Introduction to the Blog Series of Machine Learning

Hello Everyone!!!!!

I am Saikrishna Thonduru who is a Master's Graduate, specialized in Automation Technology and passionate about Machine Learning technology. I have great knowledge in Machine Learning, Deep Learning, and computer vision domains through my practical experience from my student job, master thesis, and freelance work. I have been always keen to improve myself rather than comparing with others and love to learn new tools, innovations, and researches about future technologies. Furthermore, I have always been interested in problem-solving in Mathematics since my childhood days and try to master the concepts meticulously.

I want to start a Blog Series to share my knowledge in Machine Learning. I would like to share a short story that could reveal the reason behind commencing my blog series. While pursuing my master’s, particularly in the third semester, I had taken the subject in my academics called Statistics. I was extremely interested in that subject, mastered most of the concepts, and had got a bit curious to implement those concepts in a real-world scenario. After some days of research, I came to know that Machine Learning is widely being used in almost all sectors of industries. At this point, my journey towards Machine Learning has been started. Initially, I was so confused and don’t know where to start. I had been searched many blogs and gone through many websites about Machine Learning for proper documentation or pipeline. Also, I started looking at many blogs or websites to grab info about Machine Learning concepts. Even though, if I find any info regarding the pipeline or concepts of ML that would be too long or too short was available. Finally, I constructed my way to master the concepts. Later, I have got an idea to share the knowledge which I grabbed theoretically and also experienced practically.

The main motto of commencing the Blog Series is to share the Machine Learning Concepts in a simple way without having any chaos regarding the concepts. Also, making clear the questions like what, why, when, and how [WWWH]. Implementation is highly important than simply knowing the concepts. Additionally, I will be giving some more tips and tricks that has been used in ML.

This Blog Series will be extremely useful for people who want to clarify the concepts and simply learn when and how to use those concepts. I will be delivering a total of 10 episodes from the first week of July 2021. Next week, I’ll come up with the roadmap of the ML concepts and also the episodes of the Blog Series. Until then, Happy Learning!!!!!

“Never stop learning because life never stops Teaching”

You can also email me directly or find me on LinkedIn. I’d love to hear from you if I can help you or your team with machine learning.

#MachineLearning #MLEnthusiasts #Constantlearners#MLConcepts




A Machine Learning technology researcher commencing a new Blog Series to make clear ML concepts Simple and ease for everyone.

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Proof of the Law of Large Numbers Part 1: The Weak Law

Action Rules Discovery using Machine Learning

A Common Pitfall in Evaluating Recommendation System

Speech-enhancement with Deep learning

Monte Carlo — one gate to the world of Advanced Risk Analytics in Real Estate

What are tokens in natural language processing (at a high level)?

“Machine Learning? Say what?”

Assessing the Need for Edge-Deployed ML in Your Application

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
sai krishna

sai krishna

A Machine Learning technology researcher commencing a new Blog Series to make clear ML concepts Simple and ease for everyone.

More from Medium

Why are we able to store more data than the allocated space by malloc without giving error!!

Future of Data Mining

Who pays the bill? Predicting whether customers pay their credit card bill

Constructing Chances: Single Game G+ Performance Thresholds in Major League Soccer