Learn all about Scikit-learn
The eBook is in PDF format and costs $14.99
Please click here to buy the eBook.
The eBook 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
Give an overview of the book's contents
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 effectively
The eBook is in PDF format and costs $14.99
Please click here to buy the eBook.