Machine Learning Foundations for Data Science

Machine learning is the engine behind modern innovation, yet getting started can feel overwhelming without a clear roadmap. This course simplifies complex algorithms into readable, actionable steps for anyone looking to enter the field of data science. You will progress from basic terminology to deploying functional models, gaining the confidence to analyze datasets and extract meaningful insights. By focusing on the logic behind the math and the structure of the data, you will understand how machines learn from patterns to make accurate predictions in real-world scenarios. What you'll learn: - Understand the essential seven-step machine learning workflow from data collection to prediction. - Apply supervised learning techniques including linear regression and classification models. - Master model evaluation metrics such as Pearson’s coefficient and the coefficient of determination. - Explore unsupervised learning concepts and the logic behind recommendation systems. - Practice data preprocessing and feature engineering to improve model accuracy. - Understand modern deployment strategies for moving models into cloud environments. - Learn to explain model outputs using basic prompt engineering and interpretability techniques. The course begins with foundational definitions and core terminology before guiding you through the practical application of algorithms using Jupyter Notebook environments. You will learn to navigate common data challenges and prepare datasets for rigorous analysis. This course is designed specifically for beginners with no prior experience in data science. All concepts are explained through clear written text and code examples. Begin building your foundation in machine learning today.