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Commonly asked questions
On All Courses
Q:   Does Kaggle allow the users to download the course materials?
A: 

While video lessons and quizzes are only accessible online, Kaggle allows learners to download the notebooks and use their favorite Jupyter IDE (e.g.: VS Code, Jupyter Lab, JetBrains Datalore, Google Colab, AWS Sagemaker). These downloadable resources are helpful for learners who want to continue practising even when not connected to the internet.

Q:   Can I use Kaggle courses to prepare for competitions?
A: 

Yes! Kaggle courses are designed to help learners prepare for competitions. The courses cover key topics for participating in Kaggle competitions, such as data preprocessing, feature engineering, and model evaluation. By applying the techniques learned in the courses, learners can develop the skills to build strong models and perform well in Kaggle challenges.

Q:   How do I enroll in Kaggle courses?
A: 

Enrolling in Kaggle courses is simple. Follow below steps -

  • Create a free account on Kaggle.

  • Navigate to the "Learn" section once logged in.

  • Browse through the available courses and pick the one that interests you.

  • Enrollment is instant, and you can start accessing the course content immediately.

Q:   How long does it take to complete a Kaggle course?
A: 

Most Kaggle courses are short and designed to be completed in a few hours, especially those on fundamentals. However, learners can take as much time as they need to grasp the material, as the courses are self-paced fully. Some advanced topics, like deep learning, may take longer to complete, especially if you spend additional time practising and implementing the techniques on real-world datasets.

Kaggle
Students Ratings & Reviews

4.6/5
Verified Icon80 Ratings
H
Hemkumar Vipulbhai Bhavsar
Python
4
Learning Experience: course content is good, platform is very user friendly and there is five questions after every module so the challenges of the course are very helpful for concepts.
Faculty: This course is self-learning based there is no faculties. The course curriculum is very simple understandable for beginners.
Reviewed on 4 Mar 2023Read More
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R
Ramanan
Python
4
Learning Experience: It was a very good platform to learn python as well as other programming languages. We can use hint if we want and we can also view solutions for the problems if we have vo idea
Faculty: It's a self interest platform and if you are loyal, you can gain knowledge The questions are really thinkable and creative. I like the way the questions are explained. That looks professional
Reviewed on 3 Mar 2023Read More
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S
siddheshwar paikroy
Data Cleaning
5
Learning Experience: It has quality content that is enough to start working on projects.
Faculty: It is self learning course, you have to read the content provided and do some exercises correctly to proceed to other modules. it contains well designed modules which have cintent that focuses on the job to be done but most efficiently.
Course Support: It is one of the pre requisites required for the job profile
Reviewed on 25 Feb 2023Read More
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B
Bhushan Karma
Python
5
Learning Experience: The course is quite structured and easy to grasp.one can easily understand the concepts through various practice labs and practice sets. There are realword projects too.
Faculty: The faculty is quiet good and they have met good and structured material .learning through peer graded assignment is quite fun and a good learning experience Each concept comes with their own assesments and assignments. IBM platform is really good for programming and data science
Course Support: The course had impact from basic to advance level which makes it a complete guide to learn data science
Reviewed on 19 Feb 2023Read More
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S
siddheshwar paikroy
Python
5
Learning Experience: The topics covered are enough and to the point for data analytics.
Faculty: so it is basically DIY course it's basically python coding and how to optimise the code
Course Support: learnt about new libraries and some new concepts
Reviewed on 11 Feb 2023Read More
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S
Saraswati Tiwari
Intro to Machine Learning
5
Learning Experience: This course is from Kaggle which is a large community. There is a tutorial plus excercise to do where we build our first machine learning model. In this course we learn about Model Validation, Undercutting and Overfitting & Random Forests. And in last there is a machine learning competition.
Faculty: Faculty is very cooperative... After completing the course we will gain a certificate of this course by the instructor. Here we gain a practical knowledge. This course is free of cost. In this course we learn about to build ML models.
Course Support: Very supportive
Reviewed on 28 Jan 2023Read More
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