Sep
2016 Oct Tutorials, Overviews
http likes 146All (98) | Courses, Education (7) | Meetings (11) | News, Features (20) | Opinions, Interviews (35) | Software (5) | Tutorials, Overviews (17) | Webcasts & Webinars (3)
- Decision Tree Classifiers: A Concise Technical Overview
- Nov 3, 2016.
The decision tree is one of the oldest and most intuitive classification algorithms in existence. This post provides a straightforward technical overview of this brand of classifiers.
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Learn Data Science for Excellence and not just for the Exams
- Oct 31, 2016.
Are you currently pursuing your masters in Data Science? Overwhelmed with Buzzwords and Information? Don’t know where and how to start your study? Then start with this article and a starter kit provided, but learn it for excellence and not just for the exams. - Deep Learning Research Review: Generative Adversarial Nets
- Oct 31, 2016.
This edition of Deep Learning Research Review explains recent research papers in the deep learning subfield of Generative Adversarial Networks. Don't have time to read some of the top papers? Get the overview here.
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Machine Learning: A Complete and Detailed Overview
- Oct 28, 2016.
This is an overview (with links) to a 5-part series on introductory machine learning. The set of tutorials is comprehensive, yet succinct, covering many important topics in the field (and beyond). - 5 Steps for Advanced Data Analysis using Visualization
- Oct 28, 2016.
In most of the scientific researches, due to large amount of experiment data, statistical analysis is typically done by technical experts in computing and statistics. Unfortunately, these experts are not the experts of underlying research; which may cause gaps in analysis. If actual researchers are given easy to use tools and methods to handle and analyse data, it will enrich the research outcome for sure.
- Learn Data Science in 8 (Easy) Steps
- Oct 27, 2016.
Want to learn data science? Check out these 8 (easy) steps to set out in the right direction!
- Frequent Pattern Mining and the Apriori Algorithm: A Concise Technical Overview
- Oct 27, 2016.
This post provides a technical overview of frequent pattern mining algorithms (also known by a variety of other names), along with its most famous implementation, the Apriori algorithm.
- Jupyter Notebook Best Practices for Data Science
- Oct 20, 2016.
Check out this overview of Jupyter notebook best practices as pertains to data science. Novice or expert, you may find something of use here.
- A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18!
- Oct 20, 2016.
This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.
- What is emotion analytics and why is it important?
- Oct 19, 2016.
In today’s Internet world, humans express their Emotions, Sentiments and Feelings via text/comments, emojis, likes and dislikes. Understanding the true meanings behind the combinations of these electronic symbols is very crucial and this is what this article explains.
- Clustering Key Terms, Explained
- Oct 18, 2016.
Getting started with Data Science or need a refresher? Clustering is among the most used tools of Data Scientists. Check out these 10 Clustering-related terms and their concise definitions.
- MLDB: The Machine Learning Database
- Oct 17, 2016.
MLDB is an opensource database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
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Top 10 Data Science Videos on Youtube
- Oct 17, 2016.
Learning and the future are the key topics in the recent Youtube videos on Data Science. The main questions revolve around: “how to become a Data Scientist”, “what is a data scientist”, and “where data science is going”. But why there is so little explanation of data science to the masses? - Artificial Intelligence, Deep Learning, and Neural Networks, Explained
- Oct 14, 2016.
This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.
- Deep Learning Key Terms, Explained
- Oct 12, 2016.
Gain a beginner's perspective on artificial neural networks and deep learning with this set of 14 straight-to-the-point related key concept definitions, including Biological Neuron, Multilayer Perceptron (MLP), Feedforward Neural Network, and Recurrent Neural Network.
- Adversarial Validation, Explained
- Oct 7, 2016.
This post proposes and outlines adversarial validation, a method for selecting training examples most similar to test examples and using them as a validation set, and provides a practical scenario for its usefulness.
- Beginner’s Guide to Apache Flink – 12 Key Terms, Explained
- Oct 4, 2016.
We review 12 core Apache Flink concepts, to better understand what it does and how it works, including streaming engine terminology.
How to Explain Key Machine Learning Algorithms at an Interview
Roadmap to Natural Language Processing (NLP)