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Awesome-Machine-Learning
Learning tutorial for machine learning beginners
https://github.com/Billy1900/Awesome-Machine-Learning
Last synced: 4 days ago
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1. Introduction of Machine Learning Theory
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1.1 Courses
- Deep Learning
- CS221: Artificial Intelligence: Principles and Techniques
- Machine Learning from Andrew Ng - parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
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1.2 Books
- Neural Networks and Deep Learning
- Mathematics for Machine Learning
- Foundations of machine learning - level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
- Probabilistic Machine Learning: An Introduction
- Understanding Machine Learning: From Theory to Algorithms
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2. Diving into the general theory
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1.2 Books
- Convex Optimization I - convex goals, it helps to understand the form behind the problem of manageable optimizations.
- CS 228: Probabilistic Graphical Models - making under uncertainty.
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3. Data Mining
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1.2 Books
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9. Fundamental Tool
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1.2 Books
- pandas tutorial authorized by Datawhale - source standard for analytic apps in Python.
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Supplementary resources
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1.2 Books
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