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Explore common ML algorithms, from scratch implementations to real-world use cases, 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.

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MachineAlgoBox - Guides, Codes and Use cases of Most Common Machine Learning Algorithms

Deployment Streamlit App

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.

Key Features

  • 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.

Get Started

  1. Explore the algorithm folders and choose the one you're interested in.
  2. Follow the provided instructions in the README file of each algorithm folder to run and understand the algorithm.
  3. Dive into the use cases folder to see the algorithms in action in real-world scenarios.

Contents

1. Adaboost

1 2

2. AGGLOMERATIVE CLUSTERING

4 5

3. DBSCAN

DBSCAN DBSCAN (2)

4. DECISION TREE

DECISION TREE DECISION TREE (2)

5.DEEP Q Learning

DEEP Q-LEARNING DEEP Q-LEARNING (2) DEEP Q-LEARNING (3)

6.FACTOR ANALYSIS OF CORRESPONDENCES

FACTOR ANALYSIS OF CORRESPONDENCES FACTOR ANALYSIS OF CORRESPONDENCES (2)

7.GAN

GAN GAN (2) GAN (3)

8.GMM

GMM GMM (2)

9.GNN

GRAPH NEURAL NETWORKS GRAPH NEURAL NETWORKS (2) GRAPH NEURAL NETWORKS (3)

10.GRADIENT DESCENT

GRADIENT DESCENT GRADIENT DESCENT (2)

11.HIERARCHICAL CLUSTERING

HIERARCHICAL CLUSTERING HIERARCHICAL CLUSTERING (2)

12.HIDDEN MARKOV MODEL

HIDDEN MARKOV MODEL (HMM) HIDDEN MARKOV MODEL (HMM) (2)

13.ISOLATION FOREST

ISOLATION FOREST ISOLATION FOREST (2)

14.INDEPENDENT COMPONENT ANALYSIS

INDEPENDENT COMPONENT ANALYSIS INDEPENDENT COMPONENT ANALYSIS (2)

15.K-MEANS

K-MEANS K-MEANS (2)

16.K-NEAREST NEIGHBOUR

K-NEAREST NEIGHBOUR K-NEAREST NEIGHBOUR (2)

17.MEAN SHIFT

MEAN SHIFT MEAN SHIFT (2)

18.MOBILENET

MOBILENET MOBILENET (2)

19.MULTIMODAL PARALLEL NETWORK

MULTIMODAL PARALLEL NETWORK MULTIMODAL PARALLEL NETWORK (2)

20.NAIVE BAYES CLASSIFIERS

NAIVE BAYES CLASSIFIERS NAIVE BAYES CLASSIFIERS (2)

21.PRINCIPAL COMPONENT ANALYSIS

PRINCIPAL COMPONENT ANALYSIS PRINCIPAL COMPONENT ANALYSIS (2)

22.PROXIMAL POLICY OPTIMIZATION

PROXIMAL POLICY OPTIMIZATION PROXIMAL POLICY OPTIMIZATION (2) PROXIMAL POLICY OPTIMIZATION (3)

23.Q-LEARNING

Q-LEARNING Q-LEARNING (2) Uploading Q-LEARNING (3).png…

24.RANDOM FORESTS

RANDOM FORESTS RANDOM FORESTS (2)

25.RECURRENT NEURAL NETWORK

RECURRENT NEURAL NETWORK RECURRENT NEURAL NETWORK (2)

26.RESNET

RESNET RESNET (2) RESNET (3)

27.STOCHASTIC GRADIENT DESCENT

STOCHASTIC GRADIENT DESCENT STOCHASTIC GRADIENT DESCENT (2)

28.SUPPORT VECTOR MACHINE

SUPPORT VECTOR MACHINE SUPPORT VECTOR MACHINE (2)

29. WAVENET

WAVENET Uploading WAVENET (2).png…

30.ARMA_ARIMA MODEL

ARMA_ARIMA MODEL ARMA_ARIMA MODEL (2) ARMA_ARIMA MODEL (3)

31.BERT

BERT Uploading BERT (2).png…

32.LSTM

LSTM LSTM (2) LSTM (3)

33.ADAM OPTIMIZATION

ADAM OPTIMIZATION ADAM OPTIMIZATION (2)

34.XGBOOST

XGBOOST XGBOOST (2)

Author

Contributing

Contributions to MachineAlgoBox are warmly welcome! Whether it's fixing a bug, adding a new algorithm, or improving the documentation, every contribution is valuable.

License

This repository is licensed under the MIT License.

Connect with Us

Got questions, suggestions, or feedback? We'd love to hear from you! Connect with us on Twitter or open an issue here on GitHub.

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Explore common ML algorithms, from scratch implementations to real-world use cases, 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.

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