Let’s admit that we all love weekends—time to meet new people, learn new things and do things that you love. So one Sunday in August, we decided to explore the world of machine learning (ML) with the young Humans of Data, including college students and machine learning enthusiasts.
Here’s a quick recap of the event and links to the resources shared by the amazing speakers.
Basics of ML
By Shreya Pandey
As it was a beginner-friendly event, the day started with Shreya introducing the attendees to the world of machine learning. Shreya, a student at the Delhi College of Engineering, explained that machine learning helps computer programs access data and learn, without much human intervention or programming.
Her session covered the following:
- Difference between machine learning and programming
- Relationship between deep learning, machine learning and artificial intelligence (Cheat code: deep learning is a subset of machine learning, which is a subset of artificial intelligence)
- Working knowledge of machine learning and basic terminology
- Regression vs classification
If you are interested in learning more, you can check out the slides from the session here.
Understanding ML Application in HR Analytics
By Jitendra Gaur
Jitendra Gaur, a Data Scientist at IBM, shared his insights and experience of applying machine learning models in HR analytics. He also shared the most common data problems faced by people working on machine learning. And, they were oh so relatable!
Machine Learning from the Ground Up on Google Cloud Platform
By Vaibhav Srivastav
We deal with vast amounts of data every single day! And on top of this, the data is often in different formats (mostly non-usable). But what makes data beautiful are the patterns! And machine learning involves teaching a computer to recognize those patterns.
The session by Vaibhav, a Data Scientist at Deloitte Consulting, involved learning about machine learning in detail and applying ML principles while working on Google Cloud Platform (GCP). The session covered the following:
- Basics and use cases of ML
- Three categories of ML
- ML output
- Regression and classification
- Probability estimation
- ML activity on GCP
Introduction to Deep Vision in Keras
By Prakhar Srivastava
Remember how we were taught in school that the body is a machine? In his talk, Prakhar Srivastava, an ML Engineer at Atlan, shared an analogy comparing everything we see to training data and everything we infer to target output. Interesting, right? Eyes are *clearly* the best machines (pun intended), and vision is the learning process!
The session further covered how a linear machine fails when it comes to vision. Wondering why? Because a linear machine is only capable of interpreting one dimension! So to detect multiple dimensions in learning, deep learning in computer vision comes into the picture. In this session, Prakhar used Keras to explore deep learning and its applications in computer vision.
If this was enough to make you interested in the holy grail of computer vision, here are three things from Prakhar’s session for your curious mind to explore:
- The Cambrian explosion
- The evolution of the human eye
- The interesting debate of vision vs language
Natural Language Processing: Making a Machine Read
By Kautuk Kundan
If you write regularly on the web, chances are you use the Grammarly app to check grammar. Or if you enjoy reading texts originally written in different languages, you probably use Google Translator. These are just some uber-awesome examples of the applications of NLP or Natural Language Processing. As the name indicates, NLP uses computational techniques to process, analyze and understand natural languages and speech.
During his session, Kautuk, a Google MLCC Facilitator, explained the basics of NLP and its applications in today’s world. His session covered the following:
- Basics of NLP and its applications
- Basic terminology
- Stemming vs lemmatization
- ML pipeline
- Common NLP techniques
- Sample code
Tweet About It or It Never Happened!
We absolutely enjoyed hosting the event, bringing together young, curious minds of the humans of data. And the love on Twitter makes us think that our community loved the event just as much as we did!
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