10 Best Python Libraries For Machine Learning

Machine Learning has been developing at an unprecedented pace since the last decade. When ML started as a technology, no one would have thought that it would gain so much popularity due to its popular applications in daily life. The developers then had to face a lot of things. 

 

One of them was performing and executing ML tasks manually. With passing time, the developers started to realize that this manual execution is becoming tough. The time that was being consumed in getting these executed was too much. 

 

But, when ML entered the mainstream technical world suddenly things started to ease up. One such thing that eased up ML for the developers was the introduction of Libraries in machine Learning.

 

In this blog, we are going to talk about the top-7 ML libraries for ML developers. Before we dive in let us try to know what an ML library is?

 

A Machine Learning Library

 

An ML library is often a collection of functions and procedures that are ready to use. A solid selection of libraries is an essential aspect of a developer's toolkit for researching and building sophisticated programs without having to write a lot of code.

 

Developers can avoid writing redundant code by using libraries. There are also other libraries dedicated to various topics. We have text processing libraries, graphics libraries, data manipulation libraries, and scientific calculation libraries, for example.

 

Hundreds of machine learning libraries are in active development as machine learning continues to open up new possibilities for humanity and attract newcomers. However, not all of them are excellent. However, the good news is that several of them are.

 

(Similar read: NLP Python-based libraries)

 

Machine Learning Libraries with Python

 

1. TensorFlow 

 

TensorFlow must be mentioned first when discussing Machine Learning libraries. After all, it is unquestionably one of the world's most popular Machine Learning libraries. 

 

TensorFlow is a JavaScript-based Machine Learning toolkit developed by Google that is specifically built for numerical calculation utilizing data flow graphs. 

 

It comes with several essential tools, libraries, and resources that make developing, training, and deploying machine learning applications a breeze. It can run on GPUs, CPUs, and even mobile computing platforms, which is the best part.

 

TensorFlow is widely used for model training and deployment on Node.js and in browsers. TensorFlow Lite (a lightweight library) can be used to deploy models on mobile and embedded devices, while the main library can be used to construct and train ML models in browsers.

 

2. Keras

 

Keras is a Python-based open-source neural network library. TensorFlow, Theano, Microsoft Cognitive Toolkit, and PlaidML are all supported in it. 

 

Keras is extremely user-friendly, modular, and expandable, as it was created to promote rapid experimentation with Deep Neural Networks. While Keras is capable of rapid experimentation with Deep Neural Nets, it is not capable of low-level computation; for this, it relies on the "backend" library.

 

Keras' most significant advantage is its speed. It features built-in data parallelism support, allowing it to process enormous amounts of data while also reducing the time required to train models.

 

3. Pandas

 

Pandas can be thought of as the Python equivalent of Microsoft Excel. You should consider utilizing Pandas to handle tabular data whenever possible. 

 

The good thing about Pandas is that performing operations only takes a few lines of code. If you want to do something complicated and don't want to write a lot of code, there's a good chance a Pandas command exists to accomplish your goal in a few lines.

 

Pandas can handle all aspects of data processing, transformation, and visualization one-stop-shop. If you want to be a Data Scientist or compete in Machine Learning contests, Pandas can help you cut down on your burden and focus on problem-solving rather than creating boilerplate code.

 

(Also read: Exploratory Data Analysis Using Pandas)

 

4. Theano

 

Theano is a Python-based Machine Learning package that is very similar to NumPy. It can take structures and turn them into efficient NumPy and other native libraries code. 

 

Theano is a programming language that is mostly used for numerical computations. It is capable of handling the many forms of computing required by big neural network programs used in Deep Learning.

 

Theano makes it easy to define, optimize, and evaluate multi-dimensional array-based mathematical expressions. It features clean symbolic differentiation and can generate dynamic code in C. 

 

Perhaps the most important feature of this ML package is that it makes use of GPU, which speeds up data-intensive operations by up to 100 times when compared to CPU alone. Theano's speed is what sets it apart as a powerful tool for sophisticated computation and Machine Learning projects.

 

5. PyTorch

 

PyTorch is an open-source Deep Learning library that was influenced by the Torch library. It was created by Facebook's AI research team and is a Python-based library, as the name suggests. It has a C++ frontend, but it also offers a well-polished Python interface.

 

PyTorch is mostly used in computer vision and natural language processing applications. In both research and production, PyTorch's "torch.distributed" backend offers scalable distributed training and performance improvement. 

 

Deep Neural Networks (based on a tape-based auto diff system) and Tensor computation using GPUs are PyTorch's two main characteristics.

 

6. Matplotlib

 

Matpoltlib is one of the most used Python data visualization libraries. It's a 2D-plotting package that lets you make graphs and plots in two dimensions. 

 

It, like Pandas, has nothing to do with Machine Learning. It is, however, a great data visualization technique for identifying patterns in enormous datasets.

 

Tkinter, wxPython, Qt, and GTK+ are examples of general-purpose GUI toolkits that use Matplotlib's object-oriented API to embed plots in programs. It also includes the PyPlot module, which simplifies charting by allowing users to adjust line styles, font settings, and axes formatting, among other things. 

 

You can make plots, bar charts, histograms, power spectra, error charts, scatterplots, and much more with Matplotlib.

 

7. Regular Expressions (Regex)

 

Regex, or regular expressions, is the most basic but most valuable library for text processing. It aids with the discovery of text in a document using predefined string patterns. Regex, for example, may quickly replace all the 'can't's and 'don't's in your text with cannot or do not.

 

If you want to find phone numbers in your text, all you have to do is establish a pattern and regular expressions will return all of the phone numbers. It can not only discover patterns but also replace them with any string you want. Making accurate matching patterns can be difficult at first, but once you get the hang of it, it's a lot of fun!

 

(Suggested read: Different types of Machine Learning Methods)

 

8. Scikit-Learn

 

Scikit-Learn is an open-source Python-based Machine Learning package based on NumPy, SciPy, and Matplotlib. To mention a few, Scikit-Learn includes classification, regression, clustering, and dimensionality reduction methods, as well as Naive Bayes, Gradient boosting, K-means, and model selection. It's a fantastic data-mining, data-analysis, and statistical-modeling tool.

 

Scikit-learn offers excellent documentation and a large support community, which is one of its best advantages. The only disadvantage is that it does not allow distributed computing in large-scale production environments.

 

9. NLTK

 

The Natural Language Toolkit, or NLTK, is a large library for doing Natural Language activities. 

 

It's a one-stop-shop for all your text processing requirements, including word tokenization, lemmatization, stemming, dependency parsing, chunking, stopwords removal, and more.

 

Any NLP task, such as Language Modeling, Neural Machine Translation, or Named Entity Recognition, requires extensive text processing. It also includes a wordnet synonym database.

 

 

10. SciPy

 

SciPy is a Python-based machine learning environment for math, science, and engineering. Its primary application is in scientific and technical computers. The NumPy array object is the foundation for SciPy. 

 

It's part of the NumPy stack, which also includes Matplotlib, Pandas, SymPy, and a slew of other scientific computing tools. SciPy uses a multi-dimensional array provided by the NumPy module as the underlying data structure.

 

SciPy includes modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ordinary differential equation solving, and many more tasks in scientific programming.

 

The blog ends here, machine learning libraries are one of the most important parts of the ML development process. These were some of the best machine learning libraries available. You can use them according to the need you have and the purpose they serve. These libraries take care of every requirement you will have as an ML developer.

 

(Similar reading: R Libraries/Packages for Data Science)