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How to Get Started With Machine Learning?

How to Get Started With Machine Learning?

Published by Programme B

We already know that Machine learning and Artificial Intelligence have seen growth like never before. This all started where the collection of big data began. Data is raw and is of no use until some meaningful information and insights are extracted from it. In this way, Machine Learning and artificial intelligence use the dig data to extract some meaningful information from it. Organizations have understood its importance and have adopted it in their business functions. Examples include- IT automation, intelligent process automation, automated customer service agents, automated supply and logistics, and much more. 


Seeing this immense growth of AI and Machine Learning will definitely make you curious about it? Read the rest of the article to find out how you can learn the machine learning basics

What is Machine Learning?

This is a world of the technological revolution. Some of the latest technological trends such as data science, artificial intelligence, etc have gained popularity over several years. One of them is also machine learning. Talking about machine learning, it is also a part of artificial intelligence. Machine learning is about finding patterns in large sets of data. With the help of programming languages such as R, Python, machine learning algorithms are developed which analyze the data and figure out a pattern.


These patterns are then later on put into use so that the service and products can be better used. This has become an essential part of analyzing the data. Examples of machine learning include digital assistants which recommend us the things we ask, websites and apps that recommend the things based on our browsing experience. So it must be very clear to you how important machine learning is. 

How to get started with Machine Learning?

Here are the following steps to get your career started in Machine Learning.

1. Learn prerequisites

Getting to know machine learning is definitely not an easy task. So there are some prerequisites that you need to know before studying machine learning. Here, the concepts required to know depends on the role of the job you will be performing. The difficulty level of the concepts also depends upon the job role. Some of the topics are Linear algebra, multivariate calculus, statistics, python. 

2. Learn various ML concepts

Some of the basic concepts to understand in machine learning are:

  • Model- data representation in various forms is called the model. This model is always backed up by machine learning algorithms. 
  • Feature- features can be termed as properties.  
  • Target(label)– a label is a value that is to be predicted by the model, such as the name of the object, etc.
  • Training- the training comprises giving up a set of data to the hypothesis and predicting the outputs.  
  • Prediction- the predicted inputs are given to the model which gives the output.

    3. Enroll in a Machine Learning course

Now that you have learned the prerequisites and basics of machine learning. It’s time to take up a course in machine learning that will completely train you for a better profession in the organization. The types of machine learning that should be covered in the course are:


  • Supervised learning

Supervised learning uses regression and classification models to learn from the training data set.

  • Unsupervised learning

Unsupervised learning uses cluster and factor analysis models to determine patterns and structures on the big data.

  • Semi-supervised learning

Semi-supervised learning consists of both labeled and unlabeled data for learning. 

  • Reinforcement learning

Reinforcement learning uses the hit and trial method to learn more about the data. 

4. Practice what you learned.

The machine learning concepts and prerequisites are of no use if they are not put into practice. Start by collecting the data and processing it. It often requires cleaning and integration. Often this can be time-consuming. The next step is to build models and algorithms to know more about data. It will be great if you will practice on real datasets. Lastly interpret the results obtained. 

5. Take up Projects

Now that you have practiced machine learning, it will be a good time for you to commercialize your skills. Start by taking up freelancing projects for different businesses. Or you may even find work opportunities on job platforms. kickstart your career in machine learning.

6. Participate in competitions

Competitions really have the potential to enhance your skills. It gives you the platform to experiment with your skills and challenge yourself. The same is with machine learning. Competitions will expose you to your real strengths and weaknesses. Some of the technical competitions to look out for in machine learning are Digit Recognizer and Titanic. 

Start your Career in Machine Learning Today!

Hence these were all the steps to follow for becoming proficient in machine learning. Remember to follow all of these steps very seriously. As machine learning is not an easy task to do, it requires practice and patience. The average salary of a machine learning engineer in India is ₹689,425, according to Payscale. Globally, machine learning jobs are projected to be worth almost $31 billion by 2024. That’s an annual growth rate of more than 40% over a six-year period. So this shows how machine learning is going to be gaining so much importance in the next few years. So what are you waiting for? Just have faith in your abilities, and you are good to go.