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Getting into Machine Learning using Python

Every day, breakthroughs are changing what’s possible with computers. Do you think it is organization stuff, or only top coders at a popular Python Web Development Company can do that? Let’s break this thought and dive into Machine Learning using Python, the most loved language among coders.

Have you ever wondered why you are getting ads for things you have recently browsed through your browser? Well, Machine Learning is the answer to it.

Google AdSense continuously tracks the activities from your browser and identifies what you try to search and browse the most. Based on this calculated stuff, they show you relevant advertisements.

Ever tried to unlock your mobile phone using your face? Even smartphones use machine learning for the same. In our daily life, there are many examples that use machine learning, including Face Recognition Attendance System, computer games like chess, etc.

Definition of Machine Learning

Technically, "Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed."

Guide to Machine Learning using Python

Well, in this blog, we shall learn how to get hands-on with machine learning using Python.

Python is FUN!

If you are new to the coding and development domain, Python is a perfect start to grab things quickly and efficiently. It is a high-level, object-oriented, interpreted programming language that has caught worldwide attention.

To learn the basics of Python, Sentdex is perfect for you. Few people are not able to catch up with this, so alternatively, you can go for Programming for Everybody (Getting Started with Python).


After learning the basics, it’s time to know important packages in Python for machine learning.

Machine Learning Libraries are IMPORTANT! Don’t Forget

Libraries are of great use in any programming language. Few libraries are very important for Machine Learning.

  • scipy - SciPy is a free and open-source Python library used for scientific computing and technical computing. It is distributed under the BSD licensed library to perform Mathematical, Scientific, and Engineering Computations.
  • numpy - NumPy is a Python package that stands for Numerical Python. It is the core library for scientific computing, which contains a powerful n-dimensional array object, and provides tools for integrating C, C++, etc.
  • pandas – It is a software library written for the Python programming language for data manipulation and analysis.
  • matplotlib - Matplotlib is a python library, and developers use it to create 2D graphs and plots using python scripts.

So, we are done with learning Python Software Development and important libraries to learn Machine Learning. Now it’s time to dive into the track.

Machine Learning Courses – At their Best!

You can find several courses online on various websites like Coursera, edX, Udacity, Khan Academy, Udemy, DataCamp, etc. But the search is often endless for finding the best course. In my opinion, the best course for machine learning is Machine Learning by Andrew Ng.


It is an 11 weeks course where you can learn from the basics of Machine Learning to its advance. They have taught all the tutorials in detail and prefer coding in MATLAB. This course is important for the detailed theoretical understanding of ML, its types, Neural Networks, algorithms, and the mathematical proofs of the algorithms. The course has an automated tool that generates your marks once you submit your answers. Stanford has offered the course, and it carries prime importance.

Once you complete this course, the next course is the “Applied Machine Learning with Python” by the University of Michigan. It is a 4 weeks course that focuses on practical Python implementation of Machine Learning algorithms that you learned. You will learn about the practical use of the libraries mentioned at the beginning of this article.

If you want to learn even more and jump into deep learning, you must refer to the Deep Learning Specialization. It is provided by the Deeplearning.ai, Founded by Andrew Ng.

DataCamp is also a great platform to learn the concepts of Data Science. It has partnered with Facebook to provide scholarships to people around the world for Data Science courses worth $300. It is an initiative by the Developer Circles of Facebook. If you are not a part of Developer Circles, you can find a circle near you here.


Courses are so costly!

Yes, all the courses are quite costly, but they are worth it. Not everyone is capable of paying this amount to get a verified certificate. You can apply for Financial Aid for the course. Once you have submitted the application for Financial Aid, the team will review your application and it may take up to 15 days. If the team accepts your application, ENJOY! You have got the course for free.

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