Machine Learning (ML) and Artificial Intelligence (AI) are spreading across various industries, and most enterprises have started actively investing in these technologies. With the expansion of volume as well as the complexity of data, ML and AI are widely recommended for its analysis and processing. AI offers more accurate insights, and predictions to enhance business efficiency, increase productivity and lower production cost.

AI and ML projects differ from conventional software projects. It varies based on the technology stack, the skills for ML-based projects and the demand for in-depth research. For building ML and AI outline, you have to choose a programming language, which should be flexible, stable and include predefined libraries & frameworks. Python is one of such languages wherein you can see many Python machine learning and Artificial Intelligence projects developing today. Here we have listed the top 8 best Python libraries that could be used for machine learning.

Implementing ML and AI algorithms require a well-structured & well-tested environment to empower developers to come up with the best quality coding solutions. To reduce development time, there are countless Python libraries for machine learning. Python library or framework is a pre-written program that is ready to use on common coding tasks. Let us become familiar with the best Python machine learning libraries:

Abstraction is the major benefit of TensorFlow Python towards machine learning and AI projects. This feature allows the developers to focus on comprehensive logic of the app instead of dealing with the mundane details of implementing algorithms. With this library, python developers can now effortlessly leverage AI and ML to create unique responsive applications, which responds to user inputs like facial or voice expression.

Keras is a leading open-source Python library written for constructing neural networks and machine learning projects. It can run on Deeplearning4j, MXNet, Microsoft Cognitive Toolkit (CNTK), Theano or TensorFlow. It offers almost all standalone modules including optimizers, neural layers, activation functions, initialization schemes, cost functions, and regularization schemes. It makes it easy to add new modules just like adding new functions and classes. As the model is already defined in the code, you don’t need to have a separate model config files.

Keras makes it simple for machine learning beginners to design and develop a neural network. Keras Python also deals with convolution neural networks. It includes algorithms for normalization, optimizer, and activation layers. Instead of being an end-to-end Python machine learning library, Keras functions as a user-friendly, extensible interface that enhances modularity & total expressiveness.

Since its arrival in 2007, Theano has captured the Python developers and researchers of ML and AI.

At the core, it is a well-known scientific computing library that allows you to define, optimize as well as evaluate mathematical expressions, which deals with multidimensional arrays. The fundamental of several ML and AI applications is the repetitive computation of a tricky mathematical expression. Theano allows you to make data-intensive calculation up to a hundred times faster than when executing on your CPU alone. Additionally, it is well optimized for GPUs, which offers effective symbolic differentiation and includes extensive code-testing capabilities.

When it comes toperformance, Theano is a great Python machine learning library as it includes the ability to deal with computations in large neural networks. It aims to boost development time and execution time of ML apps, particularity in deep learning algorithms. Only one drawback of Theano in front of TensorFlow is that its syntax is quite hard for the beginners.

Scikit-learn is another prominent open-source Python machine learning library with a broad range of clustering, regression and classification algorithms. DBSCAN, gradient boosting, random forests, vector machines, and k-means are a few examples. It can interoperate with numeric and scientific libraries of Python like NumPy and SciPy.

It is a commercially usable artificial intelligence library. This Python library supports both supervised as well as unsupervised ML. Here is a list of the premier benefits of Scikit-learn Python that makes it one among the most preferable Python libraries for machine learning:

Have you ever thought why PyTorch has become one among the popular Python libraries for machine learning in such a short time?

PyTorch is a production-ready Python machine-learning library with excellent examples, applications and use cases supported by a strong community. This library absorbs strong GPU acceleration and enables you to apply it from applications like NLP. As it supports GPU and CPU computations, it provides you with performance optimization and scalable distributed training in research as well as production. Deep neural networks and Tensor computation with GPU acceleration are the two high-end features of the PyTorch. It includes a machine learning compiler called Glow that boosts the performance of deep learning frameworks.

NumPy or Numerical Python is linear algebra developed in Python. Why do a large number of developers and experts prefer it to the other Python libraries for machine learning?

Almost all Python machine-learning packages like Mat-plotlib, SciPy, Scikit-learn, etc rely on this library to a reasonable extent. It comes with functions for dealing with complex mathematical operations like linear algebra, Fourier transformation, random number and features that work with matrices and n-arrays in Python. NumPy Python package also performs scientific computations. It is widely used in handling sound waves, images, and other binary functions.

In machine learning projects, a substantial amount of time is spent on preparing the data as well as analyzing basic trends & patterns. This is where the Python Pandas receives machine learning experts’ attention. Python Pandas is an open-source library that offers a wide range of tools for data manipulation & analysis. With this library, you can read data from a broad range of sources like CSV, SQL databases, JSON files, and Excel.

It enables you to manage complex data operation with just one or two commands. Python Pandas comes with several inbuilt methods for combining data, and grouping & filtering time-series functionality. Overall, Pandas is not just limited to handle data-related tasks; it serves as the best starting point to create more focused and powerful data tools.

Finally, the last library in the list of Python libraries for machine learning and AI is Seaborn – an unparalleled visualization library, based on Matplotlib’s foundations. Both storytelling and data visualization are important for machine learning projects, as they often require exploratory analysis of datasets to decide on the type of machine learning algorithm to apply. Seaborn offers high-level dataset based interface to make amazing statistical graphics.

With this Python machine learning library, it is simple to create certain types of plots like time series, heat maps, and violin plots. The functionalities of Seaborn go beyond Python Pandas and matplotlib with the features to perform statistical estimation at the time of combining data across observations, plotting and visualizing the suitability of statistical models to strengthen dataset patterns.

Here are the details of Github activities for each of the Python libraries for machine learning discussed above:

These libraries are extremely valuable when you’re working on machine learning projects as it saves time and further provides explicit functions that one can build on. Among the outstanding collection of Python libraries for machine learning, these are the best libraries, which are worth considering them. With the help of these Python machine learning libraries, you can introduce high-level analytical functions, even with minimal knowledge of the underlying algorithms you are working with.