Top 5: Most Popular Machine Learning Libraries [Python]
Machine Learning algorithms requires a well-structured and well-tested ecosystem to allow developers to develop a model that perform well. And it takes too much time to develop such a model from scratch. But there are numerous Python libraries for machine learning to reduce development time. And as things are changing and developing so fast in this field, it’s hard to say which python library is the most popular for machine learning exactly. But here are top 5 the most popular machine learning libraries that are worth checking out:
Most Popular ML Libraries
“Scikit-learn” is a NumPy, SciPy and matplotlib based open source Python library for machine learning. It’s very popular among the ones who have just started to learn Machine Lierning. It offers simple and effective tools for things like data mining and data analysis. It supports most of the supervised and unsupervised machine learning algorithms (features various classification, regression and clustering algorithms).
TensorFlow is a popular open-source library created by the Google Brain team in Google for high-performance numerical computing. Tensorflow, as the name suggests, is a system for defining and running tensor computations. It can train and run deep neural networks which can be used to develop many AI and ML applications. It is widely used in analysis and implementation of deep learning.
Keras is another popular library for Python’s Machine Learning. It can run on both CPU and GPU seamlessly. It’s a high-level neural-networks API that can run on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. It’s really easy to develop and design a Neural Network & that’s why Keras is very popular among beginners in Machine Learning. It mostly focuses on user-friendliness, modularity and extensibility.
PyTorch is a popular Torch-based open-source Machine Learning library for Python. It is an open-source library which implemented in C with a Lua wrapper. It is developed primarily by the AI research group of Facebook. It has a wide range of tools and libraries supporting computer vision, NLP and many more ML programs. It allows developers to perform GPU accelerated computations on tensors and also enables computational graphs to be generated.
OpenCV is a Intel developed library that focuses primarily on real-time computer vision. It is mostly used in the area of Facial recognition, Gesture recognition, Human–computer interaction, Motion understanding, Object detection, Augmented reality etc. And to support some of the areas, OpenCV includes a statistical machine learning library that contains many classification algorithms like KNN, DNN, SVM, Random Forest, Decision Tree, Naive Bayes classifier etc. It also supports the deep learning frameworks TensorFlow, PyTorch and Caffe.
Other Mentions: Theano, XGBoost, Caffe & Gensim.