📚 Machine Learning Basic Algorithms Welcome to the Machine Learning Basic Algorithms repository! This project provides an introduction to essential machine learning algorithms with intuitive explanations and easy-to-follow implementations. It's perfect for beginners and those looking to strengthen their foundation.
🧠 Algorithms Included
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Adaptive Linear Neuron (Adaline): Adaline is a single-layer neural network that uses a linear activation function. It minimizes error using gradient descent, making it a foundation for modern deep learning. Ideal for binary classification tasks.
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Find-S Algorithm: Find-S identifies the most specific hypothesis that matches all positive examples. It’s simple and effective for noise-free datasets with fully positive examples.
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Candidate Elimination Algorithm: This algorithm narrows down hypotheses using a version space approach. It maintains specific and general boundaries, providing a comprehensive understanding of the data.
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Decision Tree Classifier (ID3): ID3 is a decision-making algorithm that splits data using Information Gain. It creates interpretable tree structures for classification problems.
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Naive Bayes Classifier: This probabilistic model assumes independence among features and uses Bayes' theorem to classify data. Its simplicity makes it incredibly effective, especially for text classification.
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K-Means Clustering: An unsupervised learning algorithm that groups data into clusters. It iteratively assigns points to the nearest cluster center for customer segmentation, pattern recognition, and more.
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K-Nearest Neighbors (KNN): A lazy learning algorithm that bases classification on the majority class of its nearest neighbors. Simple, robust, and ideal for smaller datasets.
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Locally Weighted Regression (LWR): A non-parametric algorithm that emphasizes nearby points to fit a regression line. Perfect for modeling non-linear relationships with local data.
📂 Repository Structure 📁 Machine-Learning-Basic-Algorithms ├── 📄 README.md ├── 📂 Algorithms │ ├── Adaline.py │ ├── FindS.py │ ├── CandidateElimination.py │ ├── DecisionTree_ID3.py │ ├── NaiveBayes.py │ ├── KMeansClustering.py │ ├── KNN.py │ └── LocallyWeightedRegression.py ├── 📂 Datasets │ └── various datasets are present specifically └── 📂 Visualizations └── plots/ 🚀 Getting Started Clone this repository:
bash git clone https://github.com/skyakash19/machine-learning-basic-algorithms.git Navigate to the project directory:
bash cd machine-learning-basic-algorithms Install dependencies:
bash pip install -r requirements.txt 📊 Visualizations Explore visualizations like decision boundaries, confusion matrices, and clustering outcomes to deepen your understanding of algorithm performance.
🤝 Contributions Contributions and feedback are welcome! Feel free to fork this repository and submit a pull request with your enhancements.