We compare Gartner 2018 Magic Quadrant for Data Science, Machine Learning Platforms vs its 2017 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, Alteryx, H2O.ai, and Domino.
This article explains the synergy between DevOps and Machine Learning and their applications like tracking application delivery, troubleshooting and triage analytics, preventing production failures, etc.
Control structures allow you to specify the execution of your code. They are extremely useful if you want to run a piece of code multiple times, or if you want to run a piece a code if a certain condition is met.
This article provides a short overview of emerging data scientist types and their unique skillsets, as well as a guide for HR professionals and analytics managers who are looking to hire their first data scientists or build a data science team. Included are an overview of skills for each type and specific questions that can be asked to assess candidates.
In this article, we will compare the most commonly used platforms and analyze their main features to help you choose one or several platforms that will provide indispensable aid for your work communication.
We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors.
Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists
Interested in learning the concepts behind Logistic Regression (LogR)? Looking for a concise introduction to LogR? This article is for you. Includes a Python implementation and links to an R script as well.
In order to understand how to estimate LTV, it is useful to first think about evaluating a customer’s lifetime value at the end of their relationship with us.
One of the gems that I felt needed to be written down from Ng's deep learning courses is his general recipe to approaching a deep learning algorithm/model.
Google Colaboratory is a promising machine learning research platform. Here are 3 tips to simplify its usage and facilitate using a GPU, installing libraries, and uploading data files.
For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
In this post, I will demonstrate how to use Google Colab for fastai. You can use GPU as a backend for free for 12 hours at a time. GPU compute for free? Are you kidding me?
Automating feature engineering optimizes the process of building and deploying accurate machine learning models by handling necessary but tedious tasks so data scientists can focus more on other important steps.
This post presents 5 fantastic practical machine learning resources, covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks.
This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out.
Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy). Thus getting it right from the get go would mean lesser time for us to train the model.
In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.
Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms
A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018
Top 20 Python AI and Machine Learning Open Source Projects
5 Fantastic Practical Machine Learning Resources
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