If you are looking to transition your career to data science, don't immediately start learning Python or R. Instead, leverage the domain expertise you have accumulated over the years. Here's a foolproof guide on how to do that.
Design of Experiments (DOE) is a statistical concept used to find the cause-and-effect relationships. Surprisingly, an experiment arising from a casual conversation about tea-drinking is one of the first examples of an experiment designed using statistical ideas.
Backpropagation is one of those topics that seem to confuse many once you move past feed-forward neural networks and progress to convolutional and recurrent neural networks. This article gives you and overall process to understanding back propagation by giving you the underlying principles of backpropagation.
Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.
The training of machine learning models is often compared to winning the lottery by buying every possible ticket. But if we know how winning the lottery looks like, couldn’t we be smarter about selecting the tickets?
Animations make even more sense when depicting time series data like stock prices over the years, climate change over the past decade, seasonalities and trends since we can then see how a particular parameter behaves with time.
This webinar for professional data scientists will go over how to monitor models when in production, and how to set up automatically adaptive machine learning.
We explain why AI needs to understand business processes and how the business processes need to be able to change to bring insight from AI into the process.
Models are useful because they allow us to generalize from one situation to another. When we use a model, we’re working under the assumption that there is some underlying pattern we want to measure, but it has some error on top of it.
AI is all the rage with today’s programmers, but what about the next generation? Machine learning can be introduced to young ones just now learning about code, and you can help spark their interest.
We all are aware of the issue of overfitting, which is essentially where the model you build replicates the training data results so perfectly its fitted to the training data and does not generalise to better represent the population the data comes to, with catastrophic results when you feed in new data and get very odd results.
Social media has been gold for studying the way people communicate and behave, in this article I’ll show you the easiest way of analyzing tweets without the Twitter API and scalable for Big Data.
This extensive post covers NLP use cases, basic examples, Tokenization, Stop Words Removal, Stemming, Lemmatization, Topic Modeling, the future of NLP, and more.
Video is a natural way for us to understand three dimensional and time varying information. Read this short post on how to achieve the creation of videos from still images.
Passably-human automated text generation is a reality. How do we best go about detecting it? As it turns out, being too predictably human may actually be a reasonably good indicator of not being human at all.
Here are six sectors that are realizing how beneficial predictive analytics could be, embracing the possibilities of valuable insights extracted from such technology.
How to integrate physics-based models (these are math-based methods that explain the world around us) into machine learning models to reduce its computational complexity.
This content is part of a series about the chapter 3 on probability from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016). It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as my understanding of these concepts.
How can we build a computer vision model using CNNs? What are existing datasets? And what are approaches to train the model? This article provides an answer to these essential questions when trying to understand the most important concepts of computer vision.
Dr. Takeo Kanade shared his life lessons from an illustrious 50-year career in Computer Vision at last year's Embedded Vision Summit. You have a chance to attend the 2019 Embedded Vision Summit, from May 20-23, in the Santa Clara Convention Center, Santa Clara CA.
This article is a discussion of some of PyCharm's features, and a comparison with Spyder, an another popular IDE for Python. Read on to find the benefits and drawbacks of PyCharm, and an outline of when to prefer it to Spyder and vice versa.
Follow these updated 7 steps to go from SQL data science newbie to practitioner in a hurry. We consider only the necessary concepts and skills, and provide quality resources for each.
Clustering - including K-means clustering - is an unsupervised learning technique used for data classification. We provide several examples to help further explain how it works.
Microsoft has provided a GitHub repository with Python best practice examples to facilitate the building and evaluation of recommendation systems using Azure Machine Learning services.
We reach out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn prediction using Machine Learning.
Machine Learning has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments.
We outline some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.
Before you get too excited and sign the papers for that new data scientist job, and solidify your role as a new hire, make sure you look over these 5 things first.
Visually representing the content of a text document is one of the most important tasks in the field of text mining as a Data Scientist or NLP specialist. However, there are some gaps between visualizing unstructured (text) data and structured data.
This guide from ActiveState provides an executive overview of how you can implement Python for your team’s data science and machine learning initiatives.
Knowledge of such optimization techniques is extremely useful for data scientists and machine learning (ML) practitioners as discrete and continuous optimization lie at the heart of modern ML and AI systems as well as data-driven business analytics processes.
Vote in KDnuggets 20th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will publish the anon data, results, and trends here.
In the final part of this series, we provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across the US and Canada.
Data science and decision science are related but still separate fields, so at some points, it might be hard to compare them directly. We attempted to show our vision of the commonalities, differences, and specific features of data science and decision science.
Data science or whatever you want to call it is not just knowing some programming languages, math, statistics and have “domain knowledge” and here I show you why.
In recent years, XGBoost algorithm has gained enormous popularity in academic as well as business world. We outline some of the reasons behind this incredible success.
We are going to implement regularization techniques for linear regression of house pricing data. Our goal in price modeling is to model the pattern and ignore the noise.
In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity. TensorFlow was the champion of deep learning frameworks and PyTorch was the youngest framework. How has the landscape changed?
A Step-by-Step Guide to Transitioning your Career to Data Science – Part 1
60+ useful graph visualization libraries
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