Welcome to this exciting collection of programming tutorials from LabEx! 🎉 Whether you're a seasoned data scientist or just starting your journey, these labs will guide you through the fascinating realms of supervised and unsupervised learning.
1. Supervised Learning with Scikit-Learn 🧠
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In this lab, we'll dive into the world of supervised learning, where we aim to uncover the relationship between observed data (X) and a target variable (y) that we want to predict. Prepare to explore the power of Scikit-Learn, a renowned machine learning library, as we tackle this exciting challenge.
2. Multiclass Sparse Logistic Regression 🤖
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Delving into the realm of multiclass classification, this lab will introduce you to the intricacies of Multiclass Sparse Logistic Regression. We'll put our skills to the test on the 20newsgroups dataset, comparing the performance of Multinomial logistic regression and one-versus-rest L1 logistic regression.
3. Class Likelihood Ratios to Measure Classification Performance 📊
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Evaluating the performance of binary classifiers can be a tricky task, but this lab has the solution! 💡 We'll explore the use of positive and negative likelihood ratios (LR+, LR-) to assess the predictive power of our models, independent of the class proportions in the test set.
4. Understanding Model Complexity 🧠
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In this lab, we'll dive deep into the relationship between model complexity, prediction accuracy, and computational performance. Using the Diabetes Dataset for regression and the 20newsgroups Dataset for classification, we'll investigate the influence of complexity on various estimators.
5. Plot Agglomerative Clustering 🔍
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Clustering is a fundamental unsupervised learning technique, and in this lab, we'll explore the power of Agglomerative Clustering. 🌳 We'll learn how to leverage connectivity graphs to capture the local structure in our data, uncovering hidden patterns and insights.
6. Evaluation of Common Clustering Methods 🧪
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Navigating the vast landscape of clustering algorithms can be daunting, but this lab has got you covered! 🔍 We'll dive into nearly ten different clustering methods, exploring their effects and time consumption on datasets with varying shapes and structures.
7. Feature Discretization for Classification 📊
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In machine learning, feature discretization can be a powerful tool for simplifying complex datasets. In this lab, we'll demonstrate the process of reducing continuous variables into bins or intervals, and see how this can benefit our classification models.
8. Text Feature Extraction and Evaluation 📚
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Unleash the power of text data with this lab! 📖 We'll use the 20newsgroups dataset to showcase the Scikit-Learn library's capabilities in text feature extraction and model evaluation. Prepare to build pipelines and optimize your models for top-notch performance.
9. Face Completion With Multi-Output Estimators 🤖
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In this captivating lab, we'll explore the fascinating world of image completion. 🖼️ Using multi-output estimators, we'll tackle the challenge of predicting the lower half of a face given its upper half. Get ready to witness the magic of algorithms like extremely randomized trees, k-nearest neighbors, linear regression, and ridge regression as they work their magic.
Dive in, explore, and let these labs be your guide to the exciting realms of supervised and unsupervised learning! 🚀 Happy coding!
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