Author Description

Learn all about Scikit-learn Scikit-learn (formerly known as scikit) is a powerful open-source machine learning library in Python. It is built on top of other scientific computing libraries such as NumPy, SciPy, and Matplotlib. Scikit-learn provides a wide range of algorithms and tools for data analysis and predictive modeling. The book covers the following: 1 Introduction Introduce Scikit-learn and its purpose Brief history of Scikit-learn Discuss how Scikit-learn compares to other machine learning libraries 2 Getting Started with Scikit-learn Installation and setup of Scikit-learn Basic data manipulation with NumPy and Pandas Introduction to the Scikit-learn API Basic model building and training with Scikit-learn 3 Supervised Learning with Scikit-learn Regression models (e.g., linear regression, polynomial regression) Classification models (e.g., logistic regression, decision trees, random forests, support vector machines) Model evaluation and selection Dealing with imbalanced data Multi-class classification Using ensemble methods 4 Unsupervised Learning with Scikit-learn Clustering algorithms (e.g., K-means, hierarchical clustering) Dimensionality reduction techniques (e.g., principal component analysis, t-SNE) Model evaluation and selection for unsupervised learning Feature extraction and engineering techniques 5 Deep Learning with Scikit-learn Introduction to deep learning with Scikit-learn Building neural networks with Scikit-learn Hyperparameter tuning with Scikit-learn Transfer learning and fine-tuning with Scikit-learn 6 Advanced Topics with Scikit-learn Time series analysis with Scikit-learn Text analysis and natural language processing with Scikit-learn Handling missing data with Scikit-learn Interpretability and explainability of models with Scikit-learn Tips and tricks for using Scikit-learn effec