This nearly 4-hour beginner-friendly video course, developed by Kylie Ying and hosted by freeCodeCamp, introduces the fundamentals of machine learning in a way accessible to all. The course explains key concepts including supervised and unsupervised learning, essential data handling techniques, feature engineering, and model evaluation. Learners explore core algorithms such as k-nearest neighbors, naive Bayes, logistic regression, support vector machines, neural networks (with TensorFlow), linear regression, and unsupervised models such as K-means clustering and principal component analysis. Kylie demonstrates hands-on model building in Google Colab using real datasets (MAGIC Gamma Telescope, Seoul Bike Sharing Demand, seeds/wheat), guides students through coding each step in Python, and covers important workflow practices such as splitting data into training, validation, and testing sets, scaling features, and tackling class imbalance. Real-world exercises and downloadable code notebooks help reinforce learning and provide a practical, community-driven approach to machine learning problem solving. The course is ideal for absolute beginners, with step-by-step explanations and supporting materials for deeper experimentation.
