AI NOTES

AI Notes | Regression Metrics

In my last notebook we looked at a classification problem, and we defined many classification metrics. In this notebook, we will go through some regression metrics. Recall that in regression, the response value is continuous (and not categorical), as such different kind of prediction assessment will come into play ...
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AI Notes | Classification Metrics

Now, say, you have built a machine learning model; the question you ask is: 'how well does this thing works anyways?'. To answer this question, we will need to define the performance metrics. As you might have imagined, the metrics will depend on the kind of machine learning problem in ...
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AI Notes | Resampling Methods

These are the methods involved in sampling during machine learning. In my last notebook-blog, I hinted the idea of an analogy between a 12-year-old girl studying for an exam, and our machine trying to learn ...
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AI Notes | Machine Learning Overview

In our ML blog-syllabus, mathematical foundations of ML should be the next stop, however I have decided to postpone this till later in the blog in order to write something more comprehensive. The reader should note that getting the maths 'out of the way' is very essential to deeply understand a lot ...
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AI Notes | Regression Metrics

AI Notes | Classification Metrics

AI Notes | Resampling Methods

AI Notes | Machine Learning Overview

AI Notes | A Brief Note on Statistics

AI Notes | My Reading Data Visualization with Seaborn

AI Notes | Data Handling with Pandas

AI Notes | Introduction to Numerical Python (NumPy)

AI Notes | Jupyter Notebook

AI Notes | Machine Learning Outline