Machine learning is transforming businesses around the world. It’s already a $7.3 billion market and is expected to continue its explosive growth to $30.6 billion by 2024. Machine learning is enabling Al to become smarter and more intelligent. Autonomous vehicles, facial recognition, autonomous drones, healthcare applications, medical imaging, and military…

Synthetic Minority Oversampling Technique Synthetic data can be particularly useful in cases where there are too few examples of the minority class for a model to effectively learn the decision boundary. One way to solve this problem is to oversample the minority class, and simply duplicate examples of it in the training dataset. While…

Computer vision (CV) has made incredible strides in accuracy and applicability. Object classification, in many applications, can achieve 99% accuracy; this is up from 50% a decade ago. CV also can extract complex features in images to identify signs of disease in patients. Object classification, in many applications, can achieve…

It’s October 2020 and, as the seasons change, the world is waiting to see whether recent upticks in cases of COVID-19 in many communities will signal a new phase in the pandemic’s expansion or a trend that can be controlled and reversed. Meanwhile, data scientists have been working with disease…

“It is good to have an end to journey toward; but it is the journey that matters, in the end.” - Ursula K. Le Guin, Influential Science Fiction Author Rigorous data cleaning is essential for a successful, on-schedule machine learning project. Some may consider data cleaning to be a single…

Let’s face it, most data scientists dread annotation. That’s the process of manually labelling items in supervised machine learning training examples so that ground truth is established for training the ML model. The challenges of defining data annotation specifications stem from the nature, not the complexity of task. …

What are the odds that your ML project will reach production? One claim puts the chances at 13%, or a little better than one in ten. At VentureBeat’s “VB Transform” summit, thought leaders from IBM and Gap posed the three greatest obstacles to successful deployment: Investing in AI and big…

Arguably, the most important challenge facing machine learning today involves both data science and social equity: reducing bias in ML data, models and practices. Machine learning is playing a rapidly expanding role in industries ranging from lending to insurance, healthcare, recruiting and more. As mathematician and speaker Cathy O’Neill points…

We’re nearing the finish line! We’ve collected data, meticulously and thoughtfully prepared it, gone through the process of feature engineering as needed, and are ready to choose, train, and deploy a model. When designing a Machine Learning solution for a real-world problem, it’s important to remember that the goal is…