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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7812 Paths
7812 Paths · page 362 / 782
A comprehensive learning path for practitioners aiming to implement deep learning models using TensorFlow. It covers Python and machine learning fundamentals, TensorFlow core concepts, Keras APIs, custom training, data pipelines, distributed training, and deployment. The path progresses from foundational knowledge to advanced practical skills, ensuring a solid understanding and hands-on capability.
This advanced learning path equips deep learning practitioners with the skills to implement, train, and evaluate deep learning models using PyTorch. It covers essential PyTorch components—tensors, autograd, neural network modules, data loading, and training loops—and extends to advanced architectures like CNNs, RNNs, and Transformers. The path integrates necessary machine learning and Python prerequisites, ensuring a solid foundation for practical implementation.
A comprehensive graduate-level path covering core neural network architectures and deep learning concepts. Starting from mathematical foundations and the perceptron, progressing through training mechanics, and culminating in advanced architectures like CNNs, RNNs, LSTMs, autoencoders, and GANs, with practical framework skills.
This advanced graduate-level path equips ML practitioners with a deep understanding of ensemble methods, from foundational concepts to state-of-the-art implementations. Learners will master bagging, boosting, and stacking, and apply them using modern libraries like XGBoost, LightGBM, and CatBoost to achieve superior model accuracy.
This learning path equips ML practitioners with rigorous methods to evaluate and validate machine learning models. It covers data splitting, cross-validation, bootstrap, performance metrics, and diagnosing overfitting and underfitting, ensuring reliable model selection.
This learning path guides ML practitioners through the systematic process of creating and selecting features to improve model performance. It covers feature transformation, encoding, scaling, dimensionality reduction, and feature selection methods, with a foundation in ML basics. The path emphasizes practical application and understanding of trade-offs.
A structured learning path covering major unsupervised learning methods including clustering, dimensionality reduction, and association rules. It starts with essential prerequisites in Python and statistics, then systematically progresses through core algorithms, evaluation methods, and practical applications.
A structured path for university ML students to master core supervised learning algorithms, from foundational concepts through model evaluation and hyperparameter tuning. It covers linear and logistic regression, KNN, decision trees, and SVM, with practical knowledge of evaluation and tuning.
A systematic learning path to master Python programming for implementing machine learning models. Covers Python essentials, data science libraries, machine learning basics, and deep learning frameworks, with hands-on practice using Jupyter notebooks.
A systematic path through the essential mathematics for machine learning, starting from high school algebra and advancing through linear algebra, multivariate calculus, probability, statistics, and optimization. Each concept is tied to its role in ML algorithms, ensuring a solid foundation for further study.