The practice of data science requires the use of machine learning frameworks extensively. Now, this could be for many reasons but largely to automate their processes that drive their business forward.
Framework focused solutions mean data scientists don’t always need to have extensive experience in coding and programming languages, and can instead use their expertise in solving bigger problems on their table. Reports show that 85% of data pros have used at least one ML framework.
Top Frameworks used by Data Scientists
If you are on your path to becoming a data savy, here’s a list of the 10 best open source ML frameworks available in the market that are reportedly the most used by data science professionals.
Tensorflow is an open-source machine learning library developed at Google for numerical computation using data flow graphs is arguably one of the best, with Gmail, Uber, Airbnb, Nvidia, and lots of other prominent brands using it. It’s handy for creating and experimenting with deep learning architectures, and its formulation is convenient for data integration such as inputting graphs, SQL tables, and images together.
You Might Like: Introduction to TensorFlow in Python Free Course
Scikit-learn is a very popular open-source machine learning library for the Python programming language. With constant updations in the product for efficiency improvements coupled with the fact that its open-source makes it a go-to framework for machine learning in the industry.
Keras is an open-source neural network library written in Python. It is capable of running on top of other popular lower-level libraries such as Tensorflow, Theano & CNTK. This one might be your new best friend if you have a lot of data and/or you’re after the state-of-the-art in AI: deep learning.
Pandas is yet another open-source software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. Pandas works well with incomplete, messy, and unlabeled data and provides tools for shaping, merging, reshaping, and slicing datasets.
5. Spark MLib
Spark MLib is a popular machine learning library. A per survey, almost 6% of the data scientists use this library. This library has support for Java, Scala, Python, and R. Also you can use this library on Hadoop, Apache Mesos, Kubernetes, and other cloud services against multiple data sources.
PyTorch is developed by Facebook’s artificial intelligence research group and it is the primary software tool for deep learning after Tensorflow. Unlike TensorFlow, the PyTorch library operates with a dynamically updated graph. This means that it allows you to make changes to the architecture in the process.
Matplotlib is a plotting library for Python, a library mostly used for data visualization by plotting histograms, scatterplot, 3D plot, etc., and also serves as a numerical extension to the Numpy library. It’s the de facto visualization library used in every data science test case in Python as it makes visualizations easy and interactive giving you the power to produce histograms, scatterplot, 3D plot, image plot, bar charts, power spectra, and many more.
Numpy is an open-source library that gives programmers the versatility to work with matrices and multi-dimensional arrays. It’s the standard library for scientific computing in Python and provides powerful tools for integrating C/C++ and Fortran code. Check out the NumPy tutorial and NumPy practical examples.
Seaborn is an open-source Python data visualization library based on matplotlib. The main focus of this package is on the visualization of statistical models. visualizations which include heat maps, those which summarize the data but still depict the overall distributions.
Theano Python library is for numerical computation and is similar to Numpy. Some libraries such as Pylearn2 use Theano as their base component for mathematical computation. Theano helps you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently
Here are some other frameworks worth considering.