Best Python libraries for Machine Learning

Machine learning is a science of programming the computer by which they can learn from different types of data. According to machine learning's definition of Arthur Samuel - "Field of study that gives computers the ability to learn without being explicitly programmed". The concept of machine learning is basically used for solving different types of life problems.

In previous days, the users used to perform tasks of machine learning by manually coding all the algorithms and using mathematical and statistical formulas.

This process was time-consuming, inefficient, and tiresome compared to Python libraries, frameworks, and modules. But in today's world, users can use the Python language, which is the most popular and productive language for machine learning. Python has replaced many languages as it is a vast collection of libraries, and it makes work easier and simpler.

In this tutorial, we will discuss the best libraries of Python used for Machine Learning:

• NumPy
• SciPy
• Scikit-learn
• Theano
• TensorFlow
• Keras
• PyTorch
• Pandas
• Matplotlib

NumPy

NumPy is the most popular library in Python. This library is used for processing large multi-dimensional array and matrix formation by using a large collection of high-level mathematical functions and formulas. It is mainly used for the computation of fundamental science in machine learning. It is widely used for linear algebra, Fourier transformation, and random number capabilities. There are other High-end libraries such as TensorFlow, which user NumPy as internal functioning for manipulation of tensors.

Example:

Output:

Inner product of vectors: 222 Matrix and Vector product: [ 68 156] Matrix and matrix product: [[22 34] [46 74]] 

SciPy

SciPy is a popular library among Machine Learning developers as it contains numerous modules for performing optimization, linear algebra, integration, and statistics. SciPy library is different from SciPy stack, as SciPy library is one of the core packages which made up the SciPy stack. SciPy library is used for image manipulation tasks.

Example 1:

Output:

r (K, domain_1, 1)) Output: [[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14] [15 16 17 18 19] [20 21 22 23 24] [25 26 27 28 29] [30 31 32 33 34] [35 36 37 38 39] [40 41 42 43 44]] KK [[ 0. 1. 2. 3. 0.] [ 5. 6. 7. 8. 3.] [10. 11. 12. 13. 8.] [15. 16. 17. 18. 13.] [20. 21. 22. 23. 18.] [25. 26. 27. 28. 23.] [30. 31. 32. 33. 28.] [35. 36. 37. 38. 33.] [ 0. 35. 36. 37. 38.]] 

Example 2:

Output:

Scikit-learn

Scikit-learn is a Python library which is used for classical machine learning algorithms. It is built on the top of two basic libraries of Python, that is NumPy and SciPy. Scikit-learn is popular in Machine learning developers as it supports supervised and unsupervised learning algorithms. This library can also be used for data-analysis, and data-mining process.

Example:

Output:

DecisionTreeClassifier() precision recall f1-score support 0 1.00 1.00 1.00 50 1 1.00 1.00 1.00 50 2 1.00 1.00 1.00 50 accuracy 1.00 150 macro avg 1.00 1.00 1.00 150 weighted avg 1.00 1.00 1.00 150 [[50 0 0] [ 0 50 0] [ 0 0 50]] 

Theano

Theano is a famous library of Python, which is used for defining, evaluating, and optimizing mathematical expressions, which also efficiently involves multi-dimensional arrays.

It is achieved by optimizing the utilization of CPU and GPU. As machine learning is all about mathematics and statistics, Theano makes it easy for the users to perform mathematical operations.

It is extensively used for unit-testing and self-verification for detecting and diagnosing different types of errors. Theano is a powerful library that can be used on a large scale computationally intensive scientific project. It is a simple and approachable library, which an individual can use for their projects.

Example:

Output:

array([[0.5, 0.71135838], [0.26594342, 0.11420192]]) 

TensorFlow

TensorFlow is an open-source library of Python which is used for high performance of numerical computation. It is a popular library, which was developed by the Brain team in Google. TensorFlow is a framework that involves defining and running computations involving tensors. TensorFlow can be used for training and running deep neural networks, which can be used for developing several Artificial Intelligence applications.

Example:

Output:

[ 2 12 30 56] 

Keras

Keras is a high-level neural networking API, which is capable of running on top of TensorFlow, CNTK and Theano libraries. It is a very famous library of Python among Machine learning developers. It can run without a glitch on both CPU and GPU. IT makes it really easy and simple for Machine Learning beginners and for designing a Neural Network. It is also used for fast prototyping.

Example:

Output:

x_train shape: (60000, 28, 28, 1) 60000 Training samples 10000 Testing samples Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 26, 26, 32) 320 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64) 0 _________________________________________________________________ flatten (Flatten) (None, 1600) 0 _________________________________________________________________ dropout (Dropout) (None, 1600) 0 _________________________________________________________________ dense (Dense) (None, 10) 16010 ================================================================= Total params: 34,826 Trainable params: 34,826 Non-trainable params: 0 _________________________________________________________________ 

PyTorch

PyTorch is also an open-source Python library for Machine Learning based on Torch, which is implemented in C language and used for Machine learning. It has numerous tools and libraries supported on the computer version, Natural Language Processing (NLP) and many other Machine Learning programs. This library also allows users to perform computational tasks on Tensor with GPU acceleration.

Example:

Output:

0 35089116.0 1 33087792.0 2 42227192.0 3 56113208.0 4 61125684.0 5 45541204.0 6 21011108.0 7 6972017.0 8 2523046.5 9 1342124.5 10 950067.5625 11 753290.25 12 620475.875 13 519006.71875 14 437975.9375 15 372063.125 16 317840.8125 17 272874.46875 18 235348.421875 . . . 497 7.426088268402964e-05 498 7.348413055296987e-05 499 7.258950790856034e-05 

Pandas

Pandas is a Python library that is mainly used for data analysis. The users have to prepare the dataset before using it for training the machine learning. Pandas make it easy for the developers as it is developed specifically for data extraction. It has a wide variety of tools for analysing data in detail, providing high-level data structures.

Example:

Output:

 Countries capital Currency population 0 Bhutan Thimphu Ngultrum 20.40 1 Cape Verde Praia Cape Verdean escudo 143.50 2 Chad N'Djamena CFA Franc 12.52 3 Estonia Tallinn Estonia Kroon; Euro 135.70 4 Guinea Conakry Guinean franc 52.98 5 Kenya Nairobi Kenya shilling 76.21 6 Libya Tripoli Libyan dinar 34.28 7 Mexico Mexico City Mexican peso 54.32 

Matplotlib

Matplotlib is a Python library that is used for data visualization. It is used by developers when they want to visualize the data and its patterns. It is a 2-D plotting library that is used to create 2-D graphs and plots.

It has a module pyplot which is used for plotting graphs, and it provides different features for control line styles, font properties, formatting axes and many more. Matplotlib provides different types of graphs and plots such as histograms, error charts, bar charts and many more.

Example 1:

Output:

Example 2:

Output:

Conclusion

In this tutorial, we have discussed about different libraries of Python which are used for performing Machine learning tasks. We have also shown different examples of each library.