Python is considered one of the best computer languages if you think you can build a career in Machine Learning. It is easy to learn and compatible making it widely popular among developers. There are several reasons why you should Python. One of the reasons it is so popular is because of its massive collection of Python Libraries.
The syntax used in Python is super easy to learn, and you can even compare it with robust computer languages like C++ or Java. Also, Python is relevant to future needs, and, of course, most users believe it is ideal for AI-based projects.
Top 10 Python Libraries you Must Know
In this section, we will go through 10 of the most useful libraries you must know. We will also elaborate on the reasons why we believe it is worthy. Also, we will discuss where exactly you can use them.
The first library on our list is TensorFlow. For those who don’t know, TensorFlow is one of the first-choice libraries if you are engaged in machine learning projects. Developed by Google, this Python library is used in most Google applications. You can also use TensorFlow as a computational library that will allow you to write new algorithms.
With TensorFlow, you can easily create Responsive Constructs and train a CPU or a GPU for shared computing. This Python library is flexible in most of the operations, and you can brew several neural networks and various GPUs. Apart from that, TensorFlow is open-source and serves a large community. Thus, you should give it a try.
If you are working on a project that contains a large number of complex data, then Scikit-Learn is probably the best choice for you. Over the years, this Python library went through a lot of changes. One such notable change is the addition of cross-validation. You can use more than one metric on it now.
Other useful features include unsupervised learning algorithms like clustering, factor analysis, and principal component analysis. You can also take advantage of extracting features like images and texts. Overall, Scikit-Learn is a utility library of Python by which you can do a set of work. Also, you can use Scikit-Learn for data mining projects as well.
The third on our list is NumPy, which many believed to be the most famous library in Python when it comes to machine learning. It is most renowned because of its ability to work with TensorFlow. NumPy is easy to use and pretty much interactive. You can also solve complex mathematical problems with ease.
One of the most compelling features of NumPy is the Array interface. You can use the library for expressing images and sound waves as an array of real numbers in N-dimensional. Apart from that, if you are a full-stack developer, NumPy is the ideal Python library for you.
Keras is one of the smartest Python libraries to date, which is an ideal choice if you fancy machine learning. It can display neural networks with ease, and when it comes to compiling models, processing data sets, or viewing graphs, Keras can be your best friend. Moreover, it can also work with other libraries like Theano or TensorFlow, which gives you an edge in backend jobs.
You will be surprised to know that you use Keras almost every day. Popular programs like Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and much more use Keras. However, the most effective use of Keras is Neural Networking. That’s not all, you can do in-depth learning studies using this library as well.
If you are a developer, wanting to perform tensor computations, we believe the PyTorch library is your best option. It offers a variety of APIs to solve issues related to neural networks. PyTorch is a Torch-based machine learning library, which is not only open-source but also uses C wrapped in Lua.
Now, if we skip the technical part, you can use this library for natural language processing. PyTorch is better than a lot of other libraries. That is why it is getting plenty of attention. Some of its most important uses are Hybrid Front-end development and Distributed Training. Therefore, PyTorch justifies the list we have created.
The next on our list is LightGBM, which is popularly known as Gradient Boosting. Ideal for machine learning with new algorithms, by which you can redefine elementary models. LightGBM ensures quality in production, and at the same time, it is user-friendly. With Gradient Boosting, you can get faster training, and you are less likely to make errors.
You can use LightGBM for typical machine learning. It offers high scalability compared to other libraries, and you can expect faster Gradient Boosting. LightGBM is often the first choice for Python users so, you should give it a try.
If you want accurate predictions in machine learning, then we recommend you Eli5. It is a robust Python library that offers an ideal blending of visualization with debugging. So, you can track all the working steps of an algorithm. That’s not all, with Eli5, you can work with other libraries like XGBoost, Lightning, Scikit-learn, and even Sklearn-Crfsuite.
Now, if we want to discuss the use of Eli5; you can use it in solving Mathematical equations with ease. You can rely on Eli5 while working with other Python packages. Moreover, you can run Legacy applications and find new ways. Therefore, it should be worth your time.
If you are an application developer or an engineer, you should check out this fantastic Python library – SciPy. With SciPy, you can optimize the modules and perform a bunch of other stuff. You can work with SciPy along with NumPy to get the most out of the array feature. Apart from that, you can do numerical routines with the help of this library without breaking a sweat.
One of the most important uses of SciPy is when it works along with Numpy to resolve mathematical functions. Also, it uses NumPy for basic data structure building. Additionally, you can do linear algebra, integration, and a whole bunch of equations. Therefore, all these prove you can rely on SciPy, and it is worth a shot.
If you are looking for a computational framework well, look no further as you can check out Theano. It is primarily a Python library that offers multi-dimensional computing arrays. It is ideal for production, and it can work with other libraries as well. With Theano, you can expect speedy performances, something you can rely on.
If you are new to software development, Theano can be your ally, which can handle complex computing. You can use it in Deep Learning research as well as in creating new ones. With this, you can do projects related to the neural network thus, it is one of its kind according to many experts.
The last on our list is Pandas, which is renowned for high-level data structuring. You can perform complex programs using Pandas, and you can translate data effortlessly. With this library, you can handle the data of an entire project, and the best part is the speed it operates. Something that many of its rivals fail to deliver.
You can use Pandas to fix bugs as well as improve the overall status of a project. It supports a wide range of APIs which are best for groping and sorting data. So, you can say the main purpose of Pandas is to use it in Data Analysis.
So, these are the list of libraries that you should consider if you are using Python. Now, please note that there are hundreds of libraries for Python that you can use as well. We have created the list with those libraries which are, in some ways, unique to one another.