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How to Make Your Machine Learning Models Robust to Outliers
In this blog, we’ll try to understand the different interpretations of this “distant” notion. We will also look into the outlier detection and treatment techniques while seeing their impact on different types of machine learning models.
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Are Vectorized Random Number Generators Actually Useful?
I reported that you can multiply the speed of common (fast) random number generators such as PCG and xorshift128+ by a factor of three or four by vectorizing them using SIMD instructions. Is this actually useful in practice?
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Multi-Class Text Classification with Scikit-Learn
The vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering and sentiment analysis. Real world problem are much more complicated than that.
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 Comparison of the Most Useful Text Processing APIs
There is a need to compare different APIs to understand key pros and cons they have and when it is better to use one API instead of the other. Let us proceed with the comparison.
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UX Design Guide for Data Scientists and AI Products
Realizing that there is a legitimate knowledge gap between UX Designers and Data Scientists, I have decided to attempt addressing the needs from the Data Scientist’s perspective.
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Basic Statistics in Python: Probability
At the most basic level, probability seeks to answer the question, "What is the chance of an event happening?" To calculate the chance of an event happening, we also need to consider all the other events that can occur.
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Why Automated Feature Engineering Will Change the Way You Do Machine Learning
Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage.
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Reinforcement Learning: The Business Use Case, Part 2
In this post, I will explore the implementation of reinforcement learning in trading. The Financial industry has been exploring the applications of Artificial Intelligence and Machine Learning for their use-cases, but the monetary risk has prompted reluctance.
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Unveiling Mathematics Behind XGBoost
Follow me till the end, and I assure you will atleast get a sense of what is happening underneath the revolutionary machine learning model.
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Building Reliable Machine Learning Models with Cross-validation
Cross-validation is frequently used to train, measure and finally select a machine learning model for a given dataset because it helps assess how the results of a model will generalize to an independent data set in practice.
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Comparison of the Most Useful Text Processing APIs