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.
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 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.
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.
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.