Best 7 Python Libraries for Machine Learning & AI
Since its release in 1991, Python’s popularity has skyrocketed than all other programming languages. Now Python is considered one of the most popular programming languages in software development field. Python is easy to understand, learn and intuitive.
One of Python’s important features is its open-source libraries. Like any other field, Python is shining in AI and Machine Learning. AI and ML are leading the technical sector today because of their terrific data analysis and data processing capabilities.
|Python Libraries
Python comes with a plethora of libraries. These libraries are a collection of helpful functions that allow us to write code without having to start from scratch and can be used repeatedly in different programs. These libraries are vast and continuously growing. There are a total number of more than 137000 libraries out there.
|Best Python libraries for AI and ML
There are many libraries in Python that makes a lot of machine learning and AI tasks easy. Here are top Python libraries for AI and ML and their prominent features.
| Python Library(AI & ML) | Features |
| 1.Numpy | Shape manipulation. Discretion of Fourier transformations. Statistical operations and linear algebra. Support for n-dimensional arrays. Data cleaning and manipulation. Random simulations. |
| 2.Pandas | Data alignment and handling of missing data. Merging and joining of datasets. Dataset reshaping and pivoting. Data filtration. Data manipulation and analysis. Indexing of the data. |
| 3.SciPy | User-friendly. Data visualization and manipulation. Scientific and technical analysis. Computes large data sets |
| 4.Theano | Built-in validation and unit testing tools. Fast and stable evaluations. Data-intensive calculations. High-performing mathematical computations. |
| 5.Matplotlib | High-quality diagrams, plots, histograms, graphs, etc. Intuitive and easy to use. GUI toolkit support. Map projections. Recognition of data patterns. |
| 6.TensorFlow | Flexible architecture and framework. Runs on a variety of computational platforms. Abstraction capabilities Manages deep neural networks. |
| 7.Scikit-learn | Data modeling. End-to-end Machine Learning algorithms. Model selection. Classification of data. Dimensionality reduction. Pre-processing of the data. |
Conclusion
There are many Python libraries dedicated to AI and ML that vary in size, quality, and diversity. These libraries help your artificial intelligence journey by improving data integrity and reduce human error.