Why and How Should I Learn Machine Learning?

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Why and How Should I Learn Machine Learning?

Why and How Should I Learn Machine Learning?

There always been a question to all that why should I learn Machine Learning and why Machine Learning is being given too much importance nowadays?

If you are the one who is thinking to opt Machine Learning as a career but don’t know what is it and why is it to start then this article may help you to clear all your doubts about choosing Machine Learning as a career choice.

Artificial Intelligence is an emerging technology for upcoming times. Machine Learning is an application of Artificial Intelligence. There are many advantages of Artificial Intelligence.

The primary aim of the same is to allow software devices to learn automatically without any human interference.

By using Machine Learning, you are just trying to make your software device smart enough by feeding them the previous outcomes (e.g., Data), and based on the experience, your device will generate the output on its own.

Machine Learning has brought various scopes and improved strategies into various market fields and made us able to get more profit.

machine learning

Career Choice in Machine Learning

Machine Learning is becoming vital nowadays as it helps in very difficult terminologies involving Big Data to such things that are being used on the daily basis. Most of the computer giant companies are working in this field. So you never need to worry about your career choice. It is being used in every area of the customer’s services.

If you are the one who loves to do mathematics and good at the same then you are a suitable candidate for opting for Machine Learning as a career choice.

It is a field which you will find a very interesting and good point to start with, as Machine Learning is the subject that deals with the mathematical formulas for Data Analysis and Algorithms.

Let’s see what are the requirements for machine learning.

Programming Languages for ML

On the technical side, there are some programming languages that are being used for Machine Learning.

You must be good at any of these languages if you are willing to start with Machine Learning. In current times, Python leads the market. Python is the most popular programming language used by Machine Learning Developers and the Data Scientists.

Important Mathematics Topics for ML

Machine Learning requires a mathematical background and if you have it then you could just brush up on the concepts of Engineering Maths.

There are some Mathematical concepts mentioned below that will help you to start with Machine Learning,

  • Linear Algebra
  • Probability and Statistics
  • Calculus
  • Optimization Method
  • Graph Theories

How to start with ML?

For starting with Machine Learning,

  • You can just take advice from your faculties and the seniors who are subjected to this field.
  • You can also start with some online courses available for Machine Learning.
  • Start with learning basic machine learning algorithms.
  • You can attend the seminar and the workshops organized on Machine Learning.
  • Get familiar with machine learning tools.

It will help you to give practical knowledge and insights into the core subject.


This is all about why machine learning. I hope this article will help you to get a new direction and the workflow towards it. I wish ALL THE BEST to learn and develop new things. 🙂


  1. I spend a few months going through all AWS ML Kinesis streams, firehose, data analytics, Athena, Glue/crawler/metadata, and most ML algorithms, KNN, K-mean, Regression, classification, XGBoost, Seq2seq, Object2Vec, RCF, PCA, BlazingText, etc.

    Right now I need more practice on all these algorithms with AWS sagemaker. However, I just could not find any examples showing any end-to-end, or say lifecycle ML project out there. I even wrote to the head of AI/ML director at Amazon to ask for this kind of tutorial. I haven’t got anything so far.

    So desperately need help on this, try to practice all these algorithms with sagemaker in order to start a new job. I am a software engineer, worked as a big data architect for the last 8 years, certified Cloudera admin/developer. but failed to pass the AWS ML certificate exam. it is very hard and only for those who already have a lot of ML experiences.

    Thank you for providing a window to communicate.

    1. Robin,

      It feels great knowing about your progress. I like your approach and how are taking it forward. I would suggest building your small project around ML. You can refer to the Kaggle to get some project ideas that you can build upon.

      Best wishes!

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