Description

Discover where to obtain and gather open-sourced datasets so that we may begin building a machine learning model
Gain knowledge of how crucial data preparation is to the machine learning process
Find out how to feed the preprocessed data into a machine learning algorithm and assess the model using metrics like accuracy score and confusion matrix
Learn how to measure a model's stability with respect to constantly changing training and test data by using an alternative splitting approach to the conventional one, the holdout method
Tune a model's parameters using a method that finds the optimal parameter values from the supplied grid of parameters in order to improve a model's performance
Understand the difference between label encoding and one hot encoding

Environment

You should create a virtualenv with the required dependencies by running

make virtualenv

source ./.env/bin/activate

git clone https://github.com/pedroandreou/My-AI-Tutorial.git