6 important Python Libraries for Machine Learning and Data Science

In this guide, we’ll show the must know Python libraries for machine learning and data science.

Python and Machine Learning (ML) are the two most . And Python is the most popular programming language for machine learning. One of the reasons is Python’s extensive package availability, which makes ML easier.

What are the best Python libraries for Machine Learning?

As experienced data scientists, we’ve compiled this list with references to tutorials and examples.

You’ll know exactly where to dive deeper into your Python studying for ML.Let’s get started!

Before we start

If you are brand new to Python, please take our . We can’t learn these libraries without strong foundation knowledge of Python.

If you are new to Machine Learning, please start from .

Machine learning has different algorithms (types), which focus on solving different problems. With the basics of ML, you’ll learn better about each Python ML library since it often targets various tasks.Great!Now we are ready to look at the top 6 Python packages/libraries for machine learning and data science.

NumPy

 is the fundamental package for scientific computing in Python. Most of the other Python libraries for machine learning are built upon NumPy. You can’t do data science using Python without NumPy.

Some of NumPy’s functionalities include:

• multi-dimensional arrays and matrices creation.
• comprehensive mathematical functions.
• random number generators.
• linear algebra routines.
• discrete Fourier transforms.
• fast vectorized operations.

Further Reading: 
This is a beginner-friendly tutorial of Python NumPy (arrays) basics for data science. Learn this essential library with examples.

Pandas

 is the foundation library for data analysis and manipulation.

If you are new to data science, you might wonder what it has to do with ML?

Before training ML algorithms/models, the data needs to be processed and cleaned. This process often takes the majority of time for machine learning practitioners. And pandas makes this process a lot easier for structured datasets.

pandas offers powerful data structures like DataFrames. We can use it to:

• import or write data between Python and various sources such as CSV files, and SQL databases.
• analyze data based on descriptive statistics.
• group by with flexibility.
• manipulate and transform the datasets.

Further Reading: 
This complete tutorial helps you get hands-on experience with data analysis and manipulation. Learn more about the essential functions/methods of Python pandas library for machine learning.

Seaborn

 is a popular Python library for making statistical data visualizations. It is based on  and also integrated with  data structures.

Seaborn is especially useful for exploring and understanding data. Some of the functionalities that seaborn offers:

• options for visualizing univariate and bivariate distributions.
• options for visualizing numerical and categorical variables.
• automatic estimation and plotting of linear regression models.
• ability to build complex visualizations such as multi-plot grids.

Further Reading: 

Unlock the power of seaborn by exploring an example dataset with Histograms, Heatmaps, Scatter plots, Barplots, etc.

Scikit Learn (Sklearn)

 is one of the most popular Python libraries for machine learning, which supports supervised and unsupervised learning. It provides tools for fitting models, preprocessing data, selecting and evaluating models, etc. It’s built on , , and libraries.

Some of the main features of scikit-learn are:

• fitting machine learning algorithms and models such as classification, regression, clustering.
• transforming and preprocessing the data.
• supporting machine learning pipeline integration.
• model evaluation, such as cross-validation.

Further Readings:

Read the official documentation for instructions on features.

Check out the detailed tutorial about Linear Regression, a foundation supervised predictive algorithm.

A simple example of applying the decision tree algorithm with Scikit-Learn.

TensorFlow and Keras

is an end-to-end open-source platform for machine learning, first developed and used by Google. It makes the ML model creation easier for both beginners and experts. It’s especially prevalent when building deep learning models. Deep learning has been particularly successful with text and image data, which are popular applications of machine learning.

( is a high-level API on top of TensorFlow for building and training deep learning models. It makes TensorFlow easier to use. Keras used to be a stand-alone framework but is supported in TensorFlow now. It can be used for prototyping, research, and production.Some of the commonly used TensorFlow’s features are:

• deep learning (deep neural networks).
• image processing.
• text analysis.
• reinforcement learning.

Further Readings:

Check the official documentation for the basics.

Learn how to build a deep learning model to classify the Yelp review data in Python step-by-step.

A machine learning time series analysis example with Python. See how to transform the dataset and fit LSTM with the TensorFlow Keras model.

Neural Networks have many hyperparameters, which makes it harder to tune. This is a practical guide to Hyperparameter Tuning with Keras TensorFlow in Python. Implement this machine learning technique to improve your model’s performance.

PyTorch

is a framework that competes with TensorFlow for developing deep learning models. This library has grown and is now more popular than TensorFlow in academia.

TensorFlow used to need developers to create and compile a static graph before being able to see the mathematical operations. While PyTorch used dynamic graphs that allowed users to detect errors more quickly. The research communities had entrenched with PyTorch by the time TensorFlow 2.0 was released with similar functionality.Yet, PyTorch is still not widely considered to be production-ready compared to TensorFlow, with the latter being more scalable.Today, both frameworks offer similar things, with PyTorch holding the ground in academia and TensorFlow trending in the industry.Some of the main applications of PyTorch include:

• computer vision.
• Natural Language Processing (NLP).
• reinforcement learning.

Further Reading: 

Other Python libraries for Machine Learning and Data Science

Besides the top 6 must-know libraries, there are also a couple of other popular Python libraries for machine learning.

NLTK

(Natural Language Toolkit) is a handy package for NLP tasks. Features include:

• tokenization.
• keyword searching.
• tagging.
• text classification.
• named entity detection.
• over 50 corpora such as Wordnet.

Further Reading: 

This is an application for the NLTK package on Indeed Job postings.

SciPy

is a set of modules for advanced mathematical operations on NumPy data. It is the foundation package for higher-level libraries such as . Some features include:

• Fourier transforms.
• optimization.
• signal processing.
• linear algebra.
• probability and statistics.
• image processing.

Further Reading: 

Matplotlib

is a comprehensive data visualization library in Python. Some features include:

- creating interactive plots.
- offering flexible customization of the plot.

It is a foundation library supporting , which is easier to use. But when we want to customize the plots more, matplotlib becomes necessary.

Further Reading: 

That’s it! We’ve covered all the essential Python libraries for Machine Learning and Data Science.Hope you get a better idea of where to continue your Python learning.Which Python package for ML will you learn first?Leave a comment for any questions you may have or anything else.

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