Core machine learning concepts, algorithms, and techniques — from decision trees to deep learning, with practical applications across industries.
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Multiple AI-designed drug candidates are reaching critical clinical milestones in 2026 as biotech enters its 'clinical era,' with machine learning cutting drug discovery timelines by 40% and reducing costs by billions.
Decision tree regression splits data into branches to predict continuous values. Learn how splitting, stopping criteria, and leaf predictions work with practical examples.
Unsupervised learning discovers hidden patterns in unlabeled data. Explore 20 real-world applications from customer segmentation to drug discovery and fraud detection.
Data preprocessing transforms raw data into clean, usable input for AI models. Learn the 7 essential steps: cleaning, transformation, feature engineering, splitting, augmentation, imbalanced data handling, and dimensionality reduction.
Discriminative deep learning models identify distinctions between data categories by learning decision boundaries. Learn how CNNs, RNNs, and SVMs differ from generative models.
