Difficulty
easy
Average duration
3 hrs
Technologies
Data Science
Numpy
jupyter notebook
opencv
scikit-learn
matplotlib
Pandas
Machine Learning
Difficulty
easy
Average duration
3 hrs
Technologies
Data Science
Numpy
jupyter notebook
opencv
scikit-learn
matplotlib
Pandas
Machine Learning
Follow the instructions below:
Once you start working on the project, you will see a file ./project.ipynb
that contains a series of exercises.
Before starting, make sure to select the appropriate Kernel.
A list with available options will be displayed. Select "Python Environments" and choose the Python version you want to use.
devcontainer.json
file, as this is the recommended one for the project.All set! Now you can start solving the exercises one by one. Remember to read each statement carefully and apply what you have learned. 🚀
Once you complete the exercises, follow these steps to submit them correctly:
Save and commit the changes in your local repository:
1git add . 2git commit -m "Completed exercises"
Push the changes to GitHub with:
1git push origin main
Go to 4Geeks.com to submit the link to your repository.
Difficulty
easy
Average duration
3 hrs
Technologies
Data Science
Numpy
jupyter notebook
opencv
scikit-learn
matplotlib
Pandas
Machine Learning
Difficulty
easy
Average duration
3 hrs
Technologies
Data Science
Numpy
jupyter notebook
opencv
scikit-learn
matplotlib
Pandas
Machine Learning
Difficulty
easy
Average duration
3 hrs
Technologies
Data Science
Numpy
jupyter notebook
opencv
scikit-learn
matplotlib
Pandas
Machine Learning
Difficulty
easy
Average duration
3 hrs
Technologies
Data Science
Numpy
jupyter notebook
opencv
scikit-learn
matplotlib
Pandas
Machine Learning
Difficulty
easy
Average duration
3 hrs
Technologies
Data Science
Numpy
jupyter notebook
opencv
scikit-learn
matplotlib
Pandas
Machine Learning
Difficulty
easy
Average duration
3 hrs
Technologies
Data Science
Numpy
jupyter notebook
opencv
scikit-learn
matplotlib
Pandas
Machine Learning