My Journey from Software Engineering to Machine Learning
I was always fascinated by computers and their work. I was more interested in the ways in which computers were working along with the time complexity of various algorithms when used for searching and sorting. Later, I started to work on various projects in the field of software engineering such as designing end-to-end software to recognize the location of different users and help them navigate to the desired location using AR maps. During my studies at Arizona State University for a master’s in software engineering led me to work on interesting projects. However, my interest in machine learning and data science began to grow during my master’s journey at Arizona State University.
When I was pursuing a Master’s in Software Engineering at ASU, I went through some courses taught by Andrew Ng such as Machine Learning and Deep Learning Specialization. He did a very good job in making the concepts of machine learning and data science quite easy even for newbies who just entered the field. Furthermore, there are projects given to ensure that students gain a good amount of practical exposure to those intricate concepts, which would further enhance their understanding of data science and machine learning. After completing the courses, I’ve gained a good understanding of the usage of machine learning in different industries ranging from pharmaceutical to agricultural industries.
As a result of completing the courses, I was able to understand some of the complex concepts in machine learning such as decision trees, random forests, and gradient descent in theoretical terms. Later, I used those algorithms to solve some projects. But one thing to keep in mind when starting a machine learning project is to understand that data is the most important component in machine learning. If we give the wrong data to the algorithms, they would learn from them and make predictions in the future that would be towards the data that is being fed in the initial stages. Therefore, care must be taken, and also proper visualization must be applied before to ensure that one gets the right data to be fed to the machine learning models for predictions.
As I gained a good amount of theoretical understanding of the concepts, I began to apply the knowledge in various projects such as Predicting the chances of a person suffering from Cancer, Predicting the sentiment of Text, Predicting whether a person would pay a loan back, and so on. I’ve uploaded all the projects in my GitHub so that people could learn to use various machine learning models in their projects and follow some methodologies that I’ve used during my process of understanding data science. Below is the link. Feel free to take a look at my projects and share your feedback and suggestions.
GitHub: https://github.com/suhasmaddali
As I’ve worked on various projects in machine learning, my interest began to sky-rocket in the field, and therefore, I applied to various institutes in the US that were good for a master’s in data science. Finally, I was accepted for a Master’s in Data Science at Northeastern University Khoury College of Computer Sciences. I was really impressed and interested at the same time to pursue my master’s in one of the prestigious institutes in the US. I’m currently still pursuing my master’s at Northeastern and would keep you posted for updates in the future.
I’m also an active member on LinkedIn where I share my latest findings in the field of machine learning with the data science community so that we might find the best solution for complex data science projects. During that time, I also have an opportunity to connect with a recruiter and asked him a few questions about data science and how it is possible for people who don’t have experience in machine learning to enter that field. He told me that in order to become a data scientist or a machine learning engineer, one would require passion and interest in the field. I further asked him the technical skills that were required for a data scientist and the required amount of experience. He told me that in order to become a data scientist, one must add projects in a portfolio demonstrating work experience. Furthermore, he insisted that work experience is just a number used to filter the candidates but does not give a good picture of how well a candidate might perform in the future. Sure, experience does help in getting your foot in the door but ultimately, it is passion towards the field that makes a major difference.
All of this knowledge and useful insights led to understanding and using machine learning concepts that were really influential in the ways in which I solved projects on artificial intelligence. Recently, there has been a big boom in industries and requirements for a machine learning engineer which is a combination of a software engineer and a data scientist role. If an engineer is able to provide solutions in the cloud or in the infrastructure using data science algorithms, there would be a high demand for them.
I’m really passionate about machine learning and data science and the ways in which these algorithms are shaping the ways in which we are learning new things and interpreting the ways of life. I believe that with firm resolution and dedication, anyone who is entering the field can become a data scientist or a machine learning engineer out of passion. Hope you found this article helpful. Feel free to share your thoughts and insights. Thanks!
Feel free to follow me on Medium to get updates on more articles. Furthermore, below are websites where I’m active mostly. Feel free to connect.
LinkedIn: https://www.linkedin.com/in/suhas-maddali-b9b146136/
Facebook: https://www.facebook.com/suhas.maddali
