klotz: scikit-learn* + machine learning*

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  1. emlearn is an open-source machine learning inference engine designed for microcontrollers and embedded devices. It supports various machine learning models for classification, regression, unsupervised learning, and feature extraction. The engine is portable, with a single header file include, and uses C99 code and static memory allocation. Users can train models in Python and convert them to C code for inference.

  2. Scikit-learn — the go-to library for machine learning offering a user friendly, consistent interface. Pycaret — lowering the entry point for machine learning with low code, automated and end to end solutions. PyTorch — build and deploy powerful, scalable neural networks with its highly flexible architecture. TensorFlow — one of the most mature deep learning libraries, highly flexible and suited to a wide range of applications. Keras — TensorFlow made simple. FastAI — makes deep learning more accessible with a high-level API built on top of PyTorch.

  3. Comparing Clustering Algorithms Following table will give a comparison (based on parameters, scalability and metric) of the clustering algorithms in scikit-learn.

    Sr.No Algorithm Name Parameters Scalability Metric Used 1 K-Means No. of clusters Very large n_samples The distance between points. 2 Affinity Propagation Damping It’s not scalable with n_samples Graph Distance 3 Mean-Shift Bandwidth It’s not scalable with n_samples. The distance between points. 4 Spectral Clustering No.of clusters Medium level of scalability with n_samples. Small level of scalability with n_clusters. Graph Distance 5 Hierarchical Clustering Distance threshold or No.of clusters Large n_samples Large n_clusters The distance between points. 6 DBSCAN Size of neighborhood Very large n_samples and medium n_clusters. Nearest point distance 7 OPTICS Minimum cluster membership Very large n_samples and large n_clusters. The distance between points. 8 BIRCH Threshold, Branching factor Large n_samples Large n_clusters The Euclidean distance between points.

  4. Linear Regression Multiple Regression Polynomial Regression Decision Tree Logistic Regression K Nearest Neighbor Naive Bayes Random Forest Support Vector Machines Principal Component Analysis Linear Discriminant Analysis K Means Clustering Hierarchical Clustering

  5. A Comprehensive Guide to Understand and Implement Text Classification in Python

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