Python is one of the most efficient and popular programming languages for machine learning. As the name suggests, machine learning is the ability of computer to learn without any need of explicit programming. For this purpose, python libraries, frameworks, and modules have greatly helped.
Python has replaced many programming languages. The main reason that contributes to this fact is the wide range of python libraries.
The top 5 python libraries that are most commonly used in machine learning include the following:
We make some money when you purchase a product from a link on our website. If you found the content helpful, please use the link to get to the chosen provider of your choice. It doesn’t cost you a thing and it helps us put out great content. The money involved does not effect the ratings of any given product or service, we just link to an affiliate if there is one available after we write the article.
Numpy is the Python’s best library when it comes to data science. It supports working with multidimensional arrays and matrices. It further features advanced mathematical functions.
The collection of tools Numpy offers makes calculations easy especially while working across high performance arrays. Its structures assist in manipulating and analyzing data which is collected in not-so-functional lists in Python otherwise.
Scipy is the Python’s library that works where Numpy faces limitations. When it is not possible to work on linear algebra and matrices in Numpy at some level, it is time to further advance with Scipy.
Scipy library includes a lot of foolproof and easy numerical operations commonly. Other than this, it is comparatively convenient to learn and has a wider application range than Numpy.
Theano is another friend of Numpy with pretty much same functions and operations. It comes with multidimensional arrays which can be coded and evaluated efficiently. Moreover, you can perform optimized mathematical expressions on those arrays.
The best thing is that it runs equally great on both CPU and GPU architectures.
Pandas is one of the powerful Python open-source data science libraries as of yet. It has been implemented in applications that are used worldwide such as Google Maps and Uber.
Pandas comes with an exceptional feature; it captures data nicely and clearly into structures that helps with intuitive and efficient analysis. Apart from multiple filtering, reshaping, indexing, and pivoting methods, it has two main object types: DataFrame and Series.
These features make it a handy library.
Tnesorflow was released by Google Brain team in 2015. Its main purpose is deep neutral networks research. It has been implemented in a number of business applications to provide effective management.
The best thing about this library is its ability to support distributed computing. It helps greatly when computing graphs on different servers and for independent processes.
Where to learn?
There is a great debate whether Udacity or Pluralsight is the better option.
Pluralsight has wide range of programs and courses for Python machine learning. It offers 10 day free trial right when you sign up so you can analyze the services in 10 days. Udacity also offers some amazing fast-learning courses however, there’s no free trial. Once you pay, you have no other option.
So, get into Pluralsight vs Udacity here. We recommend going for Pluralsight ultimately though.