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 Object-oriented programming for data scientists: Build your ML estimator
Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.
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What is Benford’s Law and why is it important for data science?
Benford’s law is a little-known gem for data analytics. Learn about how this can be used for anomaly or fraud detection in scientific or technical publications.
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How a simple mix of object-oriented programming can sharpen your deep learning prototype
By mixing simple concepts of object-oriented programming, like functionalization and class inheritance, you can add immense value to a deep learning prototyping code.
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How do you check the quality of your regression model in Python?
Linear regression is rooted strongly in the field of statistical learning and therefore the model must be checked for the ‘goodness of fit’. This article shows you the essential steps of this task in a Python ecosystem.
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Optimization with Python: How to make the most amount of money with the least amount of risk?
Learn how to apply Python data science libraries to develop a simple optimization problem based on a Nobel-prize winning economic theory for maximizing investment profits while minimizing risk.
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Linear Programming and Discrete Optimization with Python using PuLP
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.
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What are Some “Advanced” AI and Machine Learning Online Courses?
Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?
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Synthetic Data Generation: A must-have skill for new data scientists
A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.
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When Bayes, Ockham, and Shannon come together to define machine learning
A beautiful idea, which binds together concepts from statistics, information theory, and philosophy.
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Why You Should Start Using .npy Files More Often
In this article, we demonstrate the utility of using native NumPy file format .npy over CSV for reading large numerical data set. It may be an useful trick if the same CSV data file needs to be read many times.
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Object-oriented programming for data scientists: Build your ML estimator
What is Benford’s Law and why is it important for data science?