90Days Data Science Bootcamp: Build Portfolio Of 90 Projects

It looks like you've compiled a comprehensive list of Python development projects, ranging from data science and machine learning to web applications and GUI-based applications. The projects are well-categorized into two sets: the first set focuses on advanced topics in data science, machine learning, and predictive modeling with tools like Auto Keras, TPOT, PyCaret, and H2O Auto ML, while the second set includes a variety of Python-based applications such as image editors, brand identification games, transaction systems, and more, utilizing frameworks like Django and libraries like OpenCV, Tkinter, and PyQt5.

For someone looking to build their skills in both data science and general Python application development, this list provides a great roadmap. Here are some additional tips based on the list you've provided:

1. Prioritize and Plan: With 90 projects, it's important to prioritize based on your interests and goals. Create a study plan that allows you to progressively build upon your skills. You might want to start with simpler projects and gradually move towards more complex ones.

2. Set Milestones: Break down the list into monthly targets. For example, focus on data science projects for the first few months, then switch to Python web and GUI applications. This will help you stay focused and measure your progress.

3. Understand the Prerequisites: Before diving into a project, make sure you understand the prerequisite knowledge required. Some machine learning projects might assume a certain level of statistical understanding, while web applications require familiarity with web frameworks and databases.

4. Document Your Work: Keep a portfolio of your code and projects. GitHub is a great platform for this. Documenting your work not only helps you remember how you solved each problem but also makes it easier to showcase your skills to potential employers or clients.

5. Iterate and Improve: With each project, try to make improvements or add features. This will not only enhance your skills but also give you a more polished portfolio.

6. Contribute to Open Source: Once you're comfortable with a few projects, consider contributing to open-source projects related to Python. This experience is invaluable and can significantly boost your resume.

7. Stay Updated: Python and its libraries are constantly evolving. Stay updated with the latest trends, updates, and best practices in both data science and application development.

8. Networking: Engage with the Python and data science communities online (e.g., Stack Overflow, Reddit, LinkedIn groups). Networking can lead to learning opportunities, job offers, and collaborations.

9. Learn from Each Project: Every project is a lesson in itself. Reflect on what went well and what didn't. Use this feedback to improve your approach to future projects.

10. Balance Theory with Practice: While the list provides practical projects, it's also important to ground your knowledge in theoretical concepts. Understanding the 'why' behind the 'what' will make you a more versatile and knowledgeable programmer.

Remember that the journey of learning is continuous. Take your time, learn at your own pace, and most importantly, enjoy the process of creating and building. Good luck on your Python development adventure!