10 Best Python Libraries for Machine Learning Every Developer Should Know

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1. NumPy

NumPy is a very popular python library for large multi-dimensional array and matrix processing, with the help of a large collection of high-level mathematical functions. It is very useful for fundamental scientific computations in Machine Learning. Almost all Python machine-learning packages like Mat-plotlib, SciPy, Scikit-learn, etc rely on this library to a reasonable extent.

2. SciPy

SciPy is a popular python library for machine learning which stand for Scientific Python. SciPy in Python is an open-source library used for solving mathematical, scientific, engineering, and technical problems. It allows users to manipulate the data and visualize the data using a wide range of high-level Python commands. SciPy is built on the Python NumPy extention.

3. Scikit-learn

Sklearn, short for scikit-learn, is a Python library for building machine learning models. Scikit-learn is among the most popular open-source machine learning libraries in the world for classical ML algorithms. It is built on top of two basic Python libraries, viz., NumPy and SciPy.Scikit-learn supports most of the supervised and unsupervised learning algorithms. Scikit-learn can also be used for data-mining and data-analysis.

4. Theano33

Theano is a python machine learning library that is used to define, evaluate and optimize mathematical expressions involving multi-dimensional arrays in an efficient manner. Theano can work on Graphics Processing Unit (GPU) and CPU.

5. Tensor Flow

TensorFlow is a very popular open-source library for high performance numerical computation developed by the Google Brain team in Google. TensorFlow is an end-to-end python machine learning library for performing high-end numerical computations. TensorFlow can handle deep neural networks for image recognition, handwritten digit classification, recurrent neural networks, NLP (Natural Language Processing), word embedding and PDE (Partial Differential Equation).

6. Keras

Keras is a very popular Machine Learning library for Python. It is a high-level neural networks API capable of running on top of TensorFlow, CNTK, or Theano. 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.

7. PyTorch

PyTorch is a production-ready Python machine-learning library with excellent examples, applications and use cases supported by a strong community. It has an extensive choice of tools and libraries that supports on Computer Vision, Natural Language Processing(NLP) and many more ML programs. PyTorch can smoothly integrate with the python data science stack, including NumPy. You will hardly make out a difference between NumPy and PyTorch.

8. Pandas

Pandas is a popular Python library for data analysis. It is not directly related to Machine Learning. 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.

9. Matplotlib

Matpoltlib is a very popular Python library for data visualization. Like Pandas, it is not directly related to Machine Learning. It particularly comes in handy when a programmer wants to visualize the patterns in the data. It is a 2D plotting library used for creating 2D graphs and plots. It works by using standard GUI toolkits like GTK+, wxPython, Tkinter, or Qt to provide an object-oriented API that helps programmers to embed graphs and plots into their applications.

10. Seaborn Python

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. 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.

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