Introduction to model ensembling

Advanced machine learning, in this course we will learn about advanced machine learning techniques and algorithms that go beyond the basics. You'll explore ensemble methods like Random Forest, Gradient Boosting, and XGBoost, as well as techniques for feature selection, dimensionality reduction, and hyperparameter optimization. The course covers deep learning fundamentals, including neural networks, activation functions, and backpropagation. You’ll also dive into unsupervised learning techniques like DBSCAN and hierarchical clustering, and advanced topics like anomaly detection, time series forecasting, and model interpretability using SHAP and LIME. We will use Python libraries such as scikit-learn, TensorFlow, and Keras for hands-on implementation. Real-world projects and datasets will help you apply what you learn in practical scenarios. By the end of this course, you'll be equipped to build robust, efficient, and scalable machine learning solutions. Prior experience with basic ML concepts and Python is recommended. Data School