LEARN SUPERVISED ALGORITHMS: Master Linear Regression, Logistic Regression, and Classification in Data Science

This book is ideal for professionals, students, and specialists seeking technical mastery in supervised machine learning with a focus on direct application, automation, and integration with global platforms. Learn to implement algorithms such as linear regression, logistic regression, and classification using established frameworks like Scikit-learn, TensorFlow, PyTorch, and native integration with AWS, Azure, Google Cloud, Databricks, Jupyter, BigQuery, and S3 environments. The content covers everything from data preparation and feature engineering, automatic feature selection, and hyperparameter tuning to automated deployment and model monitoring in corporate CI/CD pipelines. Includes:

• Modeling with Scikit-learn, TensorFlow, PyTorch, and Databricks integration

• Workflow integration with AWS, Azure, Google Cloud, BigQuery, S3

• Creation and automation of classification and regression pipelines

• Cross-validation, hyperparameter tuning, and advanced evaluation

• Model deployment and monitoring with MLOps and Airflow

• Scalable feature engineering and data preparation

• Practical examples with Jupyter, collaborative notebooks, and APIs

By the end, you will have the expertise to execute complete supervised machine learning projects, integrating market-leading tools and delivering professional, scalable solutions ready for cloud environments.

Content reviewed by A.I. with technical supervision.

scikit-learn, tensorflow, pytorch, aws, azure, google cloud, databricks, bigquery, s3, airflow, mlops, pipelines, classification, regression, automation, feature engineering, notebooks, deploy