Picture this — you want to learn all about machine learning but just can’t find the time. There’s too much to do, whether that’s our professional work or your exams are around the corner. Suddenly, you have a lot of time on your hands and a once-in-a-lifetime opportunity to learn machine learning and apply it!
That’s exactly the opportunity in front of you right now. We are living in unprecedented times with half the world in complete lockdown and following social distancing protocols. There are two types of people emerging during this lockdown:
Learning paths are easily one of the most popular and in-demand resources we curate at the start of the new year. We’ve received a ton of queries recently asking when we would be releasing the learning paths for 2020.
And here we are — we are thrilled to present the first learning path of 2020 for our community!
The learning path for 2020 is the ultimate and most comprehensive collection of resources put together in a structured manner. This learning path is for anyone who wants to make a career in data science. …
Are you ready to take that next big step in your machine learning journey? Working on toy datasets and using popular data science libraries and frameworks is a good start. But if you truly want to stand out from the competition, you need to take a leap and differentiate yourself.
A brilliant way to do this is to do a project on the latest breakthroughs in data science. Want to become a Computer Vision expert? Learn how the latest object detection algorithm works. …
I have conducted tons of interviews for data science positions in the last couple of years. One thing has stood out — aspiring machine learning professionals don’t focus enough on projects that will make them stand out.
And no, I don’t mean online competitions and hackathons (though that is always a plus point to showcase). I’m talking about off-the-cuff experiments you should do using libraries and frameworks that have just been released. This shows the interviewer two broad things:
New to analytics and data science? Interested in finding out how these metrics work in football? Heard of the term ‘xG’ but aren’t sure how it works? You’ve come to the right place!
Analytics is revolutionizing sports as we know it. I’ve been following the analytics trend in American sports for the last decade, thanks largely to Daryl Morey’s “math movement”. It’s awesome to see how teams have changed their strategies, in-game tactics, and even their backroom staff to align themselves with the shifting sands of this revolution.
Football, however, has long fought back against any analytics-driven logic. The usual…
Learn about the most common and popular machine learning applications and use cases in our daily lives!
Picture this — you have an interview tomorrow for a machine learning role you have been aspiring to for a long time. Everything needs to go as per schedule otherwise plans might get messed up.
So, you tell your virtual assistant to:
What would you do if you had the chance to pick the brains behind one of the most popular Natural Language Processing (NLP) libraries of our era? A library that has helped usher in the current boom in NLP applications and nurtured tons of NLP scientists?
Well — you invite the creators on our popular DataHack Radio podcast and let them do the talking! We are delighted to welcome Ines Montani and Matt Honnibal, the developers of spaCy — a powerful and advanced library for NLP.
That’s right — everything you’ve ever wanted to know about the wonderful spaCy…
What’s the key to cracking data science competitions? How do you use this experience to break into the data science industry? We regularly come across these questions from aspiring data scientists wondering how to make a name for themselves in data science.
Who better to answer these questions and provide an in-depth insight into the data science world than a Kaggle Master and a Analytics Vidhya hackathon expert? Ladies and gentlemen, I’m delighted to present Sonny Laskar!
Sonny is a MBA post-graduate from IIM Indore, the place he credits for starting his data science journey. So for any of you…
Check out the top machine learning and data science open source developments from April 2019.
Data science is an ever-evolving field. As data scientists, we need to have our finger on the pulse of the latest algorithms and frameworks coming up in the community.
I’ve found GitHub to be an excellent source of knowledge in that regard. The platform helps me stay current with trending data science topics. I can also look up and download code from leading data scientists and companies — what more could a data scientist ask for? So, if you’re a:
How do computer vision techniques work in an industry setting? How does an organization use data engineering to scale up its operations?
These are questions every aspiring data scientist must be aware of. Dat Tran, Head of Data Science at idealo internet GmbH, is the perfect person to shed light on these questions.
Dat has worked on a variety of data engineering projects before he came to idealo, and now leads a team of data scientists who work on really cool computer vision problems. …