List of libraries to handle most of the Machine Learning tasks
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine Learning is also one of the most prominent tools of cost-cutting in almost every sector of industry nowadays; It is used in internet search engines, email filters to sort out spam, websites to make personalised recommendations, banking software to detect unusual transactions, and lots of apps on our phones such as voice recognition.
In this blog on Machine Learning libraries, we will discuss a comprehensive list of libraries to handle most of the Machine Learning tasks.
Here’s a list of topics that will be covered:
- Numpy ----> Scientific computation
- Pandas ----> Tabular Data
- Scikit Learn ----> Data Modeling & Pre-processing
- StatsModels ----> Time-Series Analysis
- NLTK, Regular expressions ----> Text-Processing
- TensorFlow , Pytorch ----> Deep Learning
- Conclusion
1. Numpy
NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.
Numpy Installation
The only prerequisite for installing NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science.
Recommended simplest ways to install Python and Numpy:
On all of Windows, macOS, and Linux:
Install Anaconda (it installs all packages you need and all other tools mentioned below).
For writing and executing code, use notebooks in JupyterLab for exploratory and interactive computing, and Spyder or Visual Studio Code for writing scripts and packages.
Use Anaconda Navigator to manage your packages and start JupyterLab, Spyder, or Visual Studio Code. Learn more about Numpy Library.
2. Pandas
Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.
Pandas is well suited for many different kinds of data:
Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
Ordered and unordered (not necessarily fixed-frequency) time series data.
Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
Any other form of observational / statistical data sets. The data need not be labeled at all to be placed into a pandas data structure
Installation
The easiest way to install pandas is to install it as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. This is the recommended installation method for most users. Learn more about Pandas Library
3. Scikit Learn
What is Scikit-Learn (Sklearn) 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 consistent interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib.
Prerequisites
Before we start using scikit-learn latest release, we require the following:
Python (>=3.5)
NumPy (>= 1.11.0)
Scipy (>= 0.17.0)li
Joblib (>= 0.11)
Matplotlib (>= 1.5.1) is required for Sklearn plotting capabilities.
Pandas (>= 0.18.0) is required for some of the scikit-learn examples using data structure and analysis.
Installation
The two easiest ways to install scikit-learn:
- Using pip
Following command can be used to install scikit-learn via pip
- Using conda Following command can be used to install scikit-learn via conda
4. StatsModels
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. The results are tested against existing statistical packages to ensure that they are correct. Statsmodels supports Python 3.7, 3.8, and 3.9.
Installation
Statsmodels is available through conda provided by Anaconda. The latest release can be installed using:
5. NLTK, Regular expressions
When dealing with textual data, you may be required to find or replace words that follow a particular pattern. For instance, you may wish to find words that end with “al” when carrying out data wrangling. Using regular expressions is an easy way to go about this in Natural Language Processing. It is a powerful method used to find, split, or replace words according to some pattern. Regular expressions can help you extract key information from dirty data during data analysis. You can quickly get dates, price of a good, the email address of customers, or their telephone numbers.
You can make use of the regular expression by importing the re module
import re
Learn more about NLTK Regular Expressions
6. TensorFlow , Pytorch
TensorFlow
TensorFlow was developed by Google and released as open source in 2015. The name “TensorFlow” describes how you organize and perform operations on data. The basic data structure for both TensorFlow and PyTorch is a tensor. TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud. When you use TensorFlow, you perform operations on the data in these tensors by building a stateful dataflow graph, kind of like a flowchart that remembers past events. Click here to Set up Tensorflow
import tensorflow as tf print ("TensorFlow version:", tf.version)
Learn more about Tensorflow library
Pytorch PyTorch was developed by Facebook and was first publicly released in 2016. PyTorch is an optimized tensor library for deep learning using GPUs (graphics processing unit) and CPUs (central processing unit).
Installation using Anaconda
To install Anaconda, you will use the 64-bit graphical installer for PyTorch 3.x. Click on the installer link and select Run. Anaconda will download and the installer prompt will be presented to you. The default options are generally sane. CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs).With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. No CUDA To install PyTorch via Anaconda, and do not have a CUDA-capable system or do not require CUDA, in the above selector, choose OS: Windows, Package: Conda and CUDA: None. Then, run the command that is presented to you.
With CUDA To install PyTorch via Anaconda, and you do have a CUDA-capable system, in the above selector, choose OS: Windows, Package: Conda and the CUDA version suited to your machine. Often, the latest CUDA version is better. Then, run the command that is presented to you.
7. Conclusion
In this blog, we discussed the List of libraries to handle most of the Machine Learning tasks. Every library has its own positives and negatives. These aspects should be taken into account before selecting a library for the purpose of machine learning tasks.