As a Final-Year Student in Computer Science, you might pick up the Machine Learning Course as it is in high demand in the industry. But have you ever thought about in depth why to use “Python for Machine Learning”?
If you have still not thought about this & are going to pick up Machine Learning, it is high time to know about it. If the foundation of the course is clear, the entire course will become a cakewalk for you.
This article is going to highlight the necessity of Python in Machine Learning for various reasons. So, grab the seatbelt to experience an extensive journey.
TL; DR: Python For Machine Learning
Aspect | Summary |
What is Machine Learning? | Machine Learning is a way for computers to learn from data and make decisions or predictions without being directly programmed for every task. |
Ease of Learning | Python is easy to read and write, which helps students understand Machine Learning concepts without getting stuck on complex coding syntax. |
Student-Friendly Tools | Libraries like Scikit-learn, PyTorch, and TensorFlow allow students to build Machine Learning models with less effort and more clarity. |
Career & Job Scope | Python is widely used in Machine Learning jobs, internships, and academic projects, making it a strong choice for students planning their careers. |
Learning Support | Python offers massive learning resources and community help, so students can solve doubts and continue learning without frustration. |
What Is Machine Learning? Read Below
In simple words, Machine learning is a process that uses any specific coding language & implements some mathematical functions. Based on those mathematical functions, we can solve some problems where human intelligence is necessary.
From the machine learning concept, Artificial Intelligence has been developed. The Machine learning concept is developed upon the Data Analytics concept. More often, a student misunderstands these three topics. But they should know their proper order of appearance.
One of the major advantages of Machine learning is that it can store a lot of previous data. And based upon that stored data, it has analytical power. For analysis purposes, machine learning uses many mathematical concepts.
Using that theory, machine learning is being used for Voice Recognition, Face Recognition, etc. Machine learning can store past data. Using those datasets, it can develop its intelligence. And based on that intelligence, such complex tasks can be solved.
Why Machine Learning Feels Difficult For Students At First?
Before choosing Python, it’s important to understand all the problems a student faces. Let us check the following list to know more.
- In Machine Learning, there is too much math and coding together.
- You can get confusing error messages
- In some languages, you have to deal with the long and complex syntax.
- The learning path is unclear and too complicated
- Students get the fear of using the “industry-level” tools.
Many students quit Machine Learning not because they lack intelligence, but because the tools feel overwhelming.
Why Is Python Good For Machine Learning Than Any Other Comparable Programming Languages?
We hope that the definition of Machine Learning and why it feels difficult for students has become clear to you. Now, we will tell you the reasons why Python is great for the Machine Learning.
1. Independent Nature:
The Python programming language is independent of any other programming language in the field of machine learning. The code that is developed with the Python programming language can be executed in any of the IDEs or platforms.
Also, in several other operating systems, Python is executed in the normal manner. A developer doesn’t need to do anything to execute a complete Python code. It is a feature that is only available in the Python programming language.
2. Free To Use:
The Python programming language is completely free to use & open source in nature. No amount is needed to use the complete features of the Python programming language. If we remove the static programming languages from the discussion, the dynamic programming languages charge some amount to use.
There are several dynamic programming languages present, such as the Python programming language. But they are not completely free. To use some advanced concepts, one should pay some amount. So, it will be better to go for Python.
3. Scalability:
The Python programming language is highly scalable. Scalability means that the Python programming language can handle a large dataset without having any issues. It can expand more in a horizontal manner, which gives extra support.
In the Python programming language, there is a large support of functions that will be used to gather dataset information for the programs. Scalability is a good parameter in the development of machine learning.
4. Flexible Nature:
The Python programming language is highly flexible. The Python programming language can be used as the back-end programming language in the web development process. With HTML, CSS, and SQL, one can also easily use the Python programming language.
In the machine learning development process, nobody knows which programming component needs to be used along with the core programming language. So, it will be better to choose one flexible programming language for the ML development process.
5. Easy To Code:
If you choose a programming language for machine learning where you need to print a simple statement, you need to write several lines, then that choice is going to be a fatal one. In machine learning, you should choose a programming language that is very simple to write.
And the Python programming language will be appropriate for that. We will not find any programming language that is easier than Python. That is the reason; the Python programming language is highly used.
6. Large Community Support:
Python has one of the largest community support systems among all programming languages, like Java or the R Programming Language. You will be happy to know that the Python Community is increasing more rapidly than any other programming community.
In most cases, these are the communities that are highly helpful & there are numerous forums also present where you can take part & clear your doubts. If you want, you can answer any problem asked by the community as well.
7. Presence Of Data Science Tools:
It is one of the most unique features for which Python is more used for Machine Learning than any other programming language. Python is well associated with Data Science & Data Analysis. And you already know that the ML is the higher part of the Data Analysis.
From Numpy to Matplotlib, numerous libraries support working with the Dataset in Python. Whereas, for other programming languages, the Data Library Support is not as great as that of Python.
8. Presence Of Large Library:
We have already discussed various Libraries on Python that can be used seamlessly for Machine Learning Purposes. These are the libraries that are only present in Python.
- NumPy & Pandas are used for data handling purposes.
- The Scikit-learn library is the most beginner-friendly among the ML models.
- TensorFlow & PyTorch are used for deep learning and AI.
- To do visualization, the Matplotlib & Seaborn libraries are used.
These libraries are beginner-friendly, have massive community support, and are widely used in universities and companies
9. Integration With Cloud Service:
If you want to work on Machine Learning based on cloud services, then Python will be the best. There are numerous Cloud Services present, like AWS, Google Cloud, etc., where Python is the only language to work on.
Any other programming language, like the R Language, might have some library support. But they will not be as extensive as the Python Language. So when you have so much support from Python, why switch to other languages?
There are frequent times when Machine Learning should be implemented in the Cloud so that it can be extended to more fields. And if you want to work on them as well, then the Python Language will be a must.
10. Growing Industry Demand:
There is no question that the demand for the Python Language is increasing day by day more than any other programming language. And the need for the ML, AI, etc industries is also getting more value than any other industry.
If these two sections come together, then the cocktail industry will be the best in the world. And that is going to happen sooner or later. So, it will be better to understand the scope of the future & brace yourself for that.
Comparing Python and R for Machine Learning:
A great competitor and alternative to Python in the field of Machine Learning is the R programming language. And many students get confused about which to pick between Python and R for Machine learning.
To remove such confusion and to guide you to pick up the best language for the Machine Learning assignment, go through the following comparison.
Python language:
- Easier to learn as a first language
- Used in ML, AI, Web, Automation, and Data Science
- Strong career flexibility
R Language:
- Strong in statistics
- Better for academic research
- Steeper learning curve for beginners
For the students who are aiming for jobs, internships, or projects, Python is the safer and smarter starting point.
How Students Can Get Started With Machine Learning In Python?
Now, if you have made up your mind to go for Machine Learning using Python, then here are some small tips that you have to follow from now on. You have to keep in mind that Python Machine Learning is not a simple subject.
Here are some of the best tips to get started with Machine Learning Concepts.
- Enroll in Online Course: When you are going through the Python Course in Academic, enroll yourself in some Online ML Courses & start getting the knowledge from there.
- Pick ML in Academics: In your Final Year, you might get ML as the Optional Subject. Always choose the course & attend all the classes without missing.
- Be Part of Forums: From the very first day, try to be a part of different online communities & forums. Also, be active on the forums to participate in debates & discussions.
- Make Focus on Practical: In the ML Course, you will come across different Theoretical parts for sure. By understanding the theories, concentrate on the Practical Part.
- Implement Projects: When you get a bit of understanding of the practical part, start implementing some small projects from there. Gradually, then try to implement some difficult projects.
Where Students See Machine Learning In Python Used In The Real World?
Before ending the discussion, we want to shed some light on the applications that are present in the Real world & are developed with the help of Machine Learning in Python Programming Language. The applications are the following:
- Google: Google uses Machine Learning in its search engine. And also, uses ML to rank any content based on the Keywords Searched.
- YouTube: When you surf on YouTube, the Machine Learning Algorithm of YouTube understands your choices. Based on that, you get videos or songs.
- Instagram: Whatever the Video or Reels you see most of the time, based on the content, Instagram shares new videos with you. This can happen with Machine Learning.
- Reddit: The Social New Aggregation Website was not using Python back in the early 2000s. But, later, it switched to Python & got a grand success there.
- ChatGPT: The well-known AI Chatbot uses Python & Natural Language Processing, which is a more intensive Machine Learning Process.
Common Mistakes Students Make While Learning ML With Python:
Now, while learning Machine Learning with Python, there are some common mistakes that most students make. To avoid making such mistakes in your homework, let’s focus on the following list.
- Jumping straight into advanced topics like deep learning without first understanding basic Python and Machine Learning concepts.
- Copy-pasting code from tutorials or YouTube videos without actually understanding what each line is doing.
- Ignoring data cleaning and preprocessing, even though real-world Machine Learning depends heavily on good-quality data.
- Focusing only on algorithms and completely forgetting to understand the problem they are trying to solve.
- Getting discouraged by errors or low accuracy early on, instead of treating mistakes as part of the learning process.
Conclusion:
As we saw, the necessity of “Python for Machine Learning” is very high.
The Python programming language has some unique features that match the necessity of the machine learning development process. That is the reason; Python is only used for the ML development process.
If you want to be a good ML developer, start by clearing the basics of the Python programming language. The basics of the Python programming language are going to give a better grip on advanced topics in the future.
Still having issues understanding, you can always hire a programming tutor from CodingZap to clear all doubts.
Key Takeaways:
- Machine Learning is the process by which the Implemented Code can perform Complex Mathematical Operations that Humans normally do.
- To be a master in Machine Learning, you have to get a strong understanding of Python.
- Machine Learning can also be implemented with different languages like Java, C++, R, etc.
- But, the Presence of Libraries, Large Communities, the presence of cloud services, etc., provides Python the upper hand in Machine Learning.
- From PyTorch and TensorFlow to NumPy, several Libraries are present in Python to support ML.
- Google, YouTube, Instagram, etc., are some applications of Python Machine Learning.
Frequently Asked Questions
Do Students Need a Strong Programming Background Before Learning Python for Machine Learning?
No, students do not need to be expert programmers before starting Machine Learning with Python. A basic understanding of Python syntax and logic is enough to begin, and most ML concepts can be learned gradually.
Why PyTorch Is a Big Advantage for Students?
PyTorch feels more natural and easier to understand for students because it works like regular Python code. This makes debugging simpler and helps learners clearly see how Machine Learning models work.
Is Python Enough for Machine Learning as a Student?
Yes, Python is more than enough for students to learn Machine Learning from scratch. It allows students to build models, work on real projects, and even prepare for internships.
