Python for ML
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The document discusses the most basic Python libraries for machine learning. It covers libraries for data gathering (Beautiful Soup, Requests, Pandas), data cleaning (NumPy, Pandas), exploring data (Seaborn, Matplotlib.pyplot, Pandas), building models (SciKit-learn, Statsmodels), and visualization (Seaborn, Matplotlib.pyplot, Plotly, Geoplotlib). Beautiful Soup is for parsing HTML/XML, Requests makes HTTP requests, Pandas handles data structures, NumPy provides scientific computing tools. Seaborn and Matplotlib create plots and visualizations. SciKit-learn has machine learning algorithms. Statsmodels fits statistical models.
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Python for ML
- 1. Python for ML MOST BASIC LIBRARIES 1
- 2. Libraries And ML Scope ML Data Gathering Data Cleaning Exploring DataBuilding Model Visualization 2
- 3. Data Gathering Beautiful Soup Is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work. Requests Is the de facto standard for making HTTP requests in Python. It abstracts the complexities of making requests behind a beautiful, simple API so that you can focus on interacting with services and consuming data in your application. Pandas Is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. 3
- 4. Data Cleaning 4 NumPy Is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and random number capabilities Pandas Is an open source, BSD-licensed library providing high- performance, easy-to-use data structures and data analysis tools for the Python programming language.
- 5. Exploring Data 5 Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. Pandas Is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
- 6. Building Model 6 SciKit-learn Is an open source machine learning library that that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities. Statsmodels Is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator.
- 7. Visualization 7 Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., Plotly is a web-based toolkit to form data visualizations. Plotly can also be accessed from a Python Notebook and has a great API. Geoplotlib Is a toolbox for creating maps and plotting geographical data. You can use it to create a variety of map-types, like choropleths, heatmaps, and dot density maps.