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.
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.