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Day 3 - Python Libraries for Machine Learning
Hello guys,
This is day 3 of my #100DayOfMLCode challenge. Today, I read blogs about different Machine Learning Algorithms and implemented different Python Libraries which are useful for Machine Learning enthusiasts.
I studied the following Libraries-
NumPy
NumPy is an open source extension module for Python. It provides fast precompiled functions for numerical routines. It’s very easy to work with large multidimensional arrays and matrices using NumPy.
Another advantage of NumPy is that you can apply standard mathematical operations on an entire data set without having to write loops. It is also very easy to export data to external libraries that are written in low-level languages (such as C or C++), and for data to then be imported from these external libraries as NumPy arrays.
Even though NumPy does not provide powerful data analysis functionalities, understanding NumPy arrays, and array-oriented computing will help you use other Python data analysis tools more effectively.
Scipy
SciPy is a Python module that provides convenient and fast N-dimensional array manipulation. It provides many user-friendly and efficient numerical routines, such as routines for numerical integration and optimization. SciPy has modules for optimization, linear algebra, integration and other common tasks in data science.
Matplotlib
Matplotlib is a Python module for visualization. Matplotlib allows you to quickly make line graphs, pie charts, histograms and other professional grade figures. Using Matplotlib, you can customize every aspect of a figure. When used within IPython notebook, Matplotlib has interactive features like zooming and panning. It supports different GUI backends on all operating systems, and can also export graphics to common vector and graphic formats like PDF, SVG, JPG, PNG, BMP, GIF, etc.
Scikit-learn
Scikit-Learn is a Python module for machine learning built on top of SciPy. It provides a set of common machine learning algorithms to users through a consistent interface. Scikit-Learn helps to quickly implement popular algorithms on datasets. Have a look at the list of algorithms available in Scikit-Learn, and you will realise that it includes tools for many standard machine-learning tasks(such as clustering, classification, regression, etc.).
Pandas
Pandas is a Python module that contains high-level data structures and tools designed for fast and easy data analysis operations. Pandas is built on NumPy and makes it easy to use in NumPy-centric applications, such as data structures with labeled axes. Explicit data alignment prevents common errors that result from misaligned data coming in from different sources.
It is also easy to handle missing data using Python. Pandas are the best tool for doing data munging.
Theano
Theano is a Python library for numerical computation and is similar to Numpy. Some libraries such as Pylearn2 use Theano as their core component for mathematical computation. Theano allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
NLTK
NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, and wrappers for industrial-strength NLP libraries. NLTK has been used successfully as a platform for prototyping and building research systems.
Statsmodels
Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Thanks.
Happy Reading.
