Learning

7812 Paths · page 360 / 782

• Active Learning: Designing Systems that Query Data Effectivelyactive

  This advanced graduate-level path equips machine learning practitioners with the knowledge to design systems that actively select the most informative data for labeling. Starting from core ML and probabilistic foundations, it covers query strategies, uncertainty and diversity sampling, expected model change, and semi-supervised learning, with a strong emphasis on practical implementation and evaluation.

  id: 4231slug: clp-1788630489681-iswuqkupdated: 2026-09-07

• Online and Incremental Learning Algorithmsactive

  A comprehensive learning path for ML engineers to understand and implement online/incremental learning algorithms, covering foundations, core algorithms, concept drift, and practical applications.

  id: 4230slug: clp-1788630473924-f2hyx7updated: 2026-09-07

• Building Recommendation Enginesactive

  This learning path guides recommender system engineers through the design, implementation, and evaluation of recommendation engines. It covers fundamental machine learning concepts, collaborative filtering, content-based methods, matrix factorization, ALS, deep learning recommenders, hybrid systems, and evaluation techniques. The path emphasizes practical application and systematic understanding.

  id: 4229slug: clp-1788630457116-xn6et3updated: 2026-09-07

• Anomaly Detection and Rare Event Identification with Machine Learningactive

  This advanced graduate-level path equips ML practitioners with the knowledge and skills to identify outliers and rare events using machine learning. It covers statistical foundations, classic algorithms like Isolation Forest and One-Class SVM, deep learning approaches such as autoencoders, and appropriate evaluation metrics for imbalanced data.

  id: 4228slug: clp-1788630438898-bowmphupdated: 2026-09-07

• Dimensionality Reduction for Visualization and Preprocessing: PCA, t-SNE, and UMAPactive

  A comprehensive graduate-level path for ML practitioners to master linear and nonlinear dimensionality reduction. It covers foundational linear algebra, classical methods (PCA, LLE), modern manifold learning (t-SNE, UMAP), autoencoder-based approaches, and practical interpretation and preprocessing workflows.

  id: 4227slug: clp-1788630416549-xexcfsupdated: 2026-09-07

• Data Preparation for Machine Learningactive

  A foundational path for ML beginners to master essential data preparation techniques using Python, including handling missing values, scaling, encoding categorical variables, outlier treatment, and data splitting. This path ensures learners can effectively clean and transform datasets to improve model performance.

  id: 4226slug: clp-1788630394669-1do44yupdated: 2026-09-07

• End-to-End Machine Learning Projectactive

  This path guides ML students through the complete lifecycle of a machine learning project, from problem definition through deployment and documentation. It covers essential data handling, modeling, evaluation, and communication skills, ensuring a solid foundation for real-world applications.

  id: 4225slug: clp-1788630380545-3qpssnupdated: 2026-09-07

• Interpretable and Explainable Machine Learningactive

  This advanced learning path equips machine learning practitioners with the knowledge and skills to make models interpretable and explainable. It covers interpretable model design, model-agnostic explanation methods (LIME, SHAP), counterfactual explanations, and visualization techniques, grounded in necessary prerequisites from ML and statistics.

  id: 4224slug: clp-1788630365350-frhdtaupdated: 2026-09-07

• Advanced ML Workflow Automation and Hyperparameter Tuningactive

  This learning path equips ML practitioners with the knowledge and skills to automate machine learning workflows and optimize hyperparameters. It covers foundational ML concepts, core hyperparameter tuning strategies, advanced optimization methods, and AutoML frameworks, culminating in neural architecture search. The path emphasizes practical application and systematic learning.

  id: 4223slug: clp-1788630347923-sq6nshupdated: 2026-09-07

• Advanced Machine Learning Transfer and Adaptationactive

  This learning path guides experienced ML practitioners through the systematic study of knowledge transfer across domains and tasks. It covers foundational concepts, pretrained models, fine-tuning, domain adaptation, multi-task learning, and few-shot/zero-shot learning, emphasizing when and how to apply each technique.

  id: 4222slug: clp-1788630330242-recohpupdated: 2026-09-07