Do you want to learn machine learning with Python and have no clue where to start?
Even if you have zero information about programming, that is not a problem.
Machine Learning is an important subset of Artificial Intelligence. This innovative technology trend is a hot topic in the field of machine learning. We can’t stop emphasizing the adoption of machine learning and Artificial Intelligence for processing and analyzing huge volumes of data and tackle the work that can’t be done manually.
But, Machine Learning and artificial intelligence went beyond science fiction. Undoubtedly, within no time they have become the necessity of the current time.
In this step by step explanation, you will learn how to use Machine Learning with Python and add another star on your shoulder.
An introduction to Python
In 1991, Guido Van Rossum designed a programming language that is now popularly known as Python. This language was designed to emphasize coding readability with prominent use of vital whitespace.
Unlike other coding languages, Python is the only language that can be used for research as well as development and developing production systems.
Getting proper knowledge about Python is an intimidating process that can’t be understood with bookish knowledge. It’s equally important to get theoretical as well as practical knowledge of coding because without practice it’s not beneficial. The best way to start Python for machine learning is to finish a project.
When you’re practicing machine learning it’s important to stick to one coding language because it’s a determining factor for your future projects. However, Python is a perfect choice for beginners to start with Machine Learning and data science. This minimalist language is widely accepted because the syntax is quite simple than other languages and have easy to use a lot of code libraries. If you are willing to get more familiar with the library’s NumPy, Pandas, SciPy, and sci-kit-learn, you can access them online.
Python is an open-source programming language that is object-oriented and highly interactive. It also has an outstanding power of combining with syntax. Python is also used as an extension language for various applications that are written in other languages.
Let’s understand in detail why Python is the most preferred language for Machine Learning and Artificial Intelligence
Now is the time to leverage the benefits of Python’s features and use it effectively in Machine learning and data science. Data scientists and developers are already favoring Phyton over any other language and have become their go-to programming language. Python’s simple syntax and readability are the primary reason.
1.Easy to Use
Python is easy to use and less complicated which is one of the main reasons it’s used in Machine learning algorithms. It is simple and easy to read syntax that makes it a popular choice among seasoned developers and experimental students.
Python’s simplicity means that developers can concentrate on providing a problem’s solution instead of spending their time understanding the language’s technicality.
Subsequently, Python is extremely sufficient and allows developers to complete work using fewer codes. Python coding is easily understood by layman which makes it perfect coding language for machine learning.
2. Flexible language and platform independence
Python for Machine learning is the first choice because it’s very flexible and enabled with ample support from different resources. An open-source programming language that’s is supported by different resources. This language can be easily combined with any other programming language, to get the work done on time.
Developers can make any changes at any time, one need not worry about rebuilding the source code. This language provides complete assistance during all the stages of the coding process. Python can also be combined with Java, .NET components, or C/C++.
The flexibility decreases the possibility of errors because programmers get a chance to take the situation under their control. Furthermore, flexibility allows developers to work on programming styles with which they are comfortable and even combine them according to their requirements.
3. Corporate support
In the early ’90s with the introduction of Python, came a revolutionary change in the field of data science and machine learning. Many online platforms promote machine learning in Python. Apart from this, renowned companies like Google, Facebook, Instagram, Netflix, Quora, etc are using Python. Consequently, it leads to unconditional corporate support from these companies.
4. Library ecosystem
One of the factors which makes Python such a popular choice which is a great library a framework that supports coding and saves extra time spent on coding. A library is a unit or a group of the unit published by different sources like PyPi and NumPy etc. The libraries which are used in Python coding are Pandas, Keras, Tensor Flow, Stick-image and NTLK, etc.
Python libraries provide basic items so developers don’t have to code them every time from starting. As far as Machine learning is concerned, Python’s library allows you access, handle, and transform data.
5. Readability
Phyton is easy to read coding language that can reduce the risk of confusion, errors, or conflicts. Developers can easily understand the code and make any changes.
Applications of Python in ML and AI
Python is popularly known for its features and makes it the best language for ML and AI. Below are the five most important applications of it.
1.Travel
2.Fintech
3.Transportation
4.Healthcare