Best ML libraries for python in 2023

Best ML libraries for python provide a range of tools and functions to perform tasks such as data preprocessing, feature extraction, model training, and model evaluation. Machine Learning (ML) libraries in Python refer to collections of pre-written code that allow developers and data scientists to quickly and easily implement machine learning algorithms in their projects.

By using these pre-written libraries, developers and data scientists can save a significant amount of time and effort in developing their own machine learning algorithms from scratch. This enables them to focus on the specific needs of their projects, rather than the underlying mechanics of machine learning.

Best ML libraries for python

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Best ML libraries for python

1. TensorFlow 

The Google Brain team created the open-source ML library known as TensorFlow in 2015. Developers and researchers use it extensively to construct and train deep learning models like artificial neural networks. To build and train neural networks for many applications, including image identification, natural language processing, speech recognition, and more, TensorFlow offers a variety of tools and features.

TensorFlow allows users to define and run computational graphs. A computational graph is a way of representing a neural network or any other machine learning model as a directed acyclic graph, where the nodes represent mathematical operations and the edges represent data inputs and outputs. This allows TensorFlow to efficiently execute complex mathematical operations on large datasets by breaking them down into smaller, more manageable operations.

TensorFlow supports both CPU and GPU processing, which allows it to perform computations in parallel and significantly speed up the training of deep learning models. TensorFlow also provides a high-level API called Keras, which simplifies the process of building and training deep learning models, making it more accessible to developers and researchers who may not have a strong background in machine learning.



2. Matplotlib

Matplotlib is a widely used data visualization library in Python. It provides a range of tools and functions for creating various types of static, animated, and interactive visualizations. Matplotlib is often used for creating plots, histograms, bar charts, scatterplots, line charts, and other visualizations of data.

Matplotlib is highly customizable, allowing users to adjust the color, style, and layout of their visualizations to match their specific needs. It also supports a range of file formats, including PNG, PDF, SVG, and EPS, allowing users to export their visualizations for use in presentations, reports, and publications.

Matplotlib is built on NumPy, another popular Python library for numerical computing. This makes it easy to integrate Matplotlib with other data analysis libraries in Python. Matplotlib is also widely used in the scientific community for visualizing data in research papers and presentations.


3. Pandas

Pandas is a popular open-source data analysis library for the Python programming language. It is amongst the top ML libraries for python It is designed to provide powerful and easy-to-use data structures and functions for working with structured data.

Being the amongst best ML libraries for python, Pandas provides two main data structures: Series and DataFrame. A Series is a one-dimensional array-like object that can hold any data type, such as integers, strings, or floats. A DataFrame is a two-dimensional table-like structure that can hold multiple columns of data, each with a different data type.

Pandas provides a range of tools and functions for data manipulation and analysis. These include data cleaning and preparation functions, data selection and filtering functions, data merging and joining functions, and data visualization functions.

Pandas is widely used in the data science community and is a popular tool for data analysis, data exploration, and data visualization. It is often used in conjunction with other Python libraries such as NumPy, Matplotlib, and Scikit-learn to perform data analysis tasks.

Pandas python


4. NumPy

NumPy (Numerical Python) is another specific open-source numerical computing library and the one of the best ML libraries for Python. It provides a powerful N-dimensional array object, as well as a range of tools and functions for working with large, multi-dimensional arrays and matrices. NumPy is built on top of the C programming language, which allows it to perform computations much faster than pure Python code.

NumPy’s array object is similar to a list in Python, but it provides more efficient storage and access to numerical data. It supports a range of operations such as element-wise arithmetic, matrix multiplication, and linear algebra operations. NumPy also provides tools for data reshaping, indexing, and broadcasting, which allows users to perform complex numerical computations with ease.

NumPy is widely used in scientific computing, data analysis, and machine learning applications due to its fast and efficient numerical computation capabilities. It is a fundamental library for many other popular Python libraries for data analysis, such as Pandas, Matplotlib, and Scikit-learn.


5. PyTorch

PyTorch is an open-source and one of the best ML libraries for Python developed by Facebook’s AI research team. It is primarily designed to support deep learning research and development. PyTorch provides a range of tools and functions for building and training deep neural networks.

PyTorch is built on top of the Torch library, which is written in C and provides efficient numerical computation capabilities. PyTorch provides a flexible and intuitive interface for building and training deep learning models, allowing developers to easily experiment with different network architectures and hyperparameters.

PyTorch is widely used in the research community and in industry for building and training deep learning models for various applications, such as image recognition, natural language processing, and speech recognition.


Few things to keep in mind while using these best ML libraries in python:

  1. Understand the underlying concepts: Before using an ML library, it’s important to have a good understanding of the underlying concepts of machine learning. This includes knowledge of statistical concepts such as probability, regression, and clustering, as well as algorithms such as decision trees, support vector machines, and neural networks.
  2. Choose the right library for your task: There are some of the best ML libraries for Python, each with its own strengths and weaknesses. It’s important to choose the right library for your task, based on factors such as the complexity of the problem, the size of the data set, and the type of model you want to build.
  3. Preprocess your data: ML algorithms are only as good as the data they’re trained on. It’s important to preprocess your data to ensure that it’s clean, complete, and in the right format. This may involve tasks such as cleaning up missing data, scaling features, and encoding categorical variables.
  4. Split your data into training and test sets: To evaluate the performance of your ML model, you need to test it on data that it hasn’t seen before. This requires splitting your data into training and test sets. Typically, you’ll use a larger portion of the data for training and a smaller portion for testing.
  5. Tune your hyperparameters: Most ML algorithms have hyperparameters that need to be tuned to achieve optimal performance. This may involve trying different values for hyperparameters such as learning rate, regularization strength, and number of hidden layers.
  6. Evaluate your model’s performance: Once you’ve trained your model, you need to evaluate its performance on the test set. This may involve calculating metrics such as accuracy, precision, recall, and F1 score. It’s important to avoid overfitting your model to the training data, as this can result in poor performance on new data.
  7. Visualize your results: Visualization can be a powerful tool for understanding the performance of your ML model. This may involve plotting learning curves, confusion matrices, or ROC curves, to gain insights into how your model is performing and where it can be improved.



This post gives details on the best ML libraries for python which can be used for analysis, visualization and much more. Appreciate you for reading.


Krisha Lakhani
I'm Krisha Lakhani, an Electronics Engineer with a minor in Data Science, my passion for research and analysis inspires me to create technical content. By producing content for the technical teams I was a member of in college, I began to practice research and writing. This experience of working in a team professionally led to the development of this passion of mine.