Master advanced local vector storage concepts with expert-level content and cutting-edge techniques.
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Advanced probability, information theory, optimization
Expert Python; HuggingFace Transformers experience
Follow these courses in order to complete the learning path. Click on any course to enroll.
State-of-the-Art Machine Learning Papers Implementation
This course, part of the 'DeepLearning.AI Data Engineering Professional Certificate', covers designing storage architectures for various use cases. It helps in selecting appropriate technologies and practicing common query patterns. While not exclusively about row, column, and vector stores for ML, it provides the foundational knowledge of data storage principles that are essential for machine learning.
A hands-on project-based course where you build a Support Vector Machine classifier using Scikit-Learn and the RBF Kernel to predict heart disease. It focuses on the practical implementation and evaluation of SV Ms.
Explore related content to expand your skills beyond this learning path.
Master advanced local vector storage concepts with expert-level content and cutting-edge techniques.
You'll work through Master cutting-edge techniques and research; Handle complex, production-scale systems; Contribute to open source and research; Process and tokenize text data effectively; Build text classification and sentiment analysis models; Implement named entity recognition (NER) systems.
About 270 hours of study across 5 courses — and you can go at your own pace.
It's pitched at advanced level, so a little prior familiarity helps.
5 curated courses, sequenced from foundational to advanced.
Around $98 total if you buy every course — but many include free audit options.
Enroll in this path to track your progress and stay motivated.