Software Engineering for Data Scientist — Test-Driven Development. Our primary focus is to discuss how to expand the horizon of testing in DS/ML projects beyond cross-validation and test data.
Test-Driven Development (TDD) is very popular in Software Engineering practice. When it comes to Data Science/Machine Learning projects, it shrinks to validation and cross-validation. In the best-case scenario, we will be talking about A/B testing the model. ML models are becoming part of leger IT system components in the present scenario. This is high time for enterprises to think about TDD in Data Science and Machine Learning projects.
TDD is a software Engineering practice that requires a unit test to be written before the code is supposed to be validated. We are not going to discuss the what and how of test-driven development in Software Engineering. There are plenty of resources available on this topic. Our primary focus is to discuss how to expand the horizon of testing in DS/ML projects beyond cross-validation and test data.
In this article, we explore gradient descent - the grandfather of all optimization techniques and it’s variations. We implement them from scratch with Python.
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Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.
Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.