#python

Learned about Confidence Intervals and Margin of Error

Learned about about Sampling, Law of Large Numbers and Central Limit Theorem

Learned about expected value, variance, standard deviation, skewness and kurtosis

Learned about use or probability and statistics in ML

Learned how to use calculus for back propagation in training a neural network with simple layers(input and output, no hidden layer) and multiple perceptron for both linear regression and classification problems

Learned about use of Calculus in ML

Learned about Variance and Covariance and how they related to spread in a dataset

Learned about Variance and Covariance and how they related to spread in a dataset

Learned about Vectors and Linear Transformations

Learning about Mathplotlib for data visualization

Learned a whole bunch of things(most things I've done before), learn about solving systems of linear equations, rank of a matrix, echelon form, diagonal matrix and Gaussian elimination method

Build something for the Zama builder bounty

Learned about python basics such as variables, lists and loops

PyTorch is the dominant framework in research and modern ML — TensorFlow has lost the research community