A beginner’s guide

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What is anomaly detection?

Anomaly detection also known as outlier detection is the process of finding data points within a dataset that differs from the rest. Common applications of anomaly detection includes fraud detection in financial transactions, fault detection and predictive maintenance.

Broadly speaking, anomaly detection can be categorized into supervised and unsupervised realm…

Automate the “boring” stuff. Accelerate your model development lifecycle.

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Motivation

A typical machine learning workflow is an iterative cycle of data processing, feature processing, model training, and evaluation. Imagine having to experiment with different combinations of data processing methods, model algorithm, and hyperparameters until we get a satisfactory model performance. …

Yes you heard it right. It’s free.

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GPUs are an essential part of training deep learning models and they don’t come cheap. In this article, we examine some platforms that provide free GPUs without the restrictions of free trial periods, limited free credits or requiring a credit card during sign up.

Quick Comparison

The 3 platforms we are examining…

How to train your own high performing sentiment analysis model

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Objective

Sentiment analysis is a technique in natural language processing used to identify emotions associated with the text. Common use cases of sentiment analysis include monitoring customers’ feedbacks on social media, brand and campaign monitoring.

In this article, we examine how you can train your own sentiment analysis model on a…

Accelerate your machine learning development cycle

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What is Auto ML?

Automated Machine Learning (AutoML) is the process of automating tasks in the machine learning pipeline such as data preprocessing, hyperparameter tuning, model selection and evaluation. In this article we will examine how to utilize open source automated machine learning package from H2O to accelerate a Data Scientist’s model development process.

Setup

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Sentiment analysis, also known as opinion mining, is a technique in natural language processing used to identify emotions associated with the text. Some use cases of sentiment analysis include getting customer’s sentiment for making improvements to products/services and monitoring public opinions of policy changes by administrators for fine-tuning of policies…

Edwin Tan

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