Kaggle Reviews on Courses, Pricing, Features & Career Impact

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.

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.

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.