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!
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.
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.
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.
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.
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.
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.
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).
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.
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.
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".
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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!
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.
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.
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.
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?
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.
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.
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.