In this course, I saw the applications of complex mathematical theory to process and aggregate data. So far, I have gone in-depth with programming and theoretical concepts, but this course covers more deeply the intersection of both.
udemy.com/course/the-data-science-course-complete-data-science-bootcamp
Worked on the more practical application of Python. For example, I created a python game where you battle against opponents, where the last one standing wins. There is also added complexity with abilities. I also made a blog using Django and learned more about using git and version control as part of this course.
github.com/Nafisedev/Battle-game
udemy.com/course/python-complete
Using ML algorithms in a real-world problem was a great inspiration. We created a project about working on power supplier data using Azure ML services for future energy generation, storage, and consumption analysis.
github.com/pyladiesams/bootcamp-bringing-ML-models-into-production-intermediary-jun-aug2021
I read this book to learn about applying Python data science libraries with Jupyter Notebook. The book had labs in every chapter and ended with exploring real-world datasets where we learned to look at the data. An example of the datasets is looking at baby names based on birth statistics provided by the United States Social Security Administration (SSA) from 1880 to 2010.
github.com/wesm/pydata-book
goodreads.com/book/show/14744694-python-for-data-analysis
The workshop allowed me to apply my knowledge in real-world applications with current technologies. I used Google Colab to learn the application of machine learning algorithms with Python libraries.
packtpub.com/product/the-data-science-workshop/9781838981266
github.com/PacktWorkshops/The-Data-Science-Workshop
I enjoyed learning about data collection and prep. and the practical work with AWS Sagemaker. We practiced on datasets to classify images of handwritten digits, from zero to nine.
speaking.brunoamaro.com/NFKQfa#s3A404t
github.com/brunoamaroalmeida/hitchhiker-cloud-ml-aws