ML Basics

• Calculus: Limits, continuity and differentiability. Maxima and minima. Mean value theorem. Integration.
• Linear Algebra: Matrices, determinants, system of linear equations, eigenvalues and eigenvectors, LU decomposition
• Probability: Random variables. Uniform, normal, exponential, Poisson and binomial distributions. Mean, median, mode and standard deviation. Conditional probability and Bayes theorem.

CSP(Constraint satisfaction Problem): Finding a solution that meets constraints

 https://iisc.talentsprint.com/cds/main.html#curriculum

fit function trains the model, taking features (aka columns) and part of the data which is intended to be trained.

https://www.sharpsightlabs.com/blog/sklearn-fit/

“house price” is the column we’ve to predict so we take that column as y and the rest of the columns as our X variable. test_size = 0.05 specifies only 5% of the whole data is taken as our test set, and 95% as our train set. 

https://www.geeksforgeeks.org/how-to-split-a-dataset-into-train-and-test-sets-using-python/