Top 5 Machine Learning Libraries for Beginners

This article provides a comprehensive overview of the top 5 machine learning libraries for beginners. These libraries are easy to use and provide a wide range of features and capabilities, making them ideal for beginners who are just getting started with machine learning.

Whether you're interested in learning more about machine learning or building your first machine learning model, this article is a great starting point.

TensorFlow

This is the most popular ML/DL in the world today, it wasn’t the first, but when it came with its simplicity. It grew rapidly to surpass the already existing libraries. It was because of its easy-to-use APIs. You could guess, it was released by Google in November 2015.

It is written in Python, but now we have a JavaScript port of it tensorflow.js.

This was due to the upsurge of JavaScript following the advent of Node.js.

According to Wikipedia:

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.

Theano

Theano is a Python library for fast numerical computation that can be run on the CPU or GPU. It was developed by the LISA (now MILA) group at the University of Montreal, Quebec, Canada. It is named after a Greek mathematician, Theano.

According to Wikipedia:

Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones.

PyTorch

This is a Facebook deep-learning library. PyTorch was built by Facebook, as it name implies it was written in Python.

Compared to Tensorflow, it is easier to learn and use but as you can guess it was beaten by Tensorflow. This is because Tensorflow encompasses a wide-range of stuff in ML/DL while PyTorch has a few. Nonetheless, PyTorch provide a simpler API for working with Neural Networks.

According to Wikipedia:

PyTorch is a deep learning library based on the Torch library, used for applications such as computer vision and natural language processing. It is primarily developed by Facebook’s artificial intelligence research group. It is free and open-source software released under the Modified BSD license.

Scikit-learn

This is a popular ML library, built on NumPy, SciPy and matplotlib. It focuses mostly on ML algorithms:

• Supervised learning
• Unsupervised learning
• Linear regression
• Logistic regression
• SVM
• Naive Bayes
• Gradient boosting
• Clustering
• K-Means

Like PyTorch it is less mature to Tensorflow, but it provides simple and efficient tools for data mining and data analysis.

According to Wikipedia:

Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.[3] It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Keras

Keras is a DL library that wraps around the functionalities of othe libraries like Tensorflow, Theano or CNTK. Written in Python.

Keras has upper-hand on its competitors like Scikit-learn and PyTorch because it runs on top of Tensorflow.

According to Wikipedia:

Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

Conclusion

There are many more tools in ML/DL world, but these are the most popular and widely used. ML/DL is a huge world and the most promising tech right now .

If you have any question regarding this or anything I should add, correct or remove, feel free to comment, email or DM me.

Thanks for reading ❤

#machinelearning #tensorflow #python #datascience