Where do Data Scientists assemble to compete? Kaggle. Shashank Mishra (Expedia Data Engineer III) goes over everything you need to know about the platform. Check out our FREE masterclasses by leading industry experts now: https://www.scaler.com/events?utm_source=osocial&utm_medium=googleyoutube&utm_campaign=brand_scaler_events_osocial_googleyoutube_Kaggle-for-beginners&utm_content=description
What is Kaggle?
Data scientists and machine learning aficionados can connect online at Kaggle. Users of Kaggle can work together, discover and share datasets, use notebooks with GPU integration, and compete with other data scientists to solve data science challenges.
All the code and data you need to complete your data science tasks are available inside Kaggle. Utilise over 19,000 public datasets and 200,000 public notebooks to quickly complete any study.
What is Data Science?
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
What is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence (AI) that enables software applications to grow increasingly effective at predicting outcomes without explicitly programming them to do so. In order to forecast new output values, machine learning algorithms use historical data as input.
Who are Data Scientists?
Data scientists choose the questions their team should pose and work out how to use data to provide answers. They frequently create predictive models for forecasting and theorising.
Who are ML engineers?
A programmer who creates and develops software that can handle artificial intelligence and machine learning (AI/ML) models is known as a machine learning engineer (ML engineer).
Check out this detailed tutorial on Introduction to Kaggle.
Topics covered in this video:
0:00 – Introduction to Kaggle
2:25 – Step-by-step guide to use Kaggle
12:18 – Data Analysis on Kaggle Notebook
—————————————- About SCALER ————————————————–
A transformative tech school, creating talent with impeccable skills. Upskill and Create Impact.
Learn more about Scaler: http://bit.ly/3mcbILC
📌 Follow us on Social and be a part of an amazing tech community📌
👉 Meet like-minded coder folks on Discord – https://discord.com/invite/ejFeksEtTq
👉 Tweets you cannot afford to miss out on – https://twitter.com/scaler_official
👉 Check out student success stories, expert opinions, and live classes on Linkedin – https://www.linkedin.com/school/scalerofficial
👉 Explore value-packed reels, carousels and get access to exclusive updates on Instagram – https://www.instagram.com/scaler_official/
📢 Be a part of our one of a kind telegram community: https://t.me/Scalercommunity
🔔 Hit that bell icon to get notified of all our new videos 🔔
If you liked this video, please don’t forget to like and comment. Never miss out on our exclusive videos to help boost your coding career! Subscribe to Scaler now!
https://www.youtube.com/Scaler?sub_confirmation=1
#kaggle #kaggletutorial #datascience #machinelearning #scaler
source
Thank you sir 🙏
Here's the code written in the video, for your reference➡https://github.com/Kodeedudey/ScalerTutorialFiles/blob/main/demo-notebook.ipynb