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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>
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<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
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<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>
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<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-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-l2"><a class="reference internal" href="modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html">7.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html">7.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html">7.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html">7.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html">8.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html">8.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
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<li class="toctree-l2"><a class="reference internal" href="computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/computational_performance.html">9.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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<li class="toctree-l1"><a class="reference internal" href="model_persistence.html">10. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l1"><a class="reference internal" href="machine_learning_map.html">13. Choosing the right estimator</a></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">14. External Resources, Videos and Talks</a></li>
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<span id="external-resources"></span><h1><span class="section-number">14. </span>External Resources, Videos and Talks<a class="headerlink" href="#external-resources-videos-and-talks" title="Link to this heading">#</a></h1>
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<h2><span class="section-number">14.1. </span>The scikit-learn MOOC<a class="headerlink" href="#the-scikit-learn-mooc" title="Link to this heading">#</a></h2>
<p>If you are new to scikit-learn, or looking to strengthen your understanding,
we highly recommend the <strong>scikit-learn MOOC (Massive Open Online Course)</strong>.</p>
<p>The MOOC, created and maintained by some of the scikit-learn core-contributors,
is <strong>free of charge</strong> and is designed to help learners of all levels master
machine learning using scikit-learn. It covers topics
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<a class="reference external" href="https://inria.github.io/scikit-learn-mooc/">scikit-learn MOOC website</a>.</p>
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<h2><span class="section-number">14.3. </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>
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<h2><span class="section-number">14.4. </span>External Tutorials<a class="headerlink" href="#external-tutorials" title="Link to this heading">#</a></h2>
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specific subject areas:</p>
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<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>
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