Today, Python is arguably the best computer language for Machine Learning & Data Science. What makes the language so lovable around the world is it has simplified the algorithms of Machine Learning drastically. There was a time when people used to do the entire coding manually. Nowadays, you can do it quickly and efficiently with Python and its range of tools.
Of course, Python is a capable language itself, but people admire the language also because of its incredible tools. With the use of these tools, we would code with ease. However, there are hundreds of Python tools available today. So, what we are going to do; we will list most useful tools for both Machine Learning and Data Science that are famous as per the users.
Machine Learning Tools
Nowadays, we can’t ignore the use of Artificial Intelligence (AI) and Machine Learning (ML). Companies are spending millions and billions of capitals for the betterment of current technology use. One unfortunate fact is that time is limited.
So, it would be best if you learn everything quickly and precisely. That is why Python is one of the best options that you can get. Down below is a list of some Python tools, which you can use to elevate your skills.
If you are looking for an ML learning library that can perform high-end statistical calculations, then TensorFlow is ideal for you. From complex image recognition of networks to written digit grouping, it can do it all.
One of the benefits of this python tool is Concept-building. Thus, if you can focus on the logic rather than using algorithms. You can even create apps that have both AI and ML use via TensorFlow.
Theano is, first and foremost, a computing library that usually defines mathematical expressions. It mainly deals with multi-dimensional arrays. You can say there are many ML and AI apps that run on the same protocol. With Theano, you can figure out data, which are 100 times faster than your CPU.
You will also get the same performance for your GPU, where you can separate symbols. Apart from that, you can also do thorough code-testing with Theano.
For those who are wondering what makes PyTorch one of the best Python tools, they must know, it is, first of all, a production-ready tool. PyTorch has vast library support backed by strong online alliances. So, you can expect a strong GPU acceleration while you use apps like NLP.
Furthermore, you can use it for GPU and CPU computations as it delivers subtle performance in both training and production. Like most popular tools, you can also use PyTorch with other Python tools, as well.
Numerical Python, or popularly known as NumPy, is a form of linear algebra. What makes is unique is that it can easily mingle up with other Python tools and libraries. In fact, you will need this tool for almost every project you do in Python.
It can deal with complex expressions like linear algebra and Fourier transformation. With NumPy, you can also do scientific calculations based on sound waves and images. You can also try binary functions, as well.
Pandas is a renowned Python tool typically known for its data-centric programs. If you are preparing a project where you have to manage tons of data with proper assessments, Pandas is your best ally. It is Open-Source, which has a massive library of tools to handle data.
Pandas can read data from popular sources like CSV, SQL databases, JSON files, and even Excel. The commands are easy to learn and use. It also comes with several filter options, which makes it a robust tool altogether.
Seaborn is a popular tool, which is known for its library’s visualization. If you have a knack of doing a storytelling project, you best bet will be to use Seaborn. It is ideal for ML codes and comes with data visualization features. You can prepare fantastic statistical graphics using this tool.
Not only that, but you can also create plots such as time series, heat maps, and violin with Seaborn. With this tool, you can also perform statistical estimation as well.
Scikit-learn is primarily an Open-Source ML library that also has an AI library. It comes with a broad range of clustering and regression, which you can also use for classification algorithms. Now, you can also use Scikit-learn along with NumPy and SciPy, which are other reputed Python tools.
This tool can also help you to work with supervised and unsupervised ML codes. Apart from that, there are tons of features that put it among one of the best Python tools today.
First and foremost, Keras is an Open-Source Python tool by which you can write codes for complex ML projects. You can use the tool on Deeplearning4j, MXNet, Microsoft Cognitive Toolkit (CNTK). Interestingly, you can work with other Python tools like Theano or TensorFlow, as well.
You can work with almost every standalone module, like optimizers and neural layers. Apart from this, you can also work with the activation functions and cost functions, as well.
Data Science Tools
Data Science projects depend heavily on Python nowadays. As Python uses tons of tools and libraries to provide the necessary support, you should know about the tools in detail. So, down below are some of the tools that most Python developers use.
NumPy is first in this list, which is also essential for Data Science projects. The tool is easy to learn and equally crucial for every project related to Data.
The good thing about NumPy is that it is free to use and has plenty of online communities where you can take help. What makes the tool so lovable is that it can quickly solve complex mathematical implementations.
Pandas is also on this list mainly because you can perform custom operations related to Data Science. You can easily organize data using Pandas.
Not only that, you can use this tool as it is flexible to other Python tools, as well. If you are interested in using the apply method, Pandas is the ideal choice that you can rely on.
Matplotlib is, first of all, a two-dimensional plotting library that you can use in most Data Science projects. You can do cross-platform projects using Matplotlib.
The tool is also a form of Jupyter Notebook. So, you can create web server apps with it as well. It also comes with a number of GUI toolkits such as GTK+, Tkinter, Qt, and even wxPython.
SciPy is a powerful tool by which you can handle mathematical problems, which comes with many modules. With SciPy, you can manage numerical routines with ease.
That’s not all, it also supports signal processing too. Apart from that, you can also use the NumPy arrays for simple data structures. SciPy is a tool that is ideal for Data Science projects mainly because of its powers to manage data.
Scikit-Learn is yet another tool that we have also mentioned before. However, we must even know that it is also one of the best Python tools for the Data Science project as well. You can extract features that are related to images and text.
At the same time, we can also reuse several contexts using Scikit-Learn. The tool is also usable in a wide range of algorithms, such as clustering, factor analysis, and many more.
Natural Language Toolkit (NLTK) is a Python tool which is renowned for the part-of-speech tagger, which it has in-built. The n-gram and collocations feature make the tool lovable across the world.
Apart from that, NLTK comes with Named-entity recognition feature as well. Lexical analysis is yet another feature that you would admire. Overall, all the features suggest that you can use this tool for most Data Science projects.
So, those are the most used Python tools that you should know. You may be wondering why there are similar Python tools that we have mentioned for both Machine Learning and Data Science. The reason behind that is whichever language we have mentioned in both the segments; you can use both those specific tools in both sections efficiently.