Getting into Python Machine Learning is not an easy task nowadays. With a wide range of projects available, you might be confused over which is the best in regards to fast paced development of Machine Learning.
The field is growing rapidly and with that, there is a need to keep up with the progress to grasp the advances of ML efficiently. The best way is to take part in different open source projects and use advanced tools along with ML professionals.
Following are the top 9 open source Machine Learning projects in Python:
TensorFlow was developed by engineers and researchers of Google Brain Team in Google’s Machine Intelligence research organization. It was designed to research in machine learning more effectively. Also, it helped greatly in moving from research to production system.
It has all the advanced level open source projects to work on.
This system facilitates defining, optimizing, and evaluating mathematical expressions while dealing with multidimensional arrays. The contributors to Theano open source project have been highly increased over the past few years.
If you want to dive into linear algebra and matrices specifically, Theano has much to offer.
This deep learning framework is designed keeping speed and modularity in mind. Through effective projects, you can learn a great deal out of Python machine learning.
Scikit is BSD licensed open source project built on Numpy, Scipy, and matplotlib. Being commercially usable, it is simple yet efficient for data mining and data analysis.
Written in Python language, Keras is a high-end neural networks API. With good number of contributors, it is capable of excelling TensorFlow and Theano as well. Offering open source projects, it helps in mastering Python ML skills.
PyTorch is one the most dynamic neural networks in Python. It comes with strong GPU acceleration. If you are looking for an open source Python ML project on GPU, this is a good option to consider.
Gensim is a freely accessible Python library that has different features to offer. The most well-known are scalable statistical semantics, analyzing plain text documents for semantic structures as well as recover semantically corresponding documents.
Chainer is another Python-based open source framework. It comes with deep learning models such as recurrent neural networks and variational auto-encoders. If you want to get into some deep learning models, Chainer is a great option as it is highly flexible and intuitive.
PyLearn2 is built on Theano. When you write PyLearn2 plugins such as new models and algorithms through mathematical expressions, it is Theano who optimizes them and compile to your backend be it GPU or CPU.
Where to learn faster?
Both are great sources but when you get into one-on-one LinkedIn Learning vs Pluralsight comparison, Pluralsight is the ultimate winner.
So get your hands on it and sign up now to access open source machine learning projects in Python.
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