Join this session to embark on a comprehensive exploration of the fundamental concepts of data classification and regression. The presentation begins with an introduction to these two core techniques of predictive analytics, providing a clear understanding of their theoretical underpinnings and practical applications.

We then delve into the critical decision-making process of choosing between classification and regression for a given problem. This section illuminates the key factors that influence this choice, such as the nature of the target variable and the specific requirements of the analysis.

Next, we turn our attention to the crucial stage of data preprocessing. Here, we discuss various strategies and best practices for handling missing data, dealing with outliers, and transforming variables, ensuring that the data is in the optimal format for model training.

The presentation also introduces MLflow, a powerful open-source platform for managing the machine learning lifecycle. We demonstrate how to leverage MLflow to track model performance, maintain a record of experiments, and manage model deployment.

The presentation concludes with a hands-on demonstration of model training using popular machine learning libraries, providing attendees with the practical skills needed to embark on their own data science journey. This presentation serves as a comprehensive introduction to the art and science of model training, making it an invaluable resource for beginners in the field.

You will learn:

• About Classification and Regression: Learn the fundamental concepts of data classification and regression, their theoretical underpinnings, and practical applications. Understand the critical decision-making process of choosing between classification and regression for a given problem.
• About Master Data Preprocessing and MLflow: Learn various strategies and best practices for data preprocessing, including handling missing data, dealing with outliers, and transforming variables. Get introduced to MLflow, a powerful open-source platform for managing the machine learning lifecycle, and learn how to leverage it to track model performance, maintain a record of experiments, and manage model deployment.
• About Practice Model Training: Participate in a hands-on demonstration of model training using popular machine learning libraries. Gain practical skills needed to embark on your own data science journey. This session serves as a comprehensive introduction to the art and science of model training, making it an invaluable resource for beginners in the field.