We can use Pandas to conduct Bayes Theorem and Scikitlearn to implement the Naive Bayes Algorithm. We take a step by step approach to understand Bayes and implementing the different options in Scikitlearn.
Rev 2 features interactive sessions, Q&A with industry luminaries, poster sessions for interesting modeling techniques and accomplishments, and stimulating conversations about how to make data science an enterprise-grade capability.
Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline.
The goal of this post is identify a single strategy for pulling data from a DataFrame using the Pandas Python library that is straightforward to interpret and produces reliable results.
“Don’t pick just random projects to work on and add it to your resume or portfolio. Solve a problem that relates to the companies that you’re interested in.”
We provide an overview of Generative Adversarial Networks (GANs), discuss challenges in GANs learning, and examine two promising GANs: the RadialGAN, designed for numbers, and the StyleGAN, which does style transfer for images.
We provide an updated comprehensive and objective survey of online Masters in Analytics and Data Science, including rankings, tuition, and duration of the education program.
Word clouds are simple visual summaries of the mostly frequently used words in a text, presenting essentially the same information as a histogram but are somewhat less precise and vastly more eye-catching. Get a quick sense of the themes in the recently released Mueller Report and its 448 pages of legal content.
To learn ALL the skills sets in data science is next to impossible as the scope is way too wide. There’ll always be some skills (technical/non-technical) that data scientists don’t know or haven’t learned as different businesses require different skill sets.
A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including DCGAN, StyleGAN and BigGAN, as well as some real-world examples.
Seaborn and Matplotlib are two of Python's most powerful visualization libraries. Seaborn uses fewer syntax and has stunning default themes and Matplotlib is more easily customizable through accessing the classes.
Introducing Sisense BloX, the tool that allows you to integrate your business platforms inside your dashboards using prebuilt templates. Users stay within the dashboard environment and go from understanding insights to taking action—in one click.
We discuss some of the negatives of using big data, including false equivalences and bias, vulnerability to security breaches, protecting against unauthorized access and the lack of international standards for data privacy regulations.
Distributed Artificial Intelligence (DAI) is a class of technologies and methods that span from swarm intelligence to multi-agent technologies. It is one of the subsets of AI where simulation has greater importance that point-prediction.
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.
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.
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.
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.
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.
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.
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.
If you read this article you will see that the job of data scientist is NOT listed. The rest of this article will explore why it is true that data scientists need to work in groups.
Introducing Europe’s largest data science training programme. Five weeks of intensive, project-based training turning exceptional analytical PhDs and MScs into Data Scientists.
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.
Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? Take part in the latest KDnuggets survey and have your say.
Marketing scientist Kevin Gray asks University of Missouri Professor Chris Wikle about Spatio-Temporal Statistics and how it can be used in science and business.
Introducing Sisense Hunch, the new way of handling Big Data sets that uses AQP technology to construct Deep Neural Networks (DNNs) which are trained to learn the relationships between queries and their results in these huge datasets.
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.
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.
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.
Some people find the path of formal education works well for them, but this may not work for everyone, in every situation. Here are eight ways that you can take a DIY approach to your data science education.
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.
Without the right visualization tools, raw data is of little use. Data visualization helps present the data in an interactive visual format. Here are the qualities to look for in a data visualization tool.
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.
Here is a list of 10 common mistakes that a senior data scientist — who is ranked in the top 1% on Stackoverflow for python coding and who works with a lot of (junior) data scientists — frequently sees.
Top Data Science and Machine Learning Methods Used in 2018, 2019
Pandas DataFrame Indexing
The most desired skill in data science
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