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ep:labs:10 [2021/12/04 16:24] vlad.stefanescu [References] |
ep:labs:10 [2025/02/11 22:58] (current) cezar.craciunoiu [Lab 9 - Machine Learning Optimization] |
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| - | ====== Lab 10 - Machine Learning ====== | + | ====== Lab 10 - Machine Learning Optimization ====== |
| ===== Objectives ===== | ===== Objectives ===== | ||
| - | * Understand basic concepts of machine learning | + | * TODO |
| - | * Remember examples of real-world problems that can be solved with machine learning | + | |
| - | * Learn the most common performance evaluation metrics for machine learning models | + | |
| - | * Analyse the behaviour of typical machine learning algorithms using the most popular techniques | + | |
| - | * Be able to compare multiple machine learning models | + | |
| ===== Resources ===== | ===== Resources ===== | ||
| - | The exercises will be solved in Python, using various popular libraries that are usually integrated in machine learning projects: | + | TODO |
| - | + | ||
| - | * [[https://scikit-learn.org/stable/documentation.html|Scikit-Learn]]: fast model development, performance metrics, pipelines, dataset splitting | + | |
| - | * [[https://pandas.pydata.org/pandas-docs/stable/|Pandas]]: data frames, csv parser, data analysis | + | |
| - | * [[https://numpy.org/doc/|NumPy]]: scientific computation | + | |
| - | * [[https://matplotlib.org/3.1.1/users/index.html|Matplotlib]]: data plotting | + | |
| - | + | ||
| - | + | ||
| - | [[https://www.kaggle.com/uciml/pima-indians-diabetes-database/data|Classification Dataset]] | + | |
| - | [[https://www.kaggle.com/zaraavagyan/weathercsv|Regression dataset]] | + | |
| - | + | ||
| - | <solution -hidden> | + | |
| - | Solution: {{:ep:labs:lab_12_ml_revisited_solution.zip}} | + | |
| - | </solution> | + | |
| ===== Tasks ===== | ===== Tasks ===== | ||
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| ==== Google Colab Notebook ==== | ==== Google Colab Notebook ==== | ||
| - | For this lab, we will use Google Colab for exploring performance evaluation in machine learning. Please solve your tasks [[https://github.com/vladastefanescu/machine-learning-introduction/blob/main/Machine_Learning_Introduction.ipynb|here]] by clicking "**Open in Colaboratory**". | + | TODO |
| - | + | ||
| - | You can then export this python notebook as a PDF (**File -> Print**) and upload it to **Moodle**. | + | |
| + | ===== Feedback ===== | ||
| + | Please take a minute to fill in the **[[https://forms.gle/NpSRnoEh9NLYowFr5 | feedback form]]** for this lab. | ||
