Course Description
Indicates a continuation of foundational Machine Learning (ML) knowledge, focusing on advanced algorithms like gradient boosting and neural networks, deeper dives into supervised and unsupervised learning, model evaluation, and the application of these techniques to real-world problems. These courses often cover feature engineering, dimensionality reduction (like PCA), clustering, and explore concepts like the bias-variance tradeoff and ethical considerations in ML.
Explore algorithms, including the random forest algorithm, the gradient boosting algorithm, and the support vector machine algorithm as well as clustering algorithms (including k-means clustering and hierarchical clustering). Also consider neural networks, dimensionality reduction techniques (including principal component analysis), and more. Through practical projects and hands-on exercises, learn how to solve complex problems in diverse domains through the implementation and application of these advanced ML algorithms.