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 The Machine Learning Project Checklist
In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow."
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Deep Learning Cheat Sheets
Check out this collection of high-quality deep learning cheat sheets, filled with valuable, concise information on a variety of neural network-related topics.
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10 Free Must-See Courses for Machine Learning and Data Science
By Matthew Mayo, KDnuggets Managing Editor on November 8, 2018 in Data Science, Deep Learning, fast.ai, Google, Linear Algebra, Machine Learning, MIT, NLP, Reinforcement Learning, Stanford, YandexCheck out a collection of free machine learning and data science courses to kick off your winter learning season.
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Machine Learning Classification: A Dataset-based Pictorial
In order to relate machine learning classification to the practical, let's see how this concept plays out, step by step (and with images), specifically in direct relation to a dataset.
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Notes on Feature Preprocessing: The What, the Why, and the How
This article covers a few important points related to the preprocessing of numeric data, focusing on the scaling of feature values, and the broad question of dealing with outliers.
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Implementing Automated Machine Learning Systems with Open Source Tools
What if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline? Rest assured that there is no need to reinvent any wheels.
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The Main Approaches to Natural Language Processing Tasks
Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches.
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A Concise Explanation of Learning Algorithms with the Mitchell Paradigm
A single quote from Tom Mitchell can shed light on both the abstract concept and concrete implementations of machine learning algorithms.
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You Aren’t So Smart: Cognitive Biases are Making Sure of It
Cognitive biases are tendencies to think in certain ways that can lead to systematic deviations from a standard of rationality or good judgment. They have all sorts of practical impacts on our lives, whether we want to admit it or not.
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Iterative Initial Centroid Search via Sampling for k-Means Clustering
Thinking about ways to find a better set of initial centroid positions is a valid approach to optimizing the k-means clustering process. This post outlines just such an approach.
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The Machine Learning Project Checklist
10 Free Must-See Courses for Machine Learning and Data Science
The Main Approaches to Natural Language Processing Tasks
You Aren’t So Smart: Cognitive Biases are Making Sure of It