MachineAlgoBox is a comprehensive collection of the most common machine learning algorithms by Tushar Aggarwal , implemented from scratch and accompanied by detailed use cases. This repository serves as a valuable resource for both beginners and experienced practitioners, providing a hands-on approach to understanding and implementing various machine learning techniques. Explore a wide range of algorithms, from classic ones like linear regression and decision trees to advanced methods such as neural networks and support vector machines. Each algorithm is accompanied by clear explanations, code implementations, and real-world use cases, enabling you to grasp their underlying principles and apply them to different problem domains. Whether you're seeking to learn, practice, or explore machine learning, MachineAlgoBox is your go-to repository for understanding and working with diverse algorithms.
- From Scratch Implementations: Gain a deep understanding of algorithms by exploring their step-by-step implementations from scratch.
- Real-World Use Cases: Discover practical use cases for each algorithm, providing insights into how they can be applied to solve real-world problems.
- Clear Explanations: Find clear and concise explanations for each algorithm, helping you grasp their underlying principles.
- Code Examples: Access well-documented code examples that you can run and experiment with.
- Diverse Algorithm Collection: Explore a wide range of algorithms, including linear regression, decision trees, neural networks, support vector machines, and more.
- Explore the algorithm folders and choose the one you're interested in.
- Follow the provided instructions in the README file of each algorithm folder to run and understand the algorithm.
- Dive into the use cases folder to see the algorithms in action in real-world scenarios.
12.HIDDEN MARKOV MODEL
- ©2023 Tushar Aggarwal. All rights reserved
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- Kaggle
Contributions to MachineAlgoBox are warmly welcome! Whether it's fixing a bug, adding a new algorithm, or improving the documentation, every contribution is valuable.
This repository is licensed under the MIT License.
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