Machine Learning is a branch of computer science that focuses on giving computers the ability to learn without being explicitly programmed. It’s mostly used for predictive modeling, but it can also be used for other types of data analysis.
The algorithms, mathematical formulas, and statistical calculations were all manually coded in the past when Machine Learning was new. As a result, the procedure was laborious, time-consuming, and ineffective. But thanks to different Machine Learning Python libraries, frameworks, and modules, it is now a lot simpler and more effective than it was in the past. Python is currently one of the most widely used programming languages for Machine Learning. It has overtaken several businesses, partly due to its enormous library collection.
Python libraries in Machine Learning are software packages that help you get things done. There are thousands of Machine Learning modules in Python available, and you can use them to manipulate data, perform calculations and create visualizations.
What Are Python libraries, and How Do They Help?
Python libraries are sets of pre-written code that help you accomplish specific tasks. For example, if you want to compute the average of a list of numbers, there’s already a library for doing just that (it’s called NumPy).
There are also lots of Machine Learning libraries available in Python. But which one should you use? In this article, we’ll go over the top 10 Python Machine Learning libraries so that you can make an informed decision about which one is right for your project.
The following list of Python Machine Learning libraries was ranked based on a combination of factors, including popularity and usability. We also considered the number of contributors to each library’s GitHub repository and its trending popularity over time (based on Google search volume).
This is a partial list of all Python Machine Learning libraries. For example, we didn’t include libraries focusing on neural networks (like PyTorch) or deep learning (for example, TensorFlow).
Best Python Libraries for Machine Learning to Know in 2022
You can use these Python libraries for Machine Learning in 2022:
- TensorFlow is the most popular library for Data Science and Machine Learning. It provides many tools that allow you to build, train, and deploy production-ready models.
- PyTorch is an open-source Machine Learning library that focuses on deep learning research. It was developed by Facebook’s AI Research group and comes with GPU acceleration support so that it can run efficiently on large sets of training data. It also supports dynamic graphs, making it easy to modify your neural network’s structure during runtime without recompiling the model code again.
- Keras is a high-level neural networks API that runs on top of other low-level libraries like TensorFlow or Theano/CNTK but comes with pre-built operations and layers (elements) such as convolutional layers, pooling layers, etc.
- Pylearn2 is another powerful Python-based Machine Learning library that supports many different Machine Learning algorithms such as decision trees, logistic regression models, etc.
- Theano is one of those libraries designed specifically for efficient computation involving multi-dimensional arrays or matrices (mathematical objects representing data). It uses vectorization techniques so that computations are performed in parallel using CPUs or GPUs rather than sequentially using one CPU core, making computations much faster than NumPy.
- DL4J is written in Java, and it supports several deep learning models, including Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), etc. It also comes with a rich ecosystem of pre-trained models that can be used for common tasks such as image classification and speech recognition.
- SciPy is a particularly well-liked library among those interested in Machine Learning. It is because SciPy includes several modules for optimization, linear algebra, integration, and statistics.
- Scikit-learn is one of the most favored ML libraries for traditional ML algorithms. It is constructed on top of NumPy and SciPy, two fundamental Python libraries. Scikit-learn supports the majority of supervised and unsupervised learning algorithms.
- Pandas is a well-liked Python package for data analysis. Since we are aware, the dataset needs to be ready before training. Pandas library is helpful in this situation because it was created primarily for data preprocessing and extraction. It offers a large range of tools for data analysis as well as high-level data structures.
- Matplotlib is another well-liked Python library for data visualization. It is not directly associated with Machine Learning, like Pandas, and it is especially helpful when a coder needs to see the data’s patterns. It is a library for 2D charting used to produce 2D graphs and plots.
In this article, we have discussed the top 10 Python libraries for Machine Learning. Some of the libraries include scikit-learn, TensorFlow, and Keras. We have also provided a brief description of each library so that you can make an informed decision when choosing one for your use case. For more information on Machine Learning Python libraries to boost your career, PG Certificate Program in Data Science and Machine Learning is worth considering.