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update to scikit-learn 0.23.2 and Python 3.9.1

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Kevin Markham 2021-03-02 08:53:12 -05:00
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127
01_machine_learning_intro.ipynb
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@ -4,16 +4,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# What is machine learning, and how does it work? ([video #1](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1))\n",
"# What is Machine Learning, and how does it work? ([video #1](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1))\n",
"\n",
"Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos)."
"Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Machine learning](images/01_robot.png)"
"![Machine Learning](images/01_robot.png)"
]
},
{
@ -22,19 +22,19 @@
"source": [
"## Agenda\n",
"\n",
"- What is machine learning?\n",
"- What are the two main categories of machine learning?\n",
"- What are some examples of machine learning?\n",
"- How does machine learning \"work\"?"
"- What is Machine Learning?\n",
"- What are the two main categories of Machine Learning?\n",
"- What are some examples of Machine Learning?\n",
"- How does Machine Learning \"work\"?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is machine learning?\n",
"## What is Machine Learning?\n",
"\n",
"One definition: \"Machine learning is the semi-automated extraction of knowledge from data\"\n",
"One definition: \"Machine Learning is the semi-automated extraction of knowledge from data\"\n",
"\n",
"- **Knowledge from data**: Starts with a question that might be answerable using data\n",
"- **Automated extraction**: A computer provides the insight\n",
@ -45,7 +45,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## What are the two main categories of machine learning?\n",
"## What are the two main categories of Machine Learning?\n",
"\n",
"**Supervised learning**: Making predictions using data\n",
" \n",
@ -81,14 +81,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## How does machine learning \"work\"?\n",
"## How does Machine Learning \"work\"?\n",
"\n",
"High-level steps of supervised learning:\n",
"\n",
"1. First, train a **machine learning model** using **labeled data**\n",
"1. First, train a **Machine Learning model** using **labeled data**\n",
"\n",
" - \"Labeled data\" has been labeled with the outcome\n",
" - \"Machine learning model\" learns the relationship between the attributes of the data and its outcome\n",
" - \"Machine Learning model\" learns the relationship between the attributes of the data and its outcome\n",
"\n",
"2. Then, make **predictions** on **new data** for which the label is unknown"
]
@ -111,7 +111,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Questions about machine learning\n",
"## Questions about Machine Learning\n",
"\n",
"- How do I choose **which attributes** of my data to include in the model?\n",
"- How do I choose **which model** to use?\n",
@ -126,7 +126,7 @@
"source": [
"## Resources\n",
"\n",
"- Book: [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (section 2.1, 14 pages)\n",
"- Book: [An Introduction to Statistical Learning](https://www.statlearning.com/) (section 2.1, 14 pages)\n",
"- Video: [Learning Paradigms](http://work.caltech.edu/library/014.html) (13 minutes)"
]
},
@ -137,102 +137,9 @@
"## Comments or Questions?\n",
"\n",
"- Email: <kevin@dataschool.io>\n",
"- Website: http://dataschool.io\n",
"- Website: https://www.dataschool.io\n",
"- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
]
},
{
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"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
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@ -251,7 +158,7 @@
"name": "python",
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121
02_machine_learning_setup.ipynb
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@ -4,9 +4,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setting up Python for machine learning: scikit-learn and Jupyter Notebook ([video #2](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2))\n",
"# Setting up Python for Machine Learning: scikit-learn and Jupyter Notebook ([video #2](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2))\n",
"\n",
"Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
"Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
"\n",
"**Note:** Since the video recording, the official name of the \"IPython Notebook\" was changed to \"Jupyter Notebook\". However, the functionality is the same."
]
@ -38,7 +38,7 @@
"\n",
"### Benefits:\n",
"\n",
"- **Consistent interface** to machine learning models\n",
"- **Consistent interface** to Machine Learning models\n",
"- Provides many **tuning parameters** but with **sensible defaults**\n",
"- Exceptional **documentation**\n",
"- Rich set of functionality for **companion tasks**\n",
@ -46,14 +46,14 @@
"\n",
"### Potential drawbacks:\n",
"\n",
"- Harder (than R) to **get started with machine learning**\n",
"- Harder (than R) to **get started with Machine Learning**\n",
"- Less emphasis (than R) on **model interpretability**\n",
"\n",
"### Further reading:\n",
"\n",
"- Ben Lorica: [Six reasons why I recommend scikit-learn](http://radar.oreilly.com/2013/12/six-reasons-why-i-recommend-scikit-learn.html)\n",
"- scikit-learn authors: [API design for machine learning software](http://arxiv.org/pdf/1309.0238v1.pdf)\n",
"- Data School: [Should you teach Python or R for data science?](http://www.dataschool.io/python-or-r-for-data-science/)"
"- Ben Lorica: [Six reasons why I recommend scikit-learn](https://www.oreilly.com/content/six-reasons-why-i-recommend-scikit-learn/)\n",
"- scikit-learn authors: [API design for machine learning software](https://arxiv.org/pdf/1309.0238v1.pdf)\n",
"- Data School: [Should you teach Python or R for data science?](https://www.dataschool.io/python-or-r-for-data-science/)"
]
},
{
@ -69,9 +69,9 @@
"source": [
"## Installing scikit-learn\n",
"\n",
"**Option 1:** [Install scikit-learn library](http://scikit-learn.org/stable/install.html) and dependencies (NumPy and SciPy)\n",
"**Option 1:** [Install scikit-learn library](https://scikit-learn.org/stable/install.html) and dependencies (NumPy and SciPy)\n",
"\n",
"**Option 2:** [Install Anaconda distribution](https://www.anaconda.com/download/) of Python, which includes:\n",
"**Option 2:** [Install Anaconda distribution](https://www.anaconda.com/products/individual) of Python, which includes:\n",
"\n",
"- Hundreds of useful packages (including scikit-learn)\n",
"- IPython and Jupyter Notebook\n",
@ -124,9 +124,9 @@
"\n",
"### IPython, Jupyter, and Markdown resources:\n",
"\n",
"- [nbviewer](http://nbviewer.jupyter.org/): view notebooks online as static documents\n",
"- [IPython documentation](http://ipython.readthedocs.io/en/stable/)\n",
"- [Jupyter Notebook quickstart](http://jupyter.readthedocs.io/en/latest/content-quickstart.html)\n",
"- [nbviewer](https://nbviewer.jupyter.org/): view notebooks online as static documents\n",
"- [IPython documentation](https://ipython.readthedocs.io/en/stable/)\n",
"- [Jupyter Notebook quickstart](https://jupyter.readthedocs.io/en/latest/content-quickstart.html)\n",
"- [GitHub's Mastering Markdown](https://guides.github.com/features/mastering-markdown/): short guide with lots of examples"
]
},
@ -149,102 +149,9 @@
"## Comments or Questions?\n",
"\n",
"- Email: <kevin@dataschool.io>\n",
"- Website: http://dataschool.io\n",
"- Website: https://www.dataschool.io\n",
"- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
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" width: 90%;\n",
"/* margin-left:auto;*/\n",
"/* margin-right:auto;*/\n",
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" ul {\n",
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" color: rgb( 240, 20, 20 )\n",
" }\n",
"</style>\n",
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" extensions: [\"AMSmath.js\"]\n",
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" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
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"def css_styling():\n",
" styles = open(\"styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
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@ -263,7 +170,7 @@
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"version": "3.9.1"
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144
03_getting_started_with_iris.ipynb
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@ -6,9 +6,9 @@
"source": [
"# Getting started in scikit-learn with the famous iris dataset ([video #3](https://www.youtube.com/watch?v=hd1W4CyPX58&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=3))\n",
"\n",
"Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
"Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
"\n",
"**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)."
"**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16."
]
},
{
@ -17,9 +17,9 @@
"source": [
"## Agenda\n",
"\n",
"- What is the famous iris dataset, and how does it relate to machine learning?\n",
"- What is the famous iris dataset, and how does it relate to Machine Learning?\n",
"- How do we load the iris dataset into scikit-learn?\n",
"- How do we describe a dataset using machine learning terminology?\n",
"- How do we describe a dataset using Machine Learning terminology?\n",
"- What are scikit-learn's four key requirements for working with data?"
]
},
@ -47,8 +47,19 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# added empty cell so that the cell numbering matches the video"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
@ -57,14 +68,14 @@
" <iframe\n",
" width=\"300\"\n",
" height=\"200\"\n",
" src=\"http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
" src=\"https://www.dataschool.io/files/iris.txt\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
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"<IPython.lib.display.IFrame at 0x7fe408230e80>"
]
},
"execution_count": 2,
@ -74,17 +85,17 @@
],
"source": [
"from IPython.display import IFrame\n",
"IFrame('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', width=300, height=200)"
"IFrame('https://www.dataschool.io/files/iris.txt', width=300, height=200)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Machine learning on the iris dataset\n",
"## Machine Learning on the iris dataset\n",
"\n",
"- Framed as a **supervised learning** problem: Predict the species of an iris using the measurements\n",
"- Famous dataset for machine learning because prediction is **easy**\n",
"- Famous dataset for Machine Learning because prediction is **easy**\n",
"- Learn more about the iris dataset: [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets/Iris)"
]
},
@ -130,7 +141,9 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"metadata": {
"scrolled": true
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"outputs": [
{
"name": "stdout",
@ -170,10 +183,10 @@
" [5.4 3.4 1.5 0.4]\n",
" [5.2 4.1 1.5 0.1]\n",
" [5.5 4.2 1.4 0.2]\n",
" [4.9 3.1 1.5 0.1]\n",
" [4.9 3.1 1.5 0.2]\n",
" [5. 3.2 1.2 0.2]\n",
" [5.5 3.5 1.3 0.2]\n",
" [4.9 3.1 1.5 0.1]\n",
" [4.9 3.6 1.4 0.1]\n",
" [4.4 3. 1.3 0.2]\n",
" [5.1 3.4 1.5 0.2]\n",
" [5. 3.5 1.3 0.3]\n",
@ -298,7 +311,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Machine learning terminology\n",
"## Machine Learning terminology\n",
"\n",
"- Each row is an **observation** (also known as: sample, example, instance, record)\n",
"- Each column is a **feature** (also known as: predictor, attribute, independent variable, input, regressor, covariate)"
@ -378,7 +391,7 @@
"## Requirements for working with data in scikit-learn\n",
"\n",
"1. Features and response are **separate objects**\n",
"2. Features and response should be **numeric**\n",
"2. Features should always be **numeric**, and response should be **numeric** for regression problems\n",
"3. Features and response should be **NumPy arrays**\n",
"4. Features and response should have **specific shapes**"
]
@ -458,9 +471,9 @@
"source": [
"## Resources\n",
"\n",
"- scikit-learn documentation: [Dataset loading utilities](http://scikit-learn.org/stable/datasets/)\n",
"- scikit-learn documentation: [Dataset loading utilities](https://scikit-learn.org/stable/datasets.html)\n",
"- Jake VanderPlas: Fast Numerical Computing with NumPy ([slides](https://speakerdeck.com/jakevdp/losing-your-loops-fast-numerical-computing-with-numpy-pycon-2015), [video](https://www.youtube.com/watch?v=EEUXKG97YRw))\n",
"- Scott Shell: [An Introduction to NumPy](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf) (PDF)"
"- Scott Shell: [An Introduction to NumPy](https://sites.engineering.ucsb.edu/~shell/che210d/numpy.pdf) (PDF)"
]
},
{
@ -470,102 +483,9 @@
"## Comments or Questions?\n",
"\n",
"- Email: <kevin@dataschool.io>\n",
"- Website: http://dataschool.io\n",
"- Website: https://www.dataschool.io\n",
"- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
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" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
]
}
],
"metadata": {
@ -584,7 +504,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.9.1"
}
},
"nbformat": 4,

140
04_model_training.ipynb
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@ -4,11 +4,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training a machine learning model with scikit-learn ([video #4](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4))\n",
"# Training a Machine Learning model with scikit-learn ([video #4](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4))\n",
"\n",
"Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
"Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
"\n",
"**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)."
"**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16."
]
},
{
@ -19,7 +19,7 @@
"\n",
"- What is the **K-nearest neighbors** classification model?\n",
"- What are the four steps for **model training and prediction** in scikit-learn?\n",
"- How can I apply this pattern to **other machine learning models**?"
"- How can I apply this pattern to **other Machine Learning models**?"
]
},
{
@ -29,6 +29,15 @@
"## Reviewing the iris dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# added empty cell so that the cell numbering matches the video"
]
},
{
"cell_type": "code",
"execution_count": 2,
@ -41,14 +50,14 @@
" <iframe\n",
" width=\"300\"\n",
" height=\"200\"\n",
" src=\"http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
" src=\"https://www.dataschool.io/files/iris.txt\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x10fb4e4a8>"
"<IPython.lib.display.IFrame at 0x7f8c18558700>"
]
},
"execution_count": 2,
@ -58,7 +67,7 @@
],
"source": [
"from IPython.display import IFrame\n",
"IFrame('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', width=300, height=200)"
"IFrame('https://www.dataschool.io/files/iris.txt', width=300, height=200)"
]
},
{
@ -119,7 +128,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"*Image Credits: [Data3classes](http://commons.wikimedia.org/wiki/File:Data3classes.png#/media/File:Data3classes.png), [Map1NN](http://commons.wikimedia.org/wiki/File:Map1NN.png#/media/File:Map1NN.png), [Map5NN](http://commons.wikimedia.org/wiki/File:Map5NN.png#/media/File:Map5NN.png) by Agor153. Licensed under CC BY-SA 3.0*"
"*Image Credits: [Data3classes](https://commons.wikimedia.org/wiki/File:Data3classes.png#/media/File:Data3classes.png), [Map1NN](https://commons.wikimedia.org/wiki/File:Map1NN.png#/media/File:Map1NN.png), [Map5NN](https://commons.wikimedia.org/wiki/File:Map5NN.png#/media/File:Map5NN.png) by Agor153. Licensed under CC BY-SA 3.0*"
]
},
{
@ -228,9 +237,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
" weights='uniform')\n"
"KNeighborsClassifier(n_neighbors=1)\n"
]
}
],
@ -256,9 +263,7 @@
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
" weights='uniform')"
"KNeighborsClassifier(n_neighbors=1)"
]
},
"execution_count": 8,
@ -390,8 +395,8 @@
"# import the class\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"# instantiate the model (using the default parameters)\n",
"logreg = LogisticRegression()\n",
"# instantiate the model\n",
"logreg = LogisticRegression(solver='liblinear')\n",
"\n",
"# fit the model with data\n",
"logreg.fit(X, y)\n",
@ -406,9 +411,9 @@
"source": [
"## Resources\n",
"\n",
"- [Nearest Neighbors](http://scikit-learn.org/stable/modules/neighbors.html) (user guide), [KNeighborsClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) (class documentation)\n",
"- [Logistic Regression](http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) (user guide), [LogisticRegression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) (class documentation)\n",
"- [Videos from An Introduction to Statistical Learning](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)\n",
"- [Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html) (user guide), [KNeighborsClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) (class documentation)\n",
"- [Logistic Regression](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) (user guide), [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) (class documentation)\n",
"- [Videos from An Introduction to Statistical Learning](https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)\n",
" - Classification Problems and K-Nearest Neighbors (Chapter 2)\n",
" - Introduction to Classification (Chapter 4)\n",
" - Logistic Regression and Maximum Likelihood (Chapter 4)"
@ -421,102 +426,9 @@
"## Comments or Questions?\n",
"\n",
"- Email: <kevin@dataschool.io>\n",
"- Website: http://dataschool.io\n",
"- Website: https://www.dataschool.io\n",
"- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
" }\n",
" div.cell{\n",
" width: 90%;\n",
"/* margin-left:auto;*/\n",
"/* margin-right:auto;*/\n",
" }\n",
" ul {\n",
" line-height: 145%;\n",
" font-size: 90%;\n",
" }\n",
" li {\n",
" margin-bottom: 1em;\n",
" }\n",
" h1 {\n",
" font-family: Helvetica, serif;\n",
" }\n",
" h4{\n",
" margin-top: 12px;\n",
" margin-bottom: 3px;\n",
" }\n",
" div.text_cell_render{\n",
" font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
" line-height: 145%;\n",
" font-size: 130%;\n",
" width: 90%;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
" }\n",
" .CodeMirror{\n",
" font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
" }\n",
"/* .prompt{\n",
" display: None;\n",
" }*/\n",
" .text_cell_render h5 {\n",
" font-weight: 300;\n",
" font-size: 16pt;\n",
" color: #4057A1;\n",
" font-style: italic;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
" }\n",
"\n",
" .warning{\n",
" color: rgb( 240, 20, 20 )\n",
" }\n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"]\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
]
}
],
"metadata": {
@ -535,7 +447,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.9.1"
}
},
"nbformat": 4,

170
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281
10_categorical_features.ipynb
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@ -4,11 +4,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Encoding categorical features ([video #10](https://www.youtube.com/watch?v=irHhDMbw3xo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10))\n",
"# Building a Machine Learning workflow ([video #10](https://www.youtube.com/watch?v=irHhDMbw3xo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10))\n",
"\n",
"Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
"\n",
"**Note:** This notebook uses scikit-learn 0.20. Some of the code below will not work if you are using an earlier version of scikit-learn."
"**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 3.7 and scikit-learn 0.20.2."
]
},
{
@ -297,33 +297,71 @@
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(889, 1)\n",
"(889,)\n"
]
"data": {
"text/plain": [
"(889, 1)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(X.shape)\n",
"print(y.shape)"
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"(889,)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"logreg = LogisticRegression(solver='lbfgs')"
"y.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"logreg = LogisticRegression()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import cross_val_score"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
@ -331,19 +369,18 @@
"0.6783406335301212"
]
},
"execution_count": 13,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import cross_val_score\n",
"cross_val_score(logreg, X, y, cv=5, scoring='accuracy').mean()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 17,
"metadata": {},
"outputs": [
{
@ -354,7 +391,7 @@
"Name: Survived, dtype: float64"
]
},
"execution_count": 14,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@ -372,7 +409,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 18,
"metadata": {},
"outputs": [
{
@ -451,7 +488,7 @@
"4 0 3 male S"
]
},
"execution_count": 15,
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@ -462,7 +499,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@ -473,7 +510,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 20,
"metadata": {},
"outputs": [
{
@ -488,7 +525,7 @@
" [0., 1.]])"
]
},
"execution_count": 17,
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@ -499,7 +536,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 21,
"metadata": {},
"outputs": [
{
@ -508,7 +545,7 @@
"[array(['female', 'male'], dtype=object)]"
]
},
"execution_count": 18,
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@ -519,7 +556,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 22,
"metadata": {},
"outputs": [
{
@ -534,7 +571,7 @@
" [0., 1., 0.]])"
]
},
"execution_count": 19,
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@ -545,7 +582,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 23,
"metadata": {},
"outputs": [
{
@ -554,7 +591,7 @@
"[array(['C', 'Q', 'S'], dtype=object)]"
]
},
"execution_count": 20,
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@ -563,32 +600,6 @@
"ohe.categories_"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 1., 0., 0., 1.],\n",
" [1., 0., 1., 0., 0.],\n",
" [1., 0., 0., 0., 1.],\n",
" ...,\n",
" [1., 0., 0., 0., 1.],\n",
" [0., 1., 1., 0., 0.],\n",
" [0., 1., 0., 1., 0.]])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ohe.fit_transform(df[['Sex', 'Embarked']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -598,7 +609,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@ -607,7 +618,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 25,
"metadata": {},
"outputs": [
{
@ -680,7 +691,7 @@
"4 3 male S"
]
},
"execution_count": 23,
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@ -691,7 +702,7 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
@ -701,7 +712,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
@ -712,7 +723,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 28,
"metadata": {},
"outputs": [
{
@ -727,7 +738,7 @@
" [0., 1., 0., 1., 0., 3.]])"
]
},
"execution_count": 26,
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@ -738,7 +749,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
@ -748,7 +759,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
@ -757,7 +768,7 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 31,
"metadata": {},
"outputs": [
{
@ -766,7 +777,7 @@
"0.7727924839713071"
]
},
"execution_count": 29,
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
@ -786,7 +797,16 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# added empty cell so that the cell numbering matches the video"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
@ -861,7 +881,7 @@
"790 3 male Q"
]
},
"execution_count": 30,
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@ -873,7 +893,7 @@
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 34,
"metadata": {
"scrolled": true
},
@ -881,15 +901,15 @@
{
"data": {
"text/plain": [
"Pipeline(memory=None,\n",
" steps=[('columntransformer', ColumnTransformer(n_jobs=None, remainder='passthrough', sparse_threshold=0.3,\n",
" transformer_weights=None,\n",
" transformers=[('onehotencoder', OneHotEncoder(categorical_features=None, categories=None,\n",
" dtype=<class 'numpy.float64'>, handle_unknown='error...enalty='l2', random_state=None, solver='lbfgs',\n",
" tol=0.0001, verbose=0, warm_start=False))])"
"Pipeline(steps=[('columntransformer',\n",
" ColumnTransformer(remainder='passthrough',\n",
" transformers=[('onehotencoder',\n",
" OneHotEncoder(),\n",
" ['Sex', 'Embarked'])])),\n",
" ('logisticregression', LogisticRegression())])"
]
},
"execution_count": 31,
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@ -900,7 +920,7 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 35,
"metadata": {},
"outputs": [
{
@ -909,7 +929,7 @@
"array([1, 0, 1, 1, 0])"
]
},
"execution_count": 32,
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@ -927,7 +947,7 @@
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
@ -941,7 +961,7 @@
},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
@ -953,7 +973,7 @@
},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
@ -965,67 +985,13 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"pipe = make_pipeline(column_trans, logreg)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7727924839713071"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"X_new = X.sample(5, random_state=99)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(memory=None,\n",
" steps=[('columntransformer', ColumnTransformer(n_jobs=None, remainder='passthrough', sparse_threshold=0.3,\n",
" transformer_weights=None,\n",
" transformers=[('onehotencoder', OneHotEncoder(categorical_features=None, categories=None,\n",
" dtype=<class 'numpy.float64'>, handle_unknown='error...enalty='l2', random_state=None, solver='lbfgs',\n",
" tol=0.0001, verbose=0, warm_start=False))])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipe.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 40,
@ -1034,7 +1000,7 @@
{
"data": {
"text/plain": [
"array([1, 0, 1, 1, 0])"
"0.7727924839713071"
]
},
"execution_count": 40,
@ -1043,8 +1009,49 @@
}
],
"source": [
"cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"X_new = X.sample(5, random_state=99)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 1, 1, 0])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipe.fit(X, y)\n",
"pipe.predict(X_new)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comments or Questions?\n",
"\n",
"- Email: <kevin@dataschool.io>\n",
"- Website: https://www.dataschool.io\n",
"- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
]
}
],
"metadata": {
@ -1063,7 +1070,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
"version": "3.9.1"
}
},
"nbformat": 4,

34
README.md
View File

@ -1,41 +1,41 @@
# Introduction to machine learning with scikit-learn
# Introduction to Machine Learning with scikit-learn
This video series will teach you how to solve machine learning problems using Python's popular scikit-learn library. There are **10 video tutorials** totaling 4.5 hours, each with a corresponding **Jupyter notebook**. The notebook contains everything you see in the video: code, output, images, and comments.
This video series will teach you how to solve Machine Learning problems using Python's popular scikit-learn library. There are **10 video tutorials** totaling 4.5 hours, each with a corresponding **Jupyter notebook**. The notebook contains everything you see in the video: code, output, images, and comments.
**Note:** The notebooks in this repository have been updated to use Python 3.6 and scikit-learn 0.19.1. The original notebooks (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive). You can read about how I updated the code in this [blog post](https://www.dataschool.io/how-to-update-your-scikit-learn-code-for-2018/).
**Note:** The notebooks in this repository have been updated to use Python 3.9.1 and scikit-learn 0.23.2. The original notebooks (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive). You can read about how I updated the code in this [blog post](https://www.dataschool.io/how-to-update-your-scikit-learn-code-for-2018/).
You can [watch the entire series](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A) on YouTube, and [view all of the notebooks](http://nbviewer.jupyter.org/github/justmarkham/scikit-learn-videos/tree/master/) using nbviewer.
[![Watch the first tutorial video](images/youtube.png)](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1 "Watch the first tutorial video")
Once you complete this video series, I recommend enrolling in my online course, [Machine Learning with Text in Python](http://www.dataschool.io/learn/), to gain a deeper understanding of scikit-learn and Natural Language Processing.
Once you complete this video series, I recommend enrolling in my online course, [Machine Learning with Text in Python](https://www.dataschool.io/learn/), to gain a deeper understanding of scikit-learn and Natural Language Processing.
## Table of Contents
1. What is machine learning, and how does it work? ([video](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1), [notebook](01_machine_learning_intro.ipynb))
- What is machine learning?
- What are the two main categories of machine learning?
- What are some examples of machine learning?
- How does machine learning "work"?
1. What is Machine Learning, and how does it work? ([video](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1), [notebook](01_machine_learning_intro.ipynb))
- What is Machine Learning?
- What are the two main categories of Machine Learning?
- What are some examples of Machine Learning?
- How does Machine Learning "work"?
2. Setting up Python for machine learning: scikit-learn and Jupyter Notebook ([video](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2), [notebook](02_machine_learning_setup.ipynb))
2. Setting up Python for Machine Learning: scikit-learn and Jupyter Notebook ([video](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2), [notebook](02_machine_learning_setup.ipynb))
- What are the benefits and drawbacks of scikit-learn?
- How do I install scikit-learn?
- How do I use the Jupyter Notebook?
- What are some good resources for learning Python?
3. Getting started in scikit-learn with the famous iris dataset ([video](https://www.youtube.com/watch?v=hd1W4CyPX58&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=3), [notebook](03_getting_started_with_iris.ipynb))
- What is the famous iris dataset, and how does it relate to machine learning?
- What is the famous iris dataset, and how does it relate to Machine Learning?
- How do we load the iris dataset into scikit-learn?
- How do we describe a dataset using machine learning terminology?
- How do we describe a dataset using Machine Learning terminology?
- What are scikit-learn's four key requirements for working with data?
4. Training a machine learning model with scikit-learn ([video](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4), [notebook](04_model_training.ipynb))
4. Training a Machine Learning model with scikit-learn ([video](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4), [notebook](04_model_training.ipynb))
- What is the K-nearest neighbors classification model?
- What are the four steps for model training and prediction in scikit-learn?
- How can I apply this pattern to other machine learning models?
- How can I apply this pattern to other Machine Learning models?
5. Comparing machine learning models in scikit-learn ([video](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5), [notebook](05_model_evaluation.ipynb))
5. Comparing Machine Learning models in scikit-learn ([video](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5), [notebook](05_model_evaluation.ipynb))
- How do I choose which model to use for my supervised learning task?
- How do I choose the best tuning parameters for that model?
- How do I estimate the likely performance of my model on out-of-sample data?
@ -70,7 +70,7 @@ Once you complete this video series, I recommend enrolling in my online course,
- What is the purpose of an ROC curve?
- How does Area Under the Curve (AUC) differ from classification accuracy?
10. Encoding categorical features ([video](https://www.youtube.com/watch?v=irHhDMbw3xo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10), [notebook](10_categorical_features.ipynb))
10. Building a Machine Learning workflow ([video](https://www.youtube.com/watch?v=irHhDMbw3xo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10), [notebook](10_categorical_features.ipynb))
- Why should you use a Pipeline?
- How do you encode categorical features with OneHotEncoder?
- How do you apply OneHotEncoder to selected columns with ColumnTransformer?
@ -80,7 +80,7 @@ Once you complete this video series, I recommend enrolling in my online course,
## Bonus Video
At the PyCon 2016 conference, I taught a **3-hour tutorial** that builds upon this video series and focuses on **text-based data**. You can watch the [tutorial video](https://www.youtube.com/watch?v=ZiKMIuYidY0&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10) on YouTube.
At the PyCon 2016 conference, I taught a **3-hour tutorial** that builds upon this video series and focuses on **text-based data**. You can watch the [tutorial video](https://www.youtube.com/watch?v=ZiKMIuYidY0&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=11) on YouTube.
Here are the topics I covered: