Gradient Descent
In this topic you will get the mathematical explanation of gradient descent.
http://mccormickml.com/2014/03/04/gradient-descent-derivation/
Bias Variance Tradeoff
Supervised machine learning algorithms can best be understood through the lens of the bias-variance trade-off. In this post, you will discover the Bias-Variance Trade-Off and how to use it to better understand machine learning algorithms and get better performance on your data.
Ridge and Lasso Regression
In this topic you will get a comprehensive beginners guide for Linear, Ridge and Lasso Regression in Python.
Cross validation
In this topic you will get a overview of how to split data using cross validation technique.
https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6
Hyper parameter tuning
Machine learning models are parameterized so that their behavior can be tuned for a given problem. Models can have many parameters and finding the best combination of parameters can be treated as a search problem. In this topic, you will discover how to tune the parameters of machine learning algorithms in Python using the scikit-learn library. https://machinelearningmastery.com/how-to-tune-algorithm-parameters-with-scikit-learn/