Kaggle Reviews on Courses, Pricing, Features & Career Impact
Last updated on 3 Dec '24
- About Kaggle
- Why Take up Kaggle Online Courses?
- Types of Kaggle Courses
- How to Build a Kaggle Repository?
- Kaggle Student Reviews
About Kaggle
Founded by Anthony Goldbloom and Ben Hamner in 2010, Kaggle started as a platform to host machine learning competitions. Its initial goal was to connect data scientists with organizations looking for solutions to real-world predictive problems. Today, Kaggle serves over 21 million users across the globe with tools and resources customized for learners, developers, and researchers. For learners, Kaggle provides beginner-friendly competitions, practical exercises, and an active forum to discuss machine learning concepts.
Kaggle is now a subsidiary of Google, and it continues to encourage the progress of data science and machine learning through its competitions, datasets, and community-driven resources. Learners/developers/data scientists/AI ML engineers get access to public notebooks and open-source models and read through solution write-ups provided for past competitions.
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Provider Name |
Kaggle |
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Established In |
2010 |
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Official Website |
https://www.kaggle.com/learn |
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Founded By |
Anthony Goldbloom and Ben Hamner |
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Popular For |
Machine Learning, Data Science, Cloud Computing, Python, Data Visualization |
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Top Courses at Kaggle |
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Learner/User Base |
21 Million+ |
Commonly asked questions On Highlights
Q: What is the format of Kaggle courses?
Kaggle courses are designed to be engaging and practical. They include:
Video Lessons: Pre-recorded videos explaining the key concepts in an easy-to-understand manner.
Quizzes: Quizzes will test your understanding and reinforce learning.
Coding Exercises: These exercises allow you to practice what you've learned by writing code in an interactive environment directly within the course platform.
Projects: Practical, hands-on projects where you can apply concepts to solve real-world problems.
Notebooks: Kaggle courses often provide interactive Jupyter Notebooks, which learners can use to run and modify code.
Upvote 
Q: Do Kaggle provide certificates on completion of courses?
Yes, Kaggle provides certificates of completion for each course. After completing a course, learners can download and share the certificate, which can be added to resumes, LinkedIn profiles, and personal portfolios. These certificates can help demonstrate a learner's data Science or machine learning skills to potential employers or colleagues.
Upvote Q: Are Kaggle courses free?
Yes, all Kaggle courses are completely free. Kaggle does not charge for access to any of its courses, quizzes, or coding exercises. This makes Kaggle an excellent platform for learners who want to gain data Science and machine learning skills without any financial investment. The only requirement is that you create a Kaggle account to start accessing the courses.
Upvote Q: Does Kaggle allow the users to download the course materials?
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.
Upvote Why Take up Kaggle Online Courses?
Kaggle is a powerful platform for data science enthusiasts and professionals, offering numerous benefits that can enhance your profile. Here are some reasons that make Kaggle a good platform to explore -
Free Online Courses
Kaggle offers a variety of free courses on data science and machine learning, allowing users to learn new skills at no cost.
Hands-on Learning
Kaggle provides access to public data sets, machine learning tutorials, and even code notebooks that help you learn and practice data science skills, thereby improving your employability. The library of pre-trained models in Kaggle, training datasets, and hosting competitions are valuable for improving methodologies in machine learning.
Portfolio Development:
Learners can work on project notebooks and share them during the learning process. These notebooks become a portfolio of the skills developed in practice and ways of solving problems for employers.
Networking Opportunities:
Learners get in touch with Kaggle's active global community, collaborating with data scientists, researchers, and industry professionals, which is helpful in building industry level connections and learning from the experience of others.
Improved Problem-Solving Skills:
Since you work with real-life datasets, you build on your critical thinking and problem solving skills. These skills are valuable in many data-related roles, from analysis to strategy development.
Industry-Relevant Learning:
Course content includes real-life machine learning applications, which can be very helpful for the learners to understand how to use these techniques in a professional setting.
Preparation for Advanced Roles:
By mastering advanced topics such as deep learning or feature engineering, participants will be well placed for a career in specialized data science roles, like AI engineer or machine learning engineer.
Access to Tools and Resources:
Learners get hands-on experience with essential tools and frameworks through the platform, making them job-ready with practical expertise.
Types of Kaggle Courses
Kaglle offers two types of learning resources, as discussed below -
Kaggle Online Courses
Kaggle provides a wide range of courses on multiple topics that are relevant to data science and machine learning, including:
- Python Programming: Learn basic concepts such as variables, loops, conditionals, and functions, and Python libraries, such as Pandas, NumPy, and Matplotlib.
- Machine Learning: Learn the core ML algorithms such as linear regression, decision trees, random forests, ensemble learning, etc.
- Deep Learning: Covers topics like neural networks, CNNs, RNNs, etc.
- Data Visualization: Learn how to present you data in the most effective way using Matplotlib, Seaborn, Plotly, etc.
- SQL: Courses that cover how to work on databases. Learn the basics of SQL query and get hands-on with relational database data.
- Feature Engineering: Kaggle’s feature engineering courses focus on Handling Missing Data, Categorical Data, Normalization/Standardization, and Domain-Specific Features
- Data Science Best Practices: Focuses on principles and techniques that ensure data science projects are efficient, reproducible, and maintainable This introduces various techniques, such as model evaluation, cross-validation, hyperparameter tuning, etc.
Kaggle Guides
Learn Guides by Kaggle are curated collections of high-quality learning resources created by the Kaggle community. These guides help learners dive deeper into specific data science, machine learning, and other topics.
To get the most out of a Learn Guide, you should create a private copy of the associated tutorial notebooks to run the code yourself and experiment with it. If you have any questions or if you are stuck somewhere, connect with the Kaggle community by posting comments or asking questions in the notebook's discussion section.
Learn Guides may include links to helpful YouTube videos, Kaggle discussions, and other valuable external resources, offering a well-rounded learning experience. Here are some of the listed guides on Kaggle -
- 5-Day Intensive Generative AI Course: A 5-Day Intensive Generative AI Course hosted by Google.
- JAX Guide: Learn how to use JAX to build and train deep learning models.
- TensorFlow Guide: Learn about TensorFlow, a library for developing and training machine learning models.
- Transfer Learning for Computer vision (CV) Guide: Explore how to use a pre-trained model towards a new task, without having to train a model from scratch.
- Kaggle Competitions Guide: Kaggle Competitions are machine learning challenges, often with large prizes.
- Natural Language Processing Guide: Learn to build models to demonstrate human-level understanding of written and spoken text.
Point to note: Please be aware that completion certificates are only awarded by official Kaggle online courses.
How to Build a Kaggle Repository?
Follow the steps below to start on Kaggle and build your Kaggle repository,
Choose Your Programming Platform
Python is the most recommended platform for Kaggle due to its versatility and rich ecosystem of libraries, including TensorFlow, PyTorch, Scikit-learn, and Pandas.
Ensure you are comfortable with Python basics and data manipulation and visualization tools like NumPy, Matplotlib, and Seaborn.
Practice on standard datasets
After you pick the platform, you would need to practice on real data sets. Start with a suite of standard machine learning problems. You will get hands-on experience with machine learning tools, algorithms and datasets. Popular beginner datasets include the Iris dataset, Titanic survival dataset, and MNIST handwritten digits dataset.
Practice Kaggle problems
After you have worked on some real data sets, dig into some old Kaggle problems. Focus on understanding problem statements, exploring the data, and creating baseline models.
Experiment with advanced techniques such as hyperparameter tuning, ensemble learning, and feature selection. This will help you learn different types of problems and apply new techniques to solve them.
Compete
While you try your hands on Kaggle competitions, you need to pick one problem at a time and push it. You will either succeed, or there are chances that you may get stuck.
Don't give up. The target is to achieve a top 25% or 10% result on the private leaderboard. Share your leaderboard on the forum. You will learn from others.
Document and Refine Your Repository
Save your best solutions and organize them in a structured repository. You can also include detailed comments, visualizations, and explanations for clarity. We advise you to update your repository with new learnings and techniques regularly.
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Updated on Dec 3, 2024
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