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Photo by Christopher Burns on Unsplash

One of the biggest challenges I have had during my self-study of machine learning is trying to run before I could walk. Towards the end of 2019, I read the book AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee, which catapulted me into the field of data science. With much enthusiasm, I started a hands-on machine learning course from Udemy. After about one month’s time, I was equipped with knowledge of many modern machine learning models and eagerly wanted to start numerous personal side-projects with this exciting technology. …


Failure is part of the learning process. Unfortunately, it frequents being part of the machine learning development process far too often. ML projects can be doomed from conception due to a misalignment between product metrics and model metrics. Today, many skilled individuals can create highly accurate models, and low modeling capabilities are not a common pitfall. Instead, there is a tendency for accurate models to be developed that are not useful for a product, thus failing to meet business objectives.

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Image by Isaac Smith on Unsplash

Defining and Quantifying Success

In defining success, it is crucial to consider the differences between business performance and model performance. The most straightforward way…


Business Performance vs Model Performance

Failure is part of the learning process. Unfortunately, it frequents being part of the machine learning development process far too often. ML projects can be doomed from conception due to a misalignment between product metrics and model metrics. Today, there are many skilled individuals who can create highly accurate models, and poor modelling capabilities is not a common pitfall. Rather, there is a tendency for accurate models to be developed that are not useful for a product, thus failing to meet business objectives.

Image for post
Image for post
Image by Isaac Smith on Unsplash

Defining and Quantifying Success

In defining success, it is important to consider the differences between business performance and model performance. The…


Why should you care about algorithms and data structures as a data scientist?

One of the biggest challenges I have had during my self study of machine learning is trying to run before I could walk. Towards the end of 2019 I read the book AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee, which catapulted me into the field of data science. With much enthusiasm, I started a hands-on machine learning course from Udemy. After about one months time, I was equipped with knowledge of many modern machine learning models, and eagerly wanted to start numerous personal side-projects with this exciting technology. …


Prior to COVID-19 turning the whole world upside down, I was eagerly developing my very first neural network project — a model that could take APPL stock data as features and predict short-term price fluctuations. The idea was simple; buy low, sell high.

After reviewing my test dataset results, I excitedly realized that this model could work. My enthusiasm was short lived however, as I was a senior chemical engineering major who had not practiced programming since my first year at university. …

Wilhem Kornhauser

AI Enthusiast | Python Programmer | Engineering Graduate | Connect on LinkedIn! https://www.linkedin.com/in/wilhemkornhauser/

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