Tutorials, Overviews
DataCamp - Easiest Way to Learn Data Science
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Learning R? Take this Intro to R for Data Science Tutorial. 
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Learning Python? Take this Intro to Python for Data Science Tutorial. |
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R Learning Path: From beginner to expert in R in 7 steps
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Latest:
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How Optimization Works - Apr 18, 2019.
Optimization problems are naturally described in terms of costs - money, time, resources - rather than benefits. In math it's convenient to make all your problems look the same before you work out a solution, so that you can just solve it the one time. -
Best Data Visualization Techniques for small and large data - Apr 17, 2019.
Data visualization is used in many areas to model complex events and visualize phenomena that cannot be observed directly, such as weather patterns, medical conditions or mathematical relationships. Here we review basic data visualization tools and techniques. -
Building a Flask API to Automatically Extract Named Entities Using SpaCy - Apr 17, 2019.
This article discusses how to use the Named Entity Recognition module in spaCy to identify people, organizations, or locations in text, then deploy a Python API with Flask. -
How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms - Apr 16, 2019.
We outline three different clustering algorithms - k-means clustering, hierarchical clustering and Graph Community Detection - providing an explanation on when to use each, how they work and a worked example. -
2019 Best Masters in Data Science and Analytics – Europe Edition - Apr 16, 2019.
We provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across Europe. -
K-Means Clustering: Unsupervised Learning for Recommender Systems - Apr 16, 2019.
In this article we apply unsupervised learning on a Magic: The Gathering dataset in order to build a system for making recommendations. -
Data Science with Optimus Part 2: Setting your DataOps Environment - Apr 16, 2019.
Breaking down data science with Python, Spark and Optimus. Today: Data Operations for Data Science. Here we’ll learn to set-up Git, Travis CI and DVC for our project. -
An introduction to explainable AI, and why we need it - Apr 15, 2019.
We introduce explainable AI, why it is needed, and present the Reversed Time Attention Model, Local Interpretable Model-Agnostic Explanation and Layer-wise Relevance Propagation. -
Data Science with Optimus Part 1: Intro - Apr 15, 2019.
With Optimus you can clean your data, prepare it, analyze it, create profilers and plots, and perform machine learning and deep learning, all in a distributed fashion, because on the back-end we have Spark, TensorFlow, Sparkling Water and Keras. It’s super easy to use. -
AI For Ordinary Folks - Apr 11, 2019.
There are many excellent books, articles, YouTube lectures and blogs on AI and topics related to it aimed at data scientists and AI researchers. You may want to, instead, check out this list of AI resources crafted for ordinary folks. -
An Introduction on Time Series Forecasting with Simple Neural Networks & LSTM - Apr 11, 2019.
How to develop Artificial Neural Networks and LSTM recurrent neural networks for time series prediction in Python with the Keras deep learning network. -
All you need to know about text preprocessing for NLP and Machine Learning - Apr 9, 2019.
We present a comprehensive introduction to text preprocessing, covering the different techniques including stemming, lemmatization, noise removal, normalization, with examples and explanations into when you should use each of them. -
Advanced Keras — Constructing Complex Custom Losses and Metrics - Apr 8, 2019.
In this tutorial I cover a simple trick that will allow you to construct custom loss functions in Keras which can receive arguments other thany_trueandy_pred. -
Another 10 Free Must-See Courses for Machine Learning and Data Science - Apr 5, 2019.
Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas. -
Building a Recommender System - Apr 4, 2019.
A beginners guide to building a recommendation system, with a step-by-step guide on how to create a content-based filtering system to recommend movies for a user to watch. -
Predict Age and Gender Using Convolutional Neural Network and OpenCV - Apr 4, 2019.
Age and gender estimation from a single face image are important tasks in intelligent applications. As such, let's build a simple age and gender detection model in this detailed article. -
Getting started with NLP using the PyTorch framework - Apr 3, 2019.
We discuss the classes that PyTorch provides for helping with Natural Language Processing (NLP) and how they can be used for related tasks using recurrent layers. -
Top 8 Data Science Use Cases in Gaming - Apr 3, 2019.
The understanding of the data value for optimization and improvement of gaming makes specialists search for new ways to apply data science and its benefits in the gaming business. Therefore, various specific data science use cases appear. Here is our list of the most efficient and widely applied data science use cases in gaming. -
Which Face is Real? - Apr 2, 2019.
Which Face Is Real? was developed based on Generative Adversarial Networks as a web application in which users can select which image they believe is a true person and which was synthetically generated. The person in the synthetically generated photo does not exist.
March:
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Explaining Random Forest (with Python Implementation)
We provide an in-depth introduction to Random Forest, with an explanation to how it works, its advantages and disadvantages, important hyperparameters and a full example Python implementation. - A Beginner’s Guide to Linear Regression in Python with Scikit-Learn
- Interpolation in Autoencoders via an Adversarial Regularizer
- D3.js Graph Gallery for Data Visualization
- 7 “Gotchas” for Data Engineers New to Google BigQuery
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The Deep Learning Toolset — An Overview
We are observing an increasing number of great tools that help facilitate the intricate process that is deep learning, making it both more accessible and more efficient.
- How to Choose the Right Chart Type
- Data Pipelines, Luigi, Airflow: Everything you need to know
- Pedestrian Detection in Aerial Images Using RetinaNet
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R vs Python for Data Visualization
This article demonstrates creating similar plots in R and Python using two of the most prominent data visualization packages on the market, namely ggplot2 and Seaborn. - Feature Reduction using Genetic Algorithm with Python
- Checklist for Debugging Neural Networks
- Deep Compression: Optimization Techniques for Inference & Efficiency
- Deploy your PyTorch model to Production
- Mastering Fast Gradient Boosting on Google Colaboratory with free GPU
- How to Train a Keras Model 20x Faster with a TPU for Free
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Artificial Neural Networks Optimization using Genetic Algorithm with Python
This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance. - Top R Packages for Data Cleaning
- Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision
- Advanced Keras — Accurately Resuming a Training Process
- Towards Automatic Text Summarization: Extractive Methods
- Object Detection with Luminoth
- People Tracking using Deep Learning
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Who is a typical Data Scientist in 2019?
We investigate what a typical data scientist looks like and see how this differs from this time last year, looking at skill set, programming languages, industry of employment, country of employment, and more.
- Neural Networks with Numpy for Absolute Beginners — Part 2: Linear Regression
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Another 10 Free Must-Read Books for Machine Learning and Data Science
Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.
- Deconstructing BERT, Part 2: Visualizing the Inner Workings of Attention
- Neural Networks with Numpy for Absolute Beginners: Introduction
- GANs Need Some Attention, Too
- Comparing MobileNet Models in TensorFlow