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Machine learning has been one of the biggest applications that happened in the last decade, steering innovation in multiple industries. Of course, Python is one of the most favored languages among data scientists and developers. An extremely rich ecosystem of libraries available for Python simplifies the development related to machine learning models and consequently boosts productivity. In this article, we will consider the best Machine Learning libraries for Python in 2024. With this, we will review the features and benefits that each of these libraries has.
TensorFlow is among the most common machine learning libraries in use. Developed by Google Brain, it is a library with very promising functionality. It is very suitable for a beginner and, at the same time, a well-experienced developer. This library would be helpful for most machine learning tasks and, in particular, for neural networks and deep learning in association with reinforcement learning. The debate for TensorFlow vs PyTorch continues to rage but TensorFlow comes on top with its unique features.
There is room to run it on many CPUs and GPUs, which makes TensorFlow work well for large-scale models. It is very flexible in other words, from the simplest to the most complex neural network, all kinds of different machine-learning models can run in it. It was developed keeping in view the mobile and embedded devices and trying to deploy models on a maximum number of platforms.
It has been used in construction for image recognition, and a variety of complex and sophisticated systems about speech recognition models.
Out of all, a few such keys, popular libraries include PyTorch, designed at present by Facebook's AI Research lab. It is best known for its dynamic computation graph and how easy it is to work with. It is a much-preferred library among research and prototyping practices because of its flexible and intuitive design. PyTorch allows changing the behavior of the network at runtime, which is a very good tool for implementing some tasks like debugging or experimenting.
If you are wondering, if PyTorch v/s TensorFlow, is the best option for data scientists, then maybe these key features of Pytorch will convince you to decide it for your career. Both TensorFlow and PyTorch provide dynamic computation graphs, that enable their users to follow up on the architecture at runtime. More flexibility in building your models. PyTorch has a friendlier interface, and for that reason, this generally makes it a bit easier for a developer to ramp up. Among others, including TorchVision for computer vision tasks.
PyTorch is in the domain of research for computer vision, natural language processing, etc., amongst academics and industrialists.
Scikit-learn is one of the most famous libraries for the implementation of classical machine learning algorithms. It also includes tools that are simple and efficient for data mining and data analysis. Scikit-learn is developed on top of NumPy, SciPy, and MatPlotLib, so it's easy to integrate with other Python libraries. You can find useful resources for learning Scikit online for free.
Huge packs of algorithms are supported for classification, regression, clustering, and dimensionality reduction. Integration with most Python scientific libraries is easy. Its API is uniform and easy, which will help in the quick implementation of the machine-learning models.
Scikit-learn is one of the most common packages used for making predictive models. Besides, it has been used for a great deal of data preprocessing.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow. Microsoft Cognitive Toolkit or Theano is intended to speed up experimentation with deep neural networks.
Keras has a simple and consistent API, it minimizes the general complexity of building Neural Networks. Multiple different modules are available for use including but not limited to layers, objectives, activation functions, optimizers, and even specialized tools. It runs seamlessly on top of multiple deep-learning frameworks.
Keras fits for quick prototyping it uses deep learning models and research with neural networks.
Extreme Gradient Boosting is a much-upgraded version of gradient boosting. It is gradually gaining popularity and has now become one of the favorite libraries used for many competitions at Kaggle and also by many data scientists. This is likely because it is easy to manipulate with high accuracy and efficiency.
Implementation of gradient boosting on big datasets is performed efficiently. The model supports both regression and classification. L1 and L2 regularization is added to prevent data overfitting.
One of the other main reasons for using it is that XGBoost is very popular for predictive modeling within machine learning competitions and other real-world applications.
These days, machine learning has become a highly dynamic field, and here, Python, which has the backing of very exhaustive libraries, is leading. Tensorflow, PyTorch, Scikit-learn, Keras, and XGBoost are the libraries that play an equivalent role to the pulsing blood for data science. What makes each library special is the features and strengths that orient them specifically for different jobs and projects. Powered with this toolbox, data scientists and developers can build complicated machine learning models and break into a niche amid the ever-updating tech world.
Flexibility in building and training machine learning models, mostly deep learning and neural networks. It provides excellent support for different kinds of tasks but most especially for image recognition, natural language processing, and reinforcement learning. In addition to these, it has good scalability and flexibility.
It is that dynamic computation graph that separates PyTorch from TensorFlow, though, offering more flexibility for the use cases I described. This is why it is used much more mostly by users implementing their research and experimentation. Secondly, users are especially biased toward TensorFlow due to the high scalability when using platforms in a deployed environment because supporting several different platforms was a priority in its original functionalities.
Scikit-learn is useful for the more classical parts of machine learning, for example, classification, regression, clustering, and dimension reduction. It contains rich algorithms, so it's very handy for use, which is nice when combined with other scientific Python libraries.
One of the greatest features of Keras is that it has an extremely user-friendly API, through which building a neural network is easy and quick to prototype. Abstractions are very high, running on top of a range of deep learning frameworks, hence, making it easy for prototyping and testing in the best possible way.
As of the last statistics, currently, XGBoost does well over other implementations by a high number and is very effective when it comes to running gradient-boosting algorithms. It also allows a regularization technique to prevent overfitting in many classification and regression problems that data scientists and Kaggle lovers hold dear to their hearts.
