6 Important Python Libraries for Machine Learning and Data Scienceby Robert Smith Technology Expert
Are you planning to become a proficient and expert data scientist and machine learning expert? If your answer is yes, you must know about the different tools and technologies associated with it. For any individual who wishes to become an expert in data science and machine learning, they must know about the programming languages. There are several programming languages, but one of the languages that will help you a lot while pursuing and even implementing your knowledge in Python. If you are a beginner and are looking forward to the machine learning certification program, then you must consider taking the Python crash course, or you can join a full-time python learning course. Regardless of choice, you must be wondering why Python is so essential and the different Python libraries that will help you as data scientists.
Why is Python essential?
You should not be surprised to know that Python is the most sought-after programming language and is considered to be the top-rated skill by universities and even industries. If you are a Python developer, then you may receive a salary up to #123,201 per year in the US, thus making it a lucrative career option. In addition to salary, Python is a highly versatile programming language that makes it useful across the different segments. Some of the areas where Python finds usage are:
AI and Data Science research
Multiple programming paradigms
Web application and internet development
Database easy access, faster system integration, and interface customization
Now that you know about the Python programming language, you must now focus on choosing the right platform for learning Python. Various institutes are offering Python certification programs. As a part of this learning, you will also learn about different Python libraries. Going ahead, we will discuss six important Python libraries, which you must know as a machine learning expert or data science expert.
Six popular Python libraries:
NumPy – The first one that you need to learn is NumPy. This is the fundamental one, and various other Python libraries for ML are built on NumPy. As data scientists, you need to know about Python. Here are some of the uses of NumPy:
It has multi-dimensional arrays
Linear algebra routines
Random number generators
Fast vectorized operations
Comprehensive mathematical functions
Pandas- It is the foundation library that finds usage in data analysis and manipulation. As a machine learning practitioner, one needs to work on the assessment filtering of a large volume of data. Handling large volumes all by themselves is difficult and may lead to errors, but with Pandas, you can overcome this. Panda is equipped to handle all this work and complete the job in less time without error. So, if you are going for machine learning for the beginner program, Pandas is going to help you a lot. You can use it to:
To write data between Python and other sources like the SQL database, CSV files.
Data analysis based on descriptive statistics
Manipulation and transformation of the data sets
Scikit Learn (Sklearn)- It is a popular Python library for machine learning. It aids both supervised and unsupervised learning. It provides tools for fitting models, selecting, and evaluating models. It is built on NumPy and SciPy libraries. Key features of Scikit:
Fitting machine learning algorithms like clustering, regression, classification.
Supporting machine learning pipeline integration
NLTK- In addition to the above mentioned library, Natural language Toolkit is also an important Python library. It includes the following features:
Named entity detection
SciPy- The next Python library that makes our list is the SciPy. It is used for advanced mathematical operations on NumPy data. Some of the key features of SciPy are:
Keras- The last one that we will be highlighting is Keras. It is used for building neural networks and modeling. This is very easy to use and thus becomes one of the most popular Python libraries amongst the machine learning practitioners. In addition to the extensibility that this Python library has to offer, it is Microsoft integrated CNTK (Microsoft Cognitive Toolkit) that serves as backend. If you wish to experiment using compact systems, then this is a great tool, to begin with.
There are various other libraries that you can find, and can also learn. Most of the ML certification program is going to help you get actionable knowledge on these concepts. Once you have knowledge of Python libraries, machine learning, and data science applications, it will be a cakewalk.
Created on Aug 14th 2020 01:28. Viewed 226 times.