Learn scikit-learn, a powerful Python machine learning library, with this comprehensive learning path. Designed for beginners, this roadmap provides a structured approach to mastering ML algorithms, model selection, and evaluation. The scikit-learn Courses include hands-on, non-video tutorials and practical exercises in a data science playground, enabling the development of real-world experience in implementing machine learning solutions.
TABLE OF CONTENTS
CORE MODELS AND ALGORITHMS
Core Models and Algorithms covers fundamental machine learning models and algorithms, including linear models, decision trees, Naive Bayes, nearest neighbors, clustering, ensemble methods, support vector machines, neural networks, Gaussian processes, and more.
Linear models are foundational in machine learning, and scikit-learn provides various linear algorithms for regression and classification tasks, including Linear Regression and Logistic Regression.
Decision trees are a popular method for both classification and regression tasks. Scikit-learn offers DecisionTreeClassifier and DecisionTreeRegressor for creating decision tree models.
Naive Bayes is a simple but effective probabilistic classification algorithm. Scikit-learn provides implementations of Naive Bayes classifiers.
Nearest Neighbors methods are used for classification and regression tasks based on the similarity of data points. Scikit-learn includes the K-nearest neighbors algorithm.
Clustering algorithms in scikit-learn are used to group similar data points together. Methods like K-Means and DBSCAN are available for clustering.
Ensemble methods combine multiple machine learning models to improve predictive performance. Scikit-learn offers ensemble techniques like Random Forest and Gradient Boosting.
Support Vector Machines (SVM) are powerful for both classification and regression tasks. Scikit-learn provides SVM implementations with various kernels.
Scikit-learn includes basic neural network models for regression and classification tasks. While it may not be as comprehensive as specialized deep learning libraries, it offers an introduction to neural networks.
Gaussian Processes are probabilistic models used for regression tasks. Scikit-learn offers Gaussian Process Regression for modeling complex relationships in data.
Discriminant Analysis methods are used for dimensionality reduction and classification. Scikit-learn includes Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA).
Gaussian Mixture Models (GMM) are probabilistic models used for clustering and density estimation. Scikit-learn provides GMM implementations.
DATA PREPROCESSING AND FEATURE ENGINEERING
Data Preprocessing and Feature Engineering revolves around preparing and transforming data for machine learning, including techniques for feature extraction, selection, normalization, and imputation.
Preprocessing and normalization techniques in scikit-learn help prepare and clean data by scaling, standardizing, and handling missing values, making it suitable for machine learning models.
Feature extraction involves transforming raw data into a set of meaningful features that can be used as inputs for machine learning algorithms. Scikit-learn provides various methods for feature extraction.
Feature selection is the process of choosing the most relevant features from a dataset to improve model performance and reduce dimensionality. Scikit-learn offers methods for feature selection based on various criteria.
Pipelines in scikit-learn allow for the seamless chaining of multiple data preprocessing and modeling steps into a single workflow. This ensures a systematic and efficient approach to building machine learning pipelines, including data transformation, feature selection, and model training.
Dummy estimators in scikit-learn are simple models that provide baseline performance metrics for comparison. They are useful for assessing the predictive power of more advanced models and serve as a reference point for model evaluation.
Imputation techniques in scikit-learn provide ways to handle missing data by filling in the missing values with estimated or calculated values, allowing for more complete datasets.
Kernel approximation methods enable the use of kernel-based algorithms with large datasets by approximating the kernel matrix efficiently.
MODEL SELECTION AND EVALUATION
Model Selection and Evaluation focuses on techniques for selecting the best machine learning models and evaluating their performance, including metrics, cross decomposition, composite estimators, probability calibration, and model inspection.
Model selection involves choosing the most appropriate machine learning model for a specific task, considering factors like performance, interpretability, and computational efficiency.
Metrics are used to assess the performance of machine learning models, including measures like accuracy, precision, recall, F1-score, and more. Scikit-learn provides a comprehensive set of metrics.
Cross decomposition techniques in scikit-learn enable the decomposition of multi-table data, such as spectral data or multivariate measurements. These methods are valuable for extracting meaningful information from complex datasets with multiple sources of information.
Composite estimators in scikit-learn are models composed of multiple base estimators. They are useful for combining various algorithms to improve predictive performance.
Probability calibration techniques adjust the predicted probabilities of a model to improve their accuracy. Scikit-learn provides methods for probability calibration.
Model inspection techniques help analyze and understand the inner workings of machine learning models, including feature importance, coefficients, and decision boundaries.
Kernel Ridge Regression is a regression technique that combines ridge regression with kernel methods. It can capture complex relationships in data.
Isotonic Regression is a regression method that models non-decreasing relationships between variables. It is useful when dealing with ordered data.
ADVANCED DATA ANALYSIS AND DIMENSIONALITY REDUCTION
Advanced Data Analysis and Dimensionality Reduction covers advanced techniques for data analysis, dimensionality reduction, and specialized tasks like multiclass classification, multioutput regression, and semi-supervised learning.
Matrix decomposition methods are used for dimensionality reduction and feature extraction. Scikit-learn provides tools for matrix factorization and decomposition.
Covariance estimators are used for calculating covariance matrices, which are crucial in data analysis. Scikit-learn includes various covariance estimators.
Manifold learning techniques aim to discover the underlying structure in high-dimensional data. Scikit-learn offers methods for manifold learning, such as Isomap and t-SNE.
Multiclass classification is the task of classifying data into more than two classes. Scikit-learn provides methods and techniques for multiclass classification.
Multioutput regression and classification deal with tasks where multiple output variables are predicted simultaneously. Scikit-learn supports multioutput models.
Semi-supervised learning methods leverage both labeled and unlabeled data for training machine learning models. Scikit-learn includes semi-supervised learning algorithms.
UTILITIES AND DATASETS
Utilities and Datasets focuses on utility functions and datasets provided by scikit-learn for various tasks. Utilities include functions for general-purpose tasks, while datasets contain built-in datasets for practicing machine learning.
Base classes and utility functions are essential components of scikit-learn that provide foundational support for creating machine learning models. They include core functionalities for various algorithms.
Utilities in scikit-learn encompass a wide range of helper functions and tools that simplify common tasks in machine learning, such as data preprocessing and evaluation.
The Datasets section of scikit-learn offers a collection of built-in datasets that users can use to practice and experiment with machine learning algorithms. These datasets cover a variety of domains and are easily accessible for learning purposes.
Random Projection techniques in scikit-learn are used for dimensionality reduction by projecting data into a lower-dimensional space while preserving certain properties. They are useful for handling high-dimensional data efficiently.
Exceptions and warnings in scikit-learn pertain to handling errors and issues that may arise during the use of the library. Understanding these can help troubleshoot problems.
Experimental features in scikit-learn may not be fully stable or documented but offer advanced capabilities. Users can experiment with these features but should be cautious.