 Top 10 Videos on Machine Learning in Finance
Talks, tutorials and playlists – you could not get a more gentle introduction to Machine Learning (ML) in Finance. Got a quick 4 minutes or ready to study for hours on end? These videos cover all skill levels and time constraints!
on Sep 29, 2017 in Credit Risk, Finance, Investment Portfolio, Machine Learning, Python, R, Stocks, Tutorials, Videolectures, Youtube
Tensorflow Tutorial, Part 2 – Getting Started
This tutorial will lay a solid foundation to your understanding of Tensorflow, the leading Deep Learning platform. The second part shows how to get started, install, and build a small test case.
on Sep 28, 2017 in Deep Learning, GPU, Python, TensorFlow
Meet Lucy: Creating a Chatbot Prototype
This article walks you through a step by step process and comes with starter code for building your own chatbot. In the end we also provide some pointers for folks looking to take this proof of concept to production stage.
on Sep 28, 2017 in Chatbot
 Introduction to Blockchains & What It Means to Big Data
Perhaps most significant development in IT over the past few years, blockchain has the potential to change the way that the world approaches big data, with enhanced security and data quality.
on Sep 27, 2017 in Big Data, Big Data Analytics, Bitcoin, Blockchain, Monetizing
Top 10 Active Big Data, Data Science, Machine Learning Influencers on LinkedIn, Updated
Looking for advice? Guidance? Stories? We’ve put a list of the top ten LinkedIn influencers of the last three months, follow them and stay up-to-date with the latest news in Big Data, Data Science, Analytics, Machine Learning and AI.
on Sep 26, 2017 in About Gregory Piatetsky, Bernard Marr, Big Data, Carla Gentry, Data Science, DJ Patil, Influencers, Kirk D. Borne, LinkedIn, Machine Learning, Tom Davenport, Trends
Visualizing High Dimensional Data In Augmented Reality
When Data Scientists first get a data set, they oftne use a matrix of 2D scatter plots to quickly see the contents and relationships between pairs of attributes. But for data with lots of attributes, such analysis does not scale.
on Sep 25, 2017 in Data Science, Data Visualization, IBM, Instacart, Machine Learning, R
What makes a data visualization successful?
Data visualisation gives very important insights about the data. But it is subjective to the goal of analysis & area of application. Let’s see how.
on Sep 25, 2017 in Brexit, Climate Change, Data Visualization, Hans Rosling, Healthcare
Python Data Preparation Case Files: Group-based Imputation
The second part in this series addresses group-based imputation for dealing with missing data values. Check out why finding group means can be a more formidable action than overall means, and see how to accomplish it in Python.
on Sep 25, 2017 in Data Preparation, Pandas, Python
Ensemble Learning to Improve Machine Learning Results
Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).
on Sep 22, 2017 in Ensemble Methods, Machine Learning, Statsbot
 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.
on Sep 22, 2017 in Cheat Sheet, Data Science, Deep Learning, Machine Learning, Neural Networks, Probability, Python, R, SQL, Statistics
A Solution to Missing Data: Imputation Using R
Handling missing values is one of the worst nightmares a data analyst dreams of. In situations, a wise analyst ‘imputes’ the missing values instead of dropping them from the data.
on Sep 21, 2017 in Data Preparation, Missing Values, R
How To Lie With Numbers
It takes less effort to lie without numbers, but there are now more numbers and more ways to lie with them than ever before. Poor Reverend Bayes, who understood the true meaning of "evidence".
on Sep 21, 2017 in Quantitative Analytics, Statistics
5 Ways to Get Started with Reinforcement Learning
We give an accessible overview of reinforcement learning, including Deep Q Learning, and provide useful links for implementing RL.
on Sep 20, 2017 in Deep Learning, Machine Learning, Neural Networks, Reinforcement Learning
Big Data Architecture: A Complete and Detailed Overview
Data scientists may not be as educated or experienced in computer science, programming concepts, devops, site reliability engineering, non-functional requirements, software solution infrastructure, or general software architecture as compared to well-trained or experienced software architects and engineers.
on Sep 19, 2017 in Analytics, Big Data, Big Data Architecture, Cloud, Cloud Computing, Scalability, Software, Software Engineering
Evaluating Data Science Projects: A Case Study Critique
It’s not necessary to understand the inner workings of a machine learning project, but you should understand whether the right things have been measured and whether the results are suited to the business problem. You need to know whether to believe what data scientists are telling you.
on Sep 19, 2017 in Data Science, SVDS, Tom Fawcett
How To Become a 10x Data Scientist, part 1
A 10x developer is someone who is 10 times more productive than average. We adapt tips and tricks from the developer community to help you become a more proficient data scientist loved by team members and stakeholders.
on Sep 18, 2017 in Advice, Algorithmia, Data Science Team, Data Scientist, Data-Driven Business, Programming
Keras Tutorial: Recognizing Tic-Tac-Toe Winners with Neural Networks
In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Introductory neural network concerns are covered.
on Sep 18, 2017 in Games, Keras, Neural Networks, Python
Cartoon: What Else Can AI Guess From Your Face?
Recent story about AI able to guess if a person is gay or straight from their photo led us to think what else can AI guess from your face.
on Sep 16, 2017 in AI, Cartoon, Face Recognition, Food, Humor
Data Science and the Imposter Syndrome
You are not the only one who wonders how much longer they can get away with pretending to be a data scientist. You are not the only one who has nightmares about being laughed out of your next interview.
on Sep 15, 2017 in Bias, Data Science, Data Scientist
Python Data Preparation Case Files: Removing Instances & Basic Imputation
This is the first of 3 posts to cover imputing missing values in Python using Pandas. The slowest-moving of the series (out of necessity), this first installment lays out the task and data at the risk of boring you. The next 2 posts cover group- and regression-based imputation.
on Sep 14, 2017 in Data Preparation, Pandas, Python
New-Age Machine Learning Algorithms in Retail Lending
We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.
on Sep 13, 2017 in Credit Risk, Customer Analytics, Deep Learning, Fintech, Machine Learning, Recurrent Neural Networks
Neural Network Foundations, Explained: Activation Function
This is a very basic overview of activation functions in neural networks, intended to provide a very high level overview which can be read in a couple of minutes. This won't make you an expert, but it will give you a starting point toward actual understanding.
on Sep 13, 2017 in Explained, Neural Networks
K-Nearest Neighbors – the Laziest Machine Learning Technique
K-Nearest Neighbors (K-NN) is one of the simplest machine learning algorithms. When a new situation occurs, it scans through all past experiences and looks up the k closest experiences. Those experiences (or: data points) are what we call the k nearest neighbors.
on Sep 12, 2017 in Algorithms, K-nearest neighbors, Machine Learning, RapidMiner
Videos for Business Analytics using Data Mining course
Here we present links to very useful videos on Business Analytics using data mining courses.
on Sep 12, 2017 in Business Analytics, Data Mining, Galit Shmueli, Online Education, R, Youtube
Python vs R – Who Is Really Ahead in Data Science, Machine Learning?
We examine Google Trends, job trends, and more and note that while Python has only a small advantage among current Data Science and Machine Learning related jobs, this advantage is likely to increase in the future.
on Sep 12, 2017 in Data Science, Google Trends, Jobs, Kaggle, Machine Learning, Python, Python vs R, R
Top 10 Machine Learning Use Cases: Part 2
This post is the second in a series whose aim is to shake up our intuitions about what machine learning is making possible in specific sectors — to look beyond the set of use cases that always come to mind.
on Sep 11, 2017 in Healthcare, IBM, Machine Learning, Use Cases
The new Enigma Public – the platform connecting people to data
Public data has tremendous potential and different people can use it to solve variety of problems. Enigma relaunches Enigma Public — the platform connecting people to data.
on Sep 11, 2017 in Datasets, Government, Healthcare, Social Good
Asimov’s 4th Law of Robotics
It seems Isaac Asimov didn’t envision needing a law to govern robots in these sorts of life-and-death situations where it isn’t the life of the robot versus the life of a human in debate, but it’s a choice between the lives of multiple humans!
on Sep 8, 2017 in AI, Ethics, Kids, Robots, Self-Driving Car
I built a chatbot in 2 hours and this is what I learned
I set out to test two things: 1) building a bot is useless from a business perspective and 2) building bots is crazy tough. Here is what I learned.
on Sep 7, 2017 in AI, Chatbot, Hype, NLP
How Booking.com’s data scientist uses predictive analytics – PAW interview
Lukas Vermeer will speak at the upcoming Predictive Analytics World for Business, 11-12 October in London. His Keynote will focus on new perspectives on the Data Science challenges we face today.
on Sep 6, 2017 in Booking.com, PAW, Predictive Analytics, Predictive Analytics World
Are Data Lakes Fake News?
The quick answer is yes, and the biggest problem is that the term “Data Lakes” has been overloaded by vendors and analysts with different meanings, resulting in an ill-defined and blurry concept.
on Sep 6, 2017 in Data Lakes, Data Warehouse, ETL, Fake News, Hadoop
Putting the “Science” Back in Data Science
The scientific method to approach a problem, in my point of view, is the best way to tackle a problem and offer the best solution. If you start your data analysis by simply stating hypotheses and applying Machine Learning algorithms, this is the wrong way.
on Sep 6, 2017 in Business, Data Science, Machine Learning, Process, Rubens Zimbres
Closing the Insights-to-Action Gap
There are many types of analytics for getting insight out of data, but the bigger and more difficult challenge is turning that insight into action. What should we do differently based on your findings?
on Sep 5, 2017 in Analytics, Gartner, Optimization, Skills
Visualizing Cross-validation Code
Cross-validation helps to improve your prediction using the K-Fold strategy. What is K-Fold you asked? Check out this post for a visualized explanation.
on Sep 5, 2017 in Cross-validation, Machine Learning, Python, scikit-learn
Detecting Facial Features Using Deep Learning
A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now “magically” solved by deep learning and any talented teenager can do it in a few hours.
on Sep 4, 2017 in Convolutional Neural Networks, Deep Learning, Image Recognition, Neural Networks
Cartoon: Future Machine Learning Class
New KDnuggets Cartoon looks at an unusual but possible future Machine Learning Class.
on Sep 2, 2017 in Cartoon, Data Cleaning, Machine Learning
Search Millions of Documents for Thousands of Keywords in a Flash
We present a python library called FlashText that can search or replace keywords / synonyms in documents in O(n) – linear time.
on Sep 1, 2017 in Algorithms, Data Science, GitHub, NLP, Python, Search, Search Engine, Text Mining
277 Data Science Key Terms, Explained
This is a collection of 277 data science key terms, explained with a no-nonsense, concise approach. Read on to find terminology related to Big Data, machine learning, natural language processing, descriptive statistics, and much more.
on Sep 1, 2017 in Data Science, Explained, Key Terms
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