Python was released by Guido Van Rossum as a side project but he never knew that this language will get such success in the future. When it comes to “which language is best for machine learning”, Python out rules all the other languages! Python is loved by developers just because of the simplicity of its syntax and less complexity. By the end of this blog, you will know “why python for machine learning” is so much popular.
Python is the language that can be learned most easily and in today’s date, it has the most applied use in the industry.
Future of python programmers:
Python popularity is growing day by day. Even school curriculums that used to teach JAVA and C++ as coding subjects have now started teaching python. The number of installations for this high-level programming language is making it easier to adapt.
Some of the reasons for why python is popular are:
- It is popular because of GITHUB as it makes it much easier to add patches to a project. Most of the scripts on GITHUB are written in Python. Hence instead of recoding you can manipulate scripts accordingly.
- Its large supportive community: There are a lot of python libraries with massive codes and open source support along with python programming tools that make the work of a programmer easier. The programmer can do specific tasks in shortcuts by using these libraries and tools.
- Python has more tools compared to any other language due to its large customer base. From debugging to testing custom plug-in design relative to niche languages, the developers can invent features easily using python tools.
- You may found as many jobs in JAVA or other languages like C++ or C# but working with them is relatively harder.
- Python lets the user build from software to websites and games. Learning only one language gives you the chance to try different fields in the IT industry.
Machine learning using python:
Machine learning is all about recognizing patterns in a set of data. The most important aspect for a programmer involved in machine learning projects is to extract process and arrange data. This classified data is the basis of the algorithms that predict the future data.
Hence for a Data Scientist or Machine learning engineer, the important aspect is first to analyze data and find a pattern in it. Python allows the developer to do this classification easily because of the inbuilt libraries and tools it has.
Why python for machine learning- Python Libraries
For every function whether working with images audios or text files, python offer a number of libraries to the users to tackle the data-
- Images and text- numpy and scikit.
- Audio- Librosa
- Machine learning- pandas and scikit
- Deep Learning- pytorch and tensor flow
- Web applications: Django
- Scientific Computing- Scipy
Even a basic knowledge in python allows the user to use all these libraries. They are extremely simple to use and hundreds of tutorials are there on the web to learn them.
The only problem with python is that it cannot be accommodated by small processors and low memory hardware.
Hence, in that case, we have to switch to languages like C++ or C.
Mastering Machine learning with python:
Why python for machine learning is the dilemma in a person’s mind before starting with machine learning. However, when one switches to this user-friendly language, things become easier.
Why python for machine learning by a google developer!
Starting Machine learning with python:
- Learning the basic python skills: A decent level of experience in python is very essential in scientific computing and machine learning. First, you need to build the environment for python programming:
- Install python 3. Installing Anaconda is the best option as it is the best for scientific computing as well as machine learning.
- If you have a basic experience in other languages but not in python, you can try the following machine learning in python course.
- Google Developers Python Course: This is a professional course with a certificate from Google in the end.
- Learn X in Y Minutes (X = Python): This is a crash course for python learning.
- Machine learning skills: There are a lot of algorithms that require a great amount of time investment. But don’t worry as you need not be a Ph.D. scholar or MS student to learn these algorithms and skills. But make sure before entering in any field of computer science you have a great command on Data Structures. You can go for the free machine learning course for beginners by Andrew NG on Coursera. It is a very conceptual course.
- Using python libraries effectively: The various python libraries available are like a gift to developers. You should know the use of each library well before using them:
- Numpy is used for managing and analyzing N-dimensional array objects.
- Pandas is used for managing data and includes data frames
- Matplotlib is essentially useful in plotting 2D graphs.\
- Scikit consists of the main machine learning and deep learning algorithms
- Machine learning topics to be learned in python:
- Linear regression
- Supervised and unsupervised learning
- Logistic Regression
- Decision trees
- Neural Networks
- Support Vector machines
We have summed up all the answers we have regarding to your question “why python for machine learning?” However, if any of the points are uncovered you can put your views in the comment box. Thank you for reading and if you find this blog useful, do share it with others.