Problems of high dimension Many ML algorithm relies on distances to compute similarity between samples Curse of dimensionality – as number of dimension increases, distances
What is hyper-parameter tuning? Since variables of the model that cannot be learned by the learning algorithm (via gradient descent) still need to be optimized
There are two broad kind of ML problems: supervised and unsupervised learning. Supervised Learning – each record has both feature and label (X and Y)
We discuss a few ways of doing feature selection in ML. Unsupervised method Check the linear correlation among the features (without the label) Remove features
Explain the issues of Data Skew and Concept Drift in Production ML
The GameStop (GME) short squeeze in Jan 2021 made headline when it went up nearly 30 times from $17.25 to $500. You might be wondering
These are the top 6 best seller machine learning books on Amazon, but are they worth it? Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow:
If you want to get serious in learning machine learning, these books are the classics that cannot go wrong. Pattern Recognition and Machine Learning by
Git is one of the most popular Version Control System (VCS). You might have encountered Git command cheat sheets and tutorials on how to checkout
I want to give a quick overview of the K-Nearest Neighbor (KNN) model for beginners into machine learning. What is KNN? It’s easy to describe