What are the top Python Libraries for Machine Learning in 2021?

Published on Sept. 25, 2021

Machine Learning is the science of programming a computer by which they are able to learn from different kinds of data. A more general definition given by Arthur Samuel is – “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” They are typically used to solve various types of life problems. 

Before the evaluation of Machine Learning, people used to perform tasks by manually coding all the algorithms by mathematical and statistical formulas. This made the process time-consuming, tedious, and inefficient. As the technology was introduced, it became very much easy and efficient compared to the olden days by various Python libraries, frameworks, and modules.

 

Today, Python is one of the most popular programming languages for this task and it has replaced many languages in the industry, one reason is its vast collection of libraries.

 

Python libraries that are used in Machine Learning are: 

• NumPy
• Scipy
• Scikit-learn
• Theano
• TensorFlow
• Keras
• PyTorch
• Pandas
• Matplotlib

 

In this blog, we are going to discuss the first 4 libraries.

Let’s discuss the mentioned libraries in brief.

 

 

Numpy

NumPy is a very popular Python library for large multi-dimensional array and matrix processing, with the help of a large collection of high-level pre-defined mathematical functions. It is very useful for fundamental scientific computations in Machine Learning. It is particularly useful for linear algebra, Fourier transform, and random number capabilities. High-end libraries like TensorFlow uses NumPy internally for the manipulation of Tensors. Computational complexities in NumPy are much easier as compared to normal operations due to its efficient inbuilt algorithms.

 

Command

pip install numpy

import numpy

Its uses are

• Arithmetic Operations
• Statistical Operations
• Bitwise Operators
• Copying and Viewing Arrays
• Broadcasting
• Stacking and More

 

 

Scipy

The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. The SciPy is one of the core packages that make up the SciPy stack. SciPy is also very useful for image manipulation.

 

Command

pip install scipy

import scipy
 

Its uses are

• Classes for Visualizing and Manipulating Data
• Web and Database routines for parallel programming
• Easy and fast
• Open-source and More

 

 

Scikit-Learn

Scikit-learn is one of the most popular ML libraries for classical ML algorithms. It is built on top of two basic Python libraries, viz., NumPy and SciPy. Scikit-learn supports most of the supervised and unsupervised learning algorithms. Scikit-learn can also be used for data-mining and data analysis, which makes it a great tool who is starting out with Machine Learning.

 

Command

pip install scikit-learn

import scikit-learn
 

Its uses are

• Fraud Detection
• Network Intrusion
• Finance
• Insurance
• Maintenance
• Medicine (Unusual Symptoms)
• Measurement Errors (From Sensors) and More

 

 

Theano

We all know that Machine Learning is basically mathematics and statistics. Theano is a popular python library that is used to define, evaluate and optimize mathematical expressions involving multi-dimensional arrays in an efficient manner. It is achieved by optimizing the utilization of CPU and GPU. It is extensively used for unit-testing and self-verification to detect and diagnose different types of errors. Theano is a very powerful library that has been used in large-scale computationally intensive scientific projects for a long time but is simple and approachable enough to be used by individuals for their own projects. Its most common usage is seen in solving neural network problems & computing graph gradients.

 

Command

pip install Theano

import Theano
 

Its uses are

• Numerical Computation Library
• Best for implementing Neural Network Models
• Define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently and More

 

 

Final Voice

The remaining 5 libraries will be covered in the next blog.

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