Python with Machine Learning and Data Science Tools
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  • Operating System : Ubuntu 14.04 LTS / Windows

  • IDE: Spyder /PyCharm

  • Databases: PostgreSQL / MySQL / SQLite

S. No.Python LibrariesTypeDescription
1NumPyNumerical OperationsSupporting the scientific computing that is high-level mathematical functions over large, multi-dimensional arrays and matrices
2Matplotlib, Seaborn, Bokeh, Plotly, NetworkX, Basemap,d3py, ggplot, prettyplotlibVisualizationVisualizing the data from Python quickly Plotting 2D graphs in various formats such as bar charts, plots, histograms, error charts, power spectra, and scatter plots across platforms using a few lines of code
3Scikit- Learn, Shogun, Pattern, PyLearn2, PyMCMachine Learning (ML) AlgorithmsProviding machine learning algorithms such as classification, clustering, and regression Interoperating with the numerical and scientific libraries such as NumPy and SciPy
4PandasData AnalysisOffering high-performance operations and data structures for time series and numerical tables manipulation
5NLTKNatural Language Processing (NLP)Analyzing and understanding the English written human language data Providing easy interfaces over 50 lexical resources and corpora Supporting functionalities include tokenization, stemming, tagging, parsing, and semantic reasoning
6StatsmodelStatistical AnalysisConducting statistical data exploration and statistical tests Performing statistical computations such as descriptive statistics and providing classes and functions to estimate different statistical models
7PyBrainNeural NetworkProviding algorithms for reinforcement learning, neural networks, unsupervised learning, and evolution to analyze large-scale data
8GensimTopic ModelingSupporting natural language processing and unsupervised topic modeling through statistical machine learning Supporting automatic extraction of semantic topics from documents
9Keras, TensorFlow, TheanoDeep LearningProviding fast computing of numerical data with deep neural networks Effectively handling mathematical expressions, especially, matrix values
10ScrapyWeb CrawlingExtracting the required data from the websites in a simple and fast way
11SciPy, Dask, Numba, HPAT, CythonData Science toolsPerforming scientific computing involves special functions, integration, linear algebra, optimization, interpolation, Ordinary Differential Equation (ODE) solvers, Fast Fourier Transform (FFT), and image processing Optimizing the machine code at runtime
12HDF5Data manipulationEnabling the storage of huge amounts of numerical data and manipulating the data easily from NumPy
13SymPyStatistical ApplicationsSupporting symbolic mathematics and modeling the full-featured Computer Algebra System (CAS)
14csvkit, PyTables, SQLite3Storage and Data FormattingConverting to CSV formats from different file formats such as JSON and Excel and working with CSV Managing hierarchical datasets and accessing the large-scale databases
15Cryptography, pyOpenSSL, passlib, requests- oauthlib, ecdsa, PyCryptodome, service- identitySecurityProviding low-level primitives Supporting extensive error handling and providing cryptographic authority Reporting bugs and hashing the data
16NumPy, SciPy, matplotlib, OpenCV, scikit-learn, scikit-image, ilastikImage ProcessingProviding a set of algorithms for image processing Supporting geometric transformations, segmentation, filtering, color space manipulation, morphology, analysis, and feature detection
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