Tutorials, Overviews
DataCamp - Easiest Way to Learn Data Science
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Learning R? Take this free Intro to R for Data Science Tutorial. 
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Check also these fantastic posts:
R Learning Path: From beginner to expert in R in 7 steps
Comprehensive Guide to Learning Python for Data Science
Latest:
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A comprehensive survey on graph neural networks - Feb 15, 2019.
This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them. -
Decision Trees — An Intuitive Introduction - Feb 14, 2019.
An extensive introduction including a look at decision tree classification, data distribution, decision tree regression, decision tree learning, information gain, and more. -
An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning - Feb 13, 2019.
If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library. -
A Quick Guide to Feature Engineering - Feb 11, 2019.
Feature engineering plays a key role in machine learning, data mining, and data analytics. This article provides a general definition for feature engineering, together with an overview of the major issues, approaches, and challenges of the field. -
Understanding Gradient Boosting Machines - Feb 6, 2019.
However despite its massive popularity, many professionals still use this algorithm as a black box. As such, the purpose of this article is to lay an intuitive framework for this powerful machine learning technique. -
6 Data Visualization Disasters – How to Avoid Them - Feb 5, 2019.
If you intend to use data visualizations in a presentation or publication, be certain that your audience will understand and trust the information. Here are six mistakes you will want to avoid. -
From Good to Great Data Science, Part 1: Correlations and Confidence - Feb 5, 2019.
With the aid of some hospital data, part one describes how just a little inexperience in statistics could result in two common mistakes. -
Intuitive Visualization of Outlier Detection Methods - Feb 5, 2019.
Check out this visualization for outlier detection methods, and the Python project from which it comes, a toolkit for easily implementing outlier detection methods on your own. -
Using Caret in R to Classify Term Deposit Subscriptions for a Bank - Feb 4, 2019.
This article uses direct marketing campaign data from a Portuguese banking institution to predict if a customer will subscribe for a term deposit. We’ll be working with R’s Caret package to achieve this. -
Trending Deep Learning Github Repositories - Feb 1, 2019.
Check these pair of resources for trending and top GitHub deep learning repositories for some new ideas on what to be looking out for.
January:
- ELMo: Contextual Language Embedding
- Random forests explained intuitively
- Building an image search service from scratch
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7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition
With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python! - Airbnb Rental Listings Dataset Mining
- Machine Learning Security
- Data Science Project Flow for Startups
- How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy
- Building AI to Build AI: The Project That Won the NeurIPS AutoML Challenge
- Logistic Regression: A Concise Technical Overview
- 2018’s Top 7 R Packages for Data Science and AI
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2018’s Top 7 Python Libraries for Data Science and AI
This is a list of the best libraries that changed our lives this year, compiled from my weekly digests.
- Automated Machine Learning in Python
- Comparing Machine Learning Models: Statistical vs. Practical Significance
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How to build an API for a machine learning model in 5 minutes using Flask
Flask is a micro web framework written in Python. It can create a REST API that allows you to send data, and receive a prediction as a response. - Word Embeddings & Self-Supervised Learning, Explained
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Ontology and Data Science
In simple words, one can say that ontology is the study of what there is. But there is another part to that definition that will help us in the following sections, and that is ontology is usually also taken to encompass problems about the most general features and relations of the entities which do exist.
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How to solve 90% of NLP problems: a step-by-step guide
Read this insightful, step-by-step article on how to use machine learning to understand and leverage text.
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End To End Guide For Machine Learning Projects
Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.
- Practical Apache Spark in 10 Minutes
- Python Patterns: max Instead of if
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Top 10 Books on NLP and Text Analysis
When it comes to choosing the right book, you become immediately overwhelmed with the abundance of possibilities. In this review, we have collected our Top 10 NLP and Text Analysis Books of all time, ranging from beginners to experts.
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NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing
Trying to keep up with advancements at the overlap of neural networks and natural language processing can be troublesome. That's where the today's spotlighted resource comes in.
- Comparison of the Text Distance Metrics
- What to do when your training and testing data come from different distributions
- Approaches to Text Summarization: An Overview
- The Backpropagation Algorithm Demystified
- 3 More Google Colab Environment Management Tips