- 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
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