Earlier it was a tedious task to instruct the application manually again & again. But machine learning has made it very easy and simple for humans to perform tedious and complicated tasks. Machine learning is a branch of computer science that makes a computer work without the interaction of humans being.

Artificial intelligence gives rise to recently developed software, Machine learning, and deep learning. Likewise, Machine learning has given us effective web search, self-driving cars, practical speech recognition, and much more.

Python has taken over the place of many languages. Because of its features and simplicity, it is the most popular programming language. Python is the most preferred language for machine learning because of a wide range of libraries. Now there is no need to code algorithms of machine learning from scratch.

Python libraries for machine learning made it possible for beginners to use it. Moreover, the algorithm used in machine learning includes maths and stats, which make it difficult to perform. Thanks to the developers who provided us with Python. It gives accuracy and a pattern where humans lack.

**Why python is the most preferred language for Machine learning?**

These are the features of python that made it the most preferred language for machine learning.

- It has a high-level and straightforward syntax.
- A large number of libraries
- Productive and high-quality language
- It has a smooth implementation.
- It is a part of natural language processing.

Python provides flexibility in implementing and learning machine learning. Python libraries for machine learning made it the best programming language.

**Best python libraries for machine learning**

Python has an exhaustive list of libraries. These are the top 10 Python libraries for machine learning.

- Numpy
- Scipy
- Scikit-learn
- Theano
- TensorFlow
- Keras
- PyTorch
- Pandas
- Matplotlib
- Sea born

Let’s discuss the python libraries in detail.

### Keras

Keras is an open-source python library, very popular for a machine language. It is a high-level neural network API that runs on top of Theano, Tensorflow, and CNTK. This python library can run on both CPU and GPU. Keras is fast and easy to use.

### Scikit-learn

It is free software built on Numpy, scipy, and matplotlib. It is the most useful tool for machine learning and statistical models such as classification, clustering, regression, etc. This is an open-source distribution under the BSD license. There are some uses of Scikit-learn in data analysis and data mining.

### Numpy

Programmers use this Python library for mathematical and logical operations on arrays. This is known as numerical python. It uses the high-level mathematical function. Moreover, Numpy is a popular library for huge multidimensional arrays and matrices.

It helps in performing scientific calculations in machine learning. It is most useful for random number capabilities, Fourier transform, and linear algebra. Besides, libraries like tensor flow use NumPy internally to handle tensors.

### Pandas

It is an open-source Python library. It is used for data analysis and manipulation. Time-series and Data frames are two structures of Pandas. Programmers use it to solve the problems of social science, finance, engineering, and statistics. Moreover, this python library is not directly related to machine language.

Pandas is built on top of Numpy and integrated well with a scientific computing environment. Moreover, it is the most powerful, flexible, and easy to use tool accessible in any language.

### Scipy

It is an open-source python library that works with Numpy arrays to provide convenient and efficient numerical integration and optimization. It also contains modules for algebra, mathematics, engineering, and statistics. Programmers can use also it to manipulate images. Moreover, it depends on Numpy to provide a fast N-dimensional array object.

### TensorFlow

Tensorflow is used for many tasks. But it mainly focuses on training and the influence of deep neural networks. It is a library of symbolic math based on differential programming and data flow. This is an end to end open software for machine learning.

TensorFlow is created by the google team for high-level numerical computation. Moreover, it involves defining and running calculations using tensors. Tensorflow is also useful for deep learning research and application.

### PyTorch

This python library for machine library is used for computer vision and natural processing language. It is an open-source library based on torch library. Pytorch is also useful for creating a computational graph. It also allows performing computation with GPU acceleration on tensors. In contrast, Python is in competition with TensorFlow being the best machine learning language. This also supports C++ with a C++ interface.

### Theano

Theono is the most useful Python library as it automatically removes the errors of logarithm and arithmetic function. Consequently, it is built on the top of the Numpy. It is popular for optimizing, defining, and evaluating mathematical expressions with the help of multidimensional arrays.

Theano gives competition to C as it works faster in solving large data amount problems. Theano is the best library for taking structures and converting them into codes. It works more quickly on GPU rather than CPU. It is designed for computation required for large neural network algorithms used in deep learning. Therefore, it is very popular in the deep learning field.

### Matplotlib

Likewise, Pandas, it is not directly linked to machine learning. It is the most popular python library for data visualization. Moreover, Matplotlib is a 2D plotting library that is used for producing productive 2D images and figures.

Matplotlib helps to create bar graphs, histograms, error charts, scatter plots. It is similar to MATLAB. It supports python as well as IPython shells. Pyplot is a module with the control line styles, formatting axes, etc. Moreover, it allows programmers to make plotting easy.

## Seaborn

Seaborn is a python library based on Matplotlib for data visualization and statistical graphs. It functions on data frames and arrays containing datasets. Also, it performs statistical aggregation and semantic mapping to produce informative plots.

Seaborn has a close integration with Pandas for data visualizing and analyzing. The graphs produced by this library are more appealing than Matplotlib. Moreover, it also needs less code for visualization and creating graphs. Sea born provides a high-level interface for drawing attractive and informative graphs.

**Conclusion**

There is a huge list of libraries from which we have taken the top 10 Python libraries for machine learning. Machine learning makes the work of humans automatic and easy. Moreover, developers provide us with the best programming language for machine learning to ease the work for beginners.

The programmers prefer Python libraries for machine learning over other languages. As it provides us with many flexibility and benefits. It saves the time and effort of people to code and debugs. Of course python is a free and open-source library, which makes it community-friendly.

It is the best-developed tool for programming as it has a simple syntax. finally, I hope this would have given you a clear picture of python libraries for machine learning.