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<li class="toctree-l1 has-children"><a class="reference internal" href="supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html">1.4. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html">1.5. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html">1.6. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="model_selection.html">3. Model selection and evaluation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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<li class="toctree-l1"><a class="reference internal" href="visualizations.html">5. Visualizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html">6.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
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<li class="toctree-l1"><a class="reference internal" href="model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/array_api.html">11.1. Array API support (experimental)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">13. External Resources, Videos and Talks</a></li>
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<h1><span class="section-number">13. </span>External Resources, Videos and Talks<a class="headerlink" href="#external-resources-videos-and-talks" title="Link to this heading">#</a></h1>
<section id="new-to-scientific-python">
<h2><span class="section-number">13.1. </span>New to Scientific Python?<a class="headerlink" href="#new-to-scientific-python" title="Link to this heading">#</a></h2>
<p>For those that are still new to the scientific Python ecosystem, we highly
recommend the <a class="reference external" href="https://scipy-lectures.org">Python Scientific Lecture Notes</a>. This will help you find your footing a
bit and will definitely improve your scikit-learn experience. A basic
understanding of NumPy arrays is recommended to make the most of scikit-learn.</p>
</section>
<section id="external-tutorials">
<h2><span class="section-number">13.2. </span>External Tutorials<a class="headerlink" href="#external-tutorials" title="Link to this heading">#</a></h2>
<p>There are several online tutorials available which are geared toward
specific subject areas:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://nilearn.github.io/">Machine Learning for NeuroImaging in Python</a></p></li>
<li><p><a class="reference external" href="https://github.com/astroML/sklearn_tutorial">Machine Learning for Astronomical Data Analysis</a></p></li>
</ul>
</section>
<section id="videos">
<span id="id1"></span><h2><span class="section-number">13.3. </span>Videos<a class="headerlink" href="#videos" title="Link to this heading">#</a></h2>
<ul>
<li><p>An introduction to scikit-learn <a class="reference external" href="https://conference.scipy.org/scipy2013/tutorial_detail.php?id=107">Part
I</a> and
<a class="reference external" href="https://conference.scipy.org/scipy2013/tutorial_detail.php?id=111">Part II</a> at Scipy 2013
by <a class="reference external" href="https://gael-varoquaux.info">Gael Varoquaux</a>, <a class="reference external" href="http://www.vanderplas.com">Jake Vanderplas</a> and <a class="reference external" href="https://twitter.com/ogrisel">Olivier Grisel</a>. Notebooks on
<a class="reference external" href="https://github.com/jakevdp/sklearn_scipy2013">github</a>.</p></li>
<li><p><a class="reference external" href="http://videolectures.net/icml2010_varaquaux_scik/">Introduction to scikit-learn</a> by <a class="reference external" href="https://gael-varoquaux.info">Gael Varoquaux</a> at
ICML 2010</p>
<p>A three minute video from a very early stage of scikit-learn, explaining the
basic idea and approach we are following.</p>
</li>
<li><p><a class="reference external" href="https://archive.org/search.php?query=scikit-learn">Introduction to statistical learning with scikit-learn</a>
by <a class="reference external" href="https://gael-varoquaux.info">Gael Varoquaux</a> at SciPy 2011</p>
<p>An extensive tutorial, consisting of four sessions of one hour.
The tutorial covers the basics of machine learning,
many algorithms and how to apply them using scikit-learn.</p>
</li>
<li><p><a class="reference external" href="https://pyvideo.org/video/417/pycon-2011--statistical-machine-learning-for-text">Statistical Learning for Text Classification with scikit-learn and NLTK</a>
(and <a class="reference external" href="https://www.slideshare.net/ogrisel/statistical-machine-learning-for-text-classification-with-scikitlearn-and-nltk">slides</a>)
by <a class="reference external" href="https://twitter.com/ogrisel">Olivier Grisel</a> at PyCon 2011</p>
<p>Thirty minute introduction to text classification. Explains how to
use NLTK and scikit-learn to solve real-world text classification
tasks and compares against cloud-based solutions.</p>
</li>
<li><p><a class="reference external" href="https://www.youtube.com/watch?v=Zd5dfooZWG4">Introduction to Interactive Predictive Analytics in Python with scikit-learn</a>
by <a class="reference external" href="https://twitter.com/ogrisel">Olivier Grisel</a> at PyCon 2012</p>
<p>3-hours long introduction to prediction tasks using scikit-learn.</p>
</li>
<li><p><a class="reference external" href="https://www.youtube.com/watch?v=cHZONQ2-x7I">scikit-learn - Machine Learning in Python</a>
by <a class="reference external" href="http://www.vanderplas.com">Jake Vanderplas</a> at the 2012 PyData workshop at Google</p>
<p>Interactive demonstration of some scikit-learn features. 75 minutes.</p>
</li>
<li><p><a class="reference external" href="https://www.youtube.com/watch?v=cHZONQ2-x7I">scikit-learn tutorial</a> by <a class="reference external" href="http://www.vanderplas.com">Jake Vanderplas</a> at PyData NYC 2012</p>
<p>Presentation using the online tutorial, 45 minutes.</p>
</li>
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