Machine Learning: Intro to Regression, Decision Trees, SVM, and More

Dive into the world of machine learning with this beginner-friendly workbook! Learn the fundamentals of ML, explore different learning types (supervised, unsupervised, and reinforcement), and understand the process of building and deploying models. Study popular algorithms like linear regression, decision trees, and support vector machines, while also getting hands-on with scikit-learn. The workbook covers practical applications, feature engineering, hyperparameter tuning, and how to tackle overfitting and underfitting. Includes a Jupyter Notebook file with detailed instructions to guide your learning every step of the way.