Path Category
Loading Path Category from the AllPath API…
ALLPATHFoundationALLPATHFoundationPath Category
Loading Path Category from the AllPath API…
Path Category
Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7812 Paths
7812 Paths · page 344 / 782
A systematic learning path for NLP practitioners aiming to master coreference resolution and entity linking. It covers mention detection, coreference models, entity linking, evaluation, and recent neural approaches, with a focus on identifying expressions that refer to the same entity.
A comprehensive learning path for NLP professionals aiming to analyze discourse structure and relationships. It covers theoretical foundations, computational models, and practical parsing techniques, culminating in the ability to build and evaluate discourse parsers.
This advanced learning path equips legal tech practitioners with the knowledge and skills to apply natural language processing (NLP) to legal documents and applications. It covers core NLP techniques, legal text characteristics, document analysis, contract extraction, summarization, case law analysis, and information retrieval, culminating in a capstone project.
A comprehensive graduate-level learning path for building modern text-to-speech systems. It covers the core deep learning architectures (WaveNet, Tacotron, FastSpeech), the essential linguistic foundations, and advanced topics such as speaker adaptation, prosody modeling, and evaluation methodologies. Designed for practitioners aiming to develop, improve, or research TTS systems.
This path guides NLP professionals from the logical foundations of textual entailment through the design of modern NLI datasets to deep learning models for Natural Language Inference (NLI). It emphasizes the reasoning skills needed to understand and build systems that classify entailment, contradiction, and neutrality between sentence pairs. The curriculum progresses from formal logic and semantics to neural architectures, covering evaluation, analysis, and advanced reasoning challenges.
This graduate-level path equips NLP practitioners with the skills to analyze and classify sentiment in text, covering foundational NLP, core sentiment analysis tasks, and advanced deep learning techniques. It progresses from basic text processing and representation to sophisticated models for polarity, emotion, aspect-based, and fine-grained sentiment analysis.
This graduate-level learning path guides NLP practitioners through the theory and practice of topic modeling, from foundational document representation to advanced neural approaches. It covers classical models (LSA, NMF, LDA), evaluation methods, coherence metrics, and extensions like dynamic and neural topic models, with a focus on systematic understanding and practical application.
A structured learning path for data professionals to master text mining and pattern discovery using Python. It covers the essential NLP pipeline, from preprocessing to advanced techniques like topic modeling, and emphasizes practical application with Python libraries.
This learning path guides NLP students through the entire lifecycle of a natural language processing project, from problem definition and data collection to preprocessing, modeling, evaluation, deployment, and presentation. It emphasizes hands-on application and covers essential prerequisites in Python, machine learning, and deep learning, ensuring a solid foundation for building and deploying real-world NLP systems.
A comprehensive graduate-level path covering speech signal fundamentals, feature extraction, acoustic and language modeling, modern end-to-end ASR systems (Wav2Vec, Whisper), and text-to-speech synthesis, with NLP and signal processing prerequisites.