Explore SVMs, ensemble methods, dimensionality reduction, clustering, and complete ML pipelines.
1.Support Vector Machines
Learn SVM - a powerful algorithm that finds the optimal boundary between classes.
2.SVM Kernel Tricks
Learn how kernels allow SVMs to handle non-linear decision boundaries.
3.Gradient Boosting
Learn Gradient Boosting - a powerful ensemble that builds trees sequentially to correct errors.
4.Voting Classifiers
Learn to combine multiple different models using voting for better predictions.
5.PCA Dimensionality Reduction
Learn Principal Component Analysis for reducing the number of features while preserving variance.
6.K-Means Clustering
Learn K-Means clustering - an unsupervised algorithm for grouping similar data points.
7.DBSCAN Clustering
Learn DBSCAN - a density-based clustering algorithm that can find arbitrarily shaped clusters.
8.Feature Selection Techniques
Learn various feature selection methods to improve model performance and reduce complexity.
9.Handling Imbalanced Data
Learn techniques for handling datasets with unequal class distributions.
10.Complete ML Pipeline Project
Build a complete machine learning pipeline from data preprocessing to model evaluation.