Feature selection is a very important technique in machine learning. In this post we discuss one of the most common optimization algorithms for multi-modal fitness landscapes - evolutionary algorithms.
We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.
Data science needs fast computation and transformation of data. NumPy objects in Python provides that advantage over regular programming constructs like for-loop. How to demonstrate it in few easy lines of code?
Introducing the Natural Language Processing Library for Apache Spark - and yes, you can actually use it for free! This post will give you a great overview of John Snow Labs NLP Library for Apache Spark.
One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.
We compare survival analysis to other predictive techniques, and provide examples of how it can produce business value, with a focus on Kaplan-Meier and Cox Regression methods which have been underutilized in business analytics.
The definitions of training, validation, and test sets can be fairly nuanced, and the terms are sometimes inconsistently used. In the deep learning community, “test-time inference” is often used to refer to evaluating on data in production, which is not the technical definition of a test set.
This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.
Wikipedia is a rich source of well-organized textual data, and a vast collection of knowledge. What we will do here is build a corpus from the set of English Wikipedia articles, which is freely and conveniently available online.
Python has a ton of plotting libraries—but which ones should you use? And how should you go about choosing them? This webinar shows you key starting points and demonstrates how to solve a range of common problems.
Although NLP and text mining are not the same thing, they are closely related, deal with the same raw data type, and have some crossover in their uses. Let's discuss the steps in approaching these types of tasks.
We introduce a general framework for developing time series models, generating features and preprocessing the data, and exploring the potential to automate this process in order to apply advanced machine learning algorithms to almost any time series problem.
Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!
Two years. Two years is the maximum amount of time you should spend focused on your learning, education and training. That’s exactly why this guide is focused on honing the most beneficial skills in two years.
PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. With Spark, you can get started with big data processing, as it has built-in modules for streaming, SQL, machine learning and graph processing.
Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.
The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.
The first comprehensive and objective survey of online Masters in Analytics / Data Science, including rankings, tuition, and duration of the education program.
This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.
Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these five individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.
Kevin and Koen may buy the same brand for the same reasons. On the other hand, they may buy the same brand for different reasons, or buy different brands for the same reasons, or even different brands for different reasons. The brands they purchase and the reasons why may vary by occasion, too.
Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.
We analyze the results of Data Science / Machine Learning peak demand poll, examine the split between optimists and pessimists, and try to explain why predictions look so similar regardless of experience, affiliation, and region?
This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.
Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.
Learn how to identify and manage operational risk, litigation risk and reputational risk. This course is brought to you by HarvardX in collaboration with GetSmarter, experts in online education for working professionals.
In the past years, several niche tools have appeared to mine organizational business processes. In this article, we’ll show you that it is possible to get started with “process mining” using well-known data science programming languages as well.
In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.
Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.
Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras
Why You Should Forget ‘for-loop’ for Data Science Code and Embrace Vectorization
The 10 Statistical Techniques Data Scientists Need to Master
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