This course offers a hands-on introduction to machine learning, encompassing widely used models, algorithms, and tools. It delves into supervised learning techniques like linear regression, logistic regression, and neural networks, as well as unsupervised learning methods including K-means clustering, principal component analysis, and association rule learning. Additionally, the course addresses crucial practical considerations in machine learning implementation such as data visualization, model selection and workflow, evaluation techniques (including testing, validation, and addressing overfitting and underfitting), bias and variance, regularization, and strategies for large-scale machine learning applications.