Differences
This shows you the differences between two versions of the page.
|
ep:labs:10 [2021/12/04 16:22] vlad.stefanescu [Exercises] |
ep:labs:10 [2023/10/07 21:56] (current) emilian.radoi [Feedback] |
||
|---|---|---|---|
| Line 11: | Line 11: | ||
| ===== Resources ===== | ===== Resources ===== | ||
| - | The exercises will be solved in Python, using various popular libraries that are usually integrated in machine learning projects: | + | In this lab, we will study basic performance evaluation techniques used in machine learning, covering elementary concepts such as classification, regression, data fitting, clustering and much more. |
| + | |||
| + | You will work in an environment that is easy to use, and provides a couple of tools like manipulating data and visualizing results. We will use a **Jupyer Notebook** hosted on **Google Colab**, which comes with a variety of useful tools already installed. | ||
| + | |||
| + | The exercises will be solved in Python, using popular libraries that are usually integrated in machine learning projects: | ||
| * [[https://scikit-learn.org/stable/documentation.html|Scikit-Learn]]: fast model development, performance metrics, pipelines, dataset splitting | * [[https://scikit-learn.org/stable/documentation.html|Scikit-Learn]]: fast model development, performance metrics, pipelines, dataset splitting | ||
| Line 18: | Line 22: | ||
| * [[https://matplotlib.org/3.1.1/users/index.html|Matplotlib]]: data plotting | * [[https://matplotlib.org/3.1.1/users/index.html|Matplotlib]]: data plotting | ||
| - | All tasks are tutorial based and every exercise will be associated with at least one "**TODO**" within the code. Those tasks can be found in the //exercises// package, but our recommendation is to follow the entire skeleton code for a better understanding of the concepts presented in this laboratory class. Each functionality is properly documented and for some exercises, there are also hints placed in the code. | + | As datasets, we will use some public corpora provided by the Kaggle community: |
| - | <note important> | + | * [[https://www.kaggle.com/uciml/pima-indians-diabetes-database/data|Classification Dataset]] |
| - | Because the various **tasks** and **exercises** are **spread throughout the laboratory text**, they are marked with a ⚠️ emoji. Make sure you look for this emoji so that you don't miss any of them! | + | * [[https://www.kaggle.com/zaraavagyan/weathercsv|Regression dataset]] |
| - | </note> | + | |
| + | You can also check out these cheet sheets for fast reference to the most common libraries: | ||
| + | |||
| + | **Cheat sheets:** | ||
| + | |||
| + | * [[https://perso.limsi.fr/pointal/_media/python:cours:mementopython3-english.pdf)|python]] | ||
| + | * [[https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf|numpy]] | ||
| + | * [[https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Matplotlib_Cheat_Sheet.pdf|matplotlib]] | ||
| + | * [[https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf|sklearn]] | ||
| + | * [[https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf|pandas]] | ||
| <solution -hidden> | <solution -hidden> | ||
| - | Solution: {{:ep:labs:lab_12_ml_revisited_solution.zip}} | + | [[https://colab.research.google.com/drive/1aeV9PGF_uxBA3FoKNMEzsiXMxjVSCcm4?usp=sharing|Solution]] |
| </solution> | </solution> | ||
| + | ===== Tasks ===== | ||
| + | |||
| + | ==== 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**". | ||
| + | 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. | ||
| Line 43: | Line 63: | ||
| - | ===== References ===== | ||
| - | [[https://www.kaggle.com/uciml/pima-indians-diabetes-database/data|Classification Dataset]] | ||
| - | [[https://www.kaggle.com/zaraavagyan/weathercsv|Regression dataset]] | ||
| - | {{namespace>:ep:labs:10:contents:tasks&nofooter&noeditbutton}} | ||
