notebooks/04_more_science.ipynb

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    "04.1 SciPy  \n",
    "===\n",
    " \n",
    "SciPy, pronounced as Sigh Pi, is a scientific python open source, distributed under the BSD licensed library to perform Mathematical, Scientific and Engineering Computations. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation.\n",
    "\n",
    "https://docs.scipy.org/doc/scipy/reference/"
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    "|    Sub Package    | Description                            |\n",
    "|:-----------------:|----------------------------------------|\n",
    "|   scipy.cluster   | Vector quantization / Kmeans           |\n",
    "|  scipy.constants  | Physical and mathematical constants    |\n",
    "|   scipy.fftpack   | Fourier transform                      |\n",
    "|  scipy.integrate  | Integration routines                   |\n",
    "| scipy.interpolate | Interpolation                          |\n",
    "|      scipy.io     | Data input and output                  |\n",
    "|    scipy.linalg   | Linear algebra routines                |\n",
    "|   scipy.ndimage   | n-dimensional image package            |\n",
    "|     scipy.odr     | Orthogonal distance regression         |\n",
    "|   scipy.optimize  | Optimization                           |\n",
    "|    scipy.signal   | Signal processing                      |\n",
    "|    scipy.sparse   | Sparse matrices                        |\n",
    "|   scipy.spatial   | Spatial data structures and algorithms |\n",
    "|   scipy.special   | Any special mathematical functions     |\n",
    "|    scipy.stats    | Statistics                             |"
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    "04.2 Scikit-Learn\n",
    "===\n",
    "\n",
    "\n",
    "Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib."
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   "source": [
    "![scikitlearn](../img/Scikit-learn_machine_learning_decision_tree.png)"
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    "Rather than focusing on loading, manipulating and summarising data, Scikit-learn library is focused on modeling the data. Some of the most popular groups of models provided by Sklearn are as follows −\n",
    "\n",
    "* Supervised Learning algorithms − Almost all the popular supervised learning algorithms, like Linear Regression, Support Vector Machine (SVM), Decision Tree etc., are the part of scikit-learn.\n",
    "\n",
    "* Unsupervised Learning algorithms − On the other hand, it also has all the popular unsupervised learning algorithms from clustering, factor analysis, PCA (Principal Component Analysis) to unsupervised neural networks.\n",
    "\n",
    "* Clustering − This model is used for grouping unlabeled data.\n",
    "\n",
    "* Cross Validation − It is used to check the accuracy of supervised models on unseen data.\n",
    "\n",
    "* Dimensionality Reduction − It is used for reducing the number of attributes in data which can be further used for summarisation, visualisation and feature selection.\n",
    "\n",
    "* Ensemble methods − As name suggest, it is used for combining the predictions of multiple supervised models.\n",
    "\n",
    "* Feature extraction − It is used to extract the features from data to define the attributes in image and text data.\n",
    "\n",
    "* Feature selection − It is used to identify useful attributes to create supervised models.\n",
    "\n",
    "* Open Source − It is open source library and also commercially usable under BSD license."
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   "source": [
    "---\n",
    "[Go back to index](00_index.ipynb)"
   ]
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