For Improving and Staying Up to Date as a Data Scientist. This feat does not devalue the importance of finding mentors in different stages and areas of our lives.
Finding great mentors are hard to come by these days. With so much information and so many opinions flooding the internet, finding an authority in a specific field can be quite tough.
This feat does not devalue the importance of finding mentors in different stages and areas of our lives. Mentorship has long been considered the most effective way to learn and cut your learning curve in half. Heck, there’s even a Bible scripture on it – Proverbs 19:20-NLT “_Get all the advice and instruction you can, so you will be wise the rest of your life_”.
“If I have seen further it is by standing on the shoulders of Giants” – Isaac Newton
With that being said, I thought it necessary to curate a list of effective Data Science professionals that we should all be following, specifically on LinkedIn.
Coming up with this list was very difficult and there were so many names I could have added, such as Dat Tran, Kevin Tran, and Steve Nouri to name a few. But I thought “Nah” these names come up so frequently — people should know and be following them by now. I wanted new blood, names that I don’t see thrown about as much but are doing amazing things for the community.
Note: I must also consider that I do not know the whole population of Data Scientist doing amazing work on LinkedIn. If you wish, feel free to comment some names and add their LinkedIn profiles so that we can give them a follow.
He is the world’s first 4x Kaggle Grandmaster, an Author of one of the most exciting Machine Learning books this year, a Youtuber and Chief Data Scientist at Boost.AI.
If you follow me on LinkedIn, you probably knew that this was coming since I am constantly sharing his post. I personally take tons of inspiration from Abhishek because of how practical he is — everything is applied. I don’t think I’ve ever seen him share something without giving a real world example that is relatable to.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.
Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different
Artificial Intelligence (AI) will and is currently taking over an important role in our lives — not necessarily through intelligent robots.