-
What makes a winning entry in a Machine Learning competition?
So you want to show your grit in a Kaggle-style competition? Many, many others have the same idea, including domain experts and non-experts, and academic and corporate teams. What does it take for your bright ideas and skills to come out on top of thousands of competitors?
-
  Charticulator: Microsoft Research open-sourced a game-changing Data Visualization platform
Creating grand charts and graphs from your data analysis is supported by many powerful tools. However, how to make these visualizations meaningful can remain a mystery. To address this challenge, Microsoft Research has quietly open-sourced a game-changing visualization platform.
-
FluDemic – using AI and Machine Learning to get ahead of disease
We are amidst a healthcare data explosion. AI/ML will be more vital than ever in the prevention and handling of future pandemics. Here, we walk you through the different facets of modeling infectious diseases, focusing on influenza and COVID-19.
-
Best Podcasts for Machine Learning
Podcasts, especially those featuring interviews, are great for learning about the subfields and tools of AI, as well as the rock stars and superheroes of the AI world. Here, we highlight some of the best podcasts today that are perfect for both those learning about machine learning and seasoned practitioners.
-
Improving model performance through human participation
Certain industries, such as medicine and finance, are sensitive to false positives. Using human input in the model inference loop can increase the final precision and recall. Here, we describe how to incorporate human feedback at inference time, so that Machines + Humans = Higher Precision & Recall.
-
How to ace A/B Testing Data Science Interviews
Understanding the process of A/B testing and knowing how to discuss this approach during data science job interviews can give you a leg up over other candidates. This mock interview provides a step-by-step guide through how to demonstrate your mastery of the key concepts and logical considerations.
-
Data Science 101: Normalization, Standardization, and Regularization
Normalization, standardization, and regularization all sound similar. However, each plays a unique role in your data preparation and model building process, so you must know when and how to use these important procedures.
-
What makes a song popular? Analyzing Top Songs on Spotify
With so many great (and not-so-great) songs out there, it can be hard to find those that match your musical preferences. Follow along this ML model building project to explore the extensive song data available on Spotify and design a recommendation engine that could help you discover your next favorite artist!
-
Continuous Training for Machine Learning – a Framework for a Successful Strategy
A basic appreciation by anyone who builds machine learning models is that the model is not useful without useful data. This doesn't change after a model is deployed to production. Effectively monitoring and retraining models with updated data is key to maintaining valuable ML solutions, and can be accomplished with effective approaches to production-level continuous training that is guided by the data.
-
7 Must-Haves in your Data Science CV
If you are looking for a new role as a Data Scientist -- either as a first job fresh out of school, a career change, or a shift to another organization -- then check off as many of these critical points as possible to stand out in the crowd and pass the hiring manager's initial CV screen.
|
Charticulator: Microsoft Research open-sourced a game-changing Data Visualization platform
How to ace A/B Testing Data Science Interviews