5 Advanced Features of Pandas and How to Use Them

The pandas library offers core functionality when preparing your data using Python. But, many don't go beyond the basics, so learn about these lesser-known advanced methods that will make handling your data easier and cleaner.

https://www.kdnuggets.com/2019/10/5-advanced-features-pandas.html

PySpark for Beginners – Take your First Steps into Big Data Analytics (with Code)

Overview Big Data is becoming bigger by the day, and at an unprecedented pace How do you store, process and use this amount of ...
The post PySpark for Beginners – Take your First Steps into Big Data Analytics (with Code) appeared first on Analytics Vidhya.

https://www.analyticsvidhya.com/blog/2019/10/pyspark-for-beginners-first-steps-big-data-analysis/

DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models

Visualizing the datasets is an essential component to identify potential sources of bias and unfairness. DeepMind relied on a method called Causal Bayesian networks (CBNs) to represent and estimate unfairness in a dataset.

https://www.kdnuggets.com/2019/10/deepmind-using-old-technique-evaluate-fairness-machine-learning-models.html

How Bayes’ Theorem is Applied in Machine Learning

Learn how Bayes Theorem is in Machine Learning for classification and regression!

https://www.kdnuggets.com/2019/10/bayes-theorem-applied-machine-learning.html

DataTech20 Seeking Speaker Submissions (16 March 2020, Glasgow)

DataTech is a one-day conference on 16 Mar 2020, at the Technology and Innovation Centre in Glasgow, focusing on key topics in data science, and welcoming members of industry, academia, and the public sector alike. DataTech provides a forum for these different communities to meet, share knowledge and expertise, and forge new collaborations. We are currently welcoming workshop, talk and poster proposals for the DataTech20 conference.

https://www.kdnuggets.com/2019/10/datatech-speaker-submissions-march-glasgow.html

Data Sources 101

Data collection is one of the first steps of the data lifecycle — you need to get all the data you require in the first place. To collect the right data, you need to know where to find it and determine the effort involved in collecting it. This article answers the most basic question: where does all the data you need (or might need) come from?

https://www.kdnuggets.com/2019/10/data-sources-101.html

Top Stories, Oct 21-27: Everything a Data Scientist Should Know About Data Management; How YouTube is Recommending Your Next Video

Also: Introduction to Natural Language Processing (NLP); Anomaly Detection, A Key Task for AI and Machine Learning, Explained; How to Become a (Good) Data Scientist — Beginner Guide

https://www.kdnuggets.com/2019/10/top-news-week-1021-1027.html

How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow

In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.

https://www.kdnuggets.com/2019/10/extend-scikit-learn-bring-sanity-machine-learning-workflow.html

About Google’s Self-Proclaimed Quantum Supremacy and its Impact on Artificial Intelligence

Google claimed quantum supremacy, IBM challenged it… but the development is really important for the future of AI.

https://www.kdnuggets.com/2019/10/googles-self-proclaimed-quantum-supremacy-impact-artificial-intelligence.html

Why is Machine Learning Deployment Hard?

Developing an excellent machine learning model is one thing. Deploying it to production is another. Consider these lessons learned and recommendations for approaching this important challenge to help ensure value from your AI work.

https://www.kdnuggets.com/2019/10/machine-learning-deployment-hard.html

Here are 8 Powerful Sessions to Learn the Latest Computer Vision Techniques

Do you want to build your own smart city? Picture it – self-driving cars strolling around, traffic lights optimised to maintain a smooth flow, ...
The post Here are 8 Powerful Sessions to Learn the Latest Computer Vision Techniques appeared first on Analytics Vidhya.

https://www.analyticsvidhya.com/blog/2019/10/8-latest-computer-vision-sessions-datahack-summit-2019/

KDnuggets™ News 19:n41, Oct 30: Feature Selection: Beyond feature importance?; Time Series Analysis Using KNIME and Spark

This week in KDnuggets: Feature Selection: Beyond feature importance?; Time Series Analysis: A Simple Example with KNIME and Spark; 5 Advanced Features of Pandas and How to Use Them; How to Measure Foot Traffic Using Data Analytics; Introduction to Natural Language Processing (NLP); and much, much more!

https://www.kdnuggets.com/2019/n41.html

5 Statistical Traps Data Scientists Should Avoid

Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid.

https://www.kdnuggets.com/2019/10/statistical-traps-data-scientists-avoid.html

Research Guide for Transformers

The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning.

https://www.kdnuggets.com/2019/10/research-guide-transformers.html

AutoML for Temporal Relational Data: A New Frontier

While AutoML started out as an automation approach to develop optimal machine learning pipelines, extensions of AutoML to Data Science embedded products can now enable the processing of much more, including temporal relational data.

https://www.kdnuggets.com/2019/10/automl-temporal-relational-data.html

Top KDnuggets tweets, Oct 23-29: End To End Guide For Machine Learning Project – Explained

Also: Highest paid positions in 2019 are DevOps, Data Scientist, Data Engineer (all over $100K) - Stack Overflow Salary Calculator, Updated; A neural net solves the three-body problem 100 million times faster; The Last SQL Guide for Data Analysis You’ll Ever Need; How YouTube is Recommending Your Next Video

https://www.kdnuggets.com/2019/10/top-tweets-oct23-29.html

Build Better and Accurate Clusters with Gaussian Mixture Models

Overview Gaussian Mixture Models are a powerful clustering algorithm Understand how Gaussian Mixture Models work and how to implement them in Python We’ll also ...
The post Build Better and Accurate Clusters with Gaussian Mixture Models appeared first on Analytics Vidhya.

https://www.analyticsvidhya.com/blog/2019/10/gaussian-mixture-models-clustering/

How to Build Your Own Logistic Regression Model in Python

A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer.

https://www.kdnuggets.com/2019/10/build-logistic-regression-model-python.html

How Data Labeling Facilitates AI Models

AI-based models are highly dependent on accurate, clean, well-labeled, and prepared data in order to produce the desired output and cognition. These models are fed with bulky datasets covering an array of probabilities and computations to make its functioning as smart and gifted as human intelligence.

https://www.kdnuggets.com/2019/10/data-labeling-facilitates-ai-models.html

How to Make an Agile Team Work for Big Data Analytics

Learn how to approach the challenges when merging an agile methodology into a data science team to bring out the best value your Big Data products.

https://www.kdnuggets.com/2019/10/agile-team-big-data-analytics.html

Which Data Science Skills are core and which are hot/emerging ones? – By Region and Employment

These results will go into each each region and employment type to find out the differences and similarities especially between people from Industry and Students.

https://www.kdnuggets.com/2019/10/core-hot-data-science-skills-continued.html