCA can be too complicated, too technical, or even too tedious to be properly understood the basic principals, therefore, I decided to write this article that articulate every step in practical way and easily digested for beginners.

Firstly, we need to understand better on why we need to use PCA in Machine Learning:

  1. Get rid of noise data: Sometimes in a dataset, there are too many data to be analysed whether we need to…

A comprehensive tutorial for Titanic Regression dataset for beginners in Machine Learning

This is a follow-up article from the Iris dataset article that you can find out here that gives an introductory guide for classification project where it is used to determine through the provided data whether the new data belong to class 1, 2, or 3. In this article, we will go through the other type of Machine Learning project, which is the regression type. Regression or sometimes it is called a predictive Machine learning project will be able to predict the future outcome through the learning of the historical dataset given to the Machine. …

You probably have heard a suggestion whether from your friends or just some random people on internet when you are asking what should I do if I want to start to learn Machine Learning or Data Science? Most of them will directly point their finger on Andrew Ng’s Coursera Machine Learning course straight away.

There are several reasons that this is the best suggestion for whoever wanted to start learning Machine Learning. Firstly, it has one of the most compact and high quality machine learning knowledge and skills packed into one course. …

A comprehensive tutorial for beginners in Machine Learning

If you have read my previous article on how to get started in Machine Learning here, this is the perfect next article because in this article, we will dive deeper in the practical way of all the things that was mentioned in that article, for instance, drawing various graphs for data visualisation, using libraries (if you still remember, these are all the functions in Machine Learning that you can use without actually code from scratch, such as for importing data, drawing graph, even applying basic ML algorithms directly), and the steps to successfully executing ML algorithm from the given dataset…

Machine learning is a broad term in the current 4.0 industrial revolution era where factories are trying to automate every single menial task to reduce costs, improve product quality, boost productivity, minimise workplace accidents, and enhance efficiency in materials usage. However, machine learning is not only implemented in the industrial environment, but also in the household equipment. …

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