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

How to set up a virtual environment

You should create a virtualenv with the required dependencies by running

make virtualenv

How to activate the virtual environment:

source ./.env/bin/activate

🛠 Initialization & Setup

Clone the repository

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

Author