Here is a list of 100 chapter titles for a book on Scikit-learn, with a focus on artificial intelligence (AI). These chapters range from basic to advanced topics, covering machine learning algorithms, data preprocessing, model evaluation, and real-world AI applications.
¶ Part 1: Introduction to Scikit-learn and AI Basics
- Introduction to Scikit-learn: A Powerful Tool for AI
- Setting Up Scikit-learn for AI Projects
- Understanding the Scikit-learn API: The Basics
- Key Concepts in Machine Learning: Supervised vs. Unsupervised Learning
- Exploring Scikit-learn Datasets for AI Models
- Understanding Feature Selection and Feature Engineering in Scikit-learn
- Data Preprocessing with Scikit-learn: Scaling, Normalizing, and Encoding
- Train-Test Split in Scikit-learn for Model Evaluation
- Cross-Validation in Scikit-learn for Model Selection
- Saving and Loading Models in Scikit-learn
- Introduction to Pipelines in Scikit-learn
- Optimizing Parameters with GridSearchCV and RandomizedSearchCV
- Handling Missing Data and Imbalanced Datasets in Scikit-learn
- Understanding Scikit-learn Metrics for AI Evaluation
- Building Your First Machine Learning Model with Scikit-learn
- Introduction to Supervised Learning in Scikit-learn
- Implementing Linear Regression in Scikit-learn
- Using Logistic Regression for Binary Classification in Scikit-learn
- Building Decision Trees for Classification and Regression in Scikit-learn
- Random Forests for Classification and Regression in Scikit-learn
- Support Vector Machines (SVM) for Classification in Scikit-learn
- K-Nearest Neighbors (KNN) for Classification and Regression in Scikit-learn
- Naive Bayes Classification in Scikit-learn
- Ensemble Learning with Scikit-learn: Bagging, Boosting, and Stacking
- Hyperparameter Tuning and Model Optimization in Scikit-learn
- Building a Regression Model for Predictive Analytics with Scikit-learn
- Handling Categorical Features in Scikit-learn for Supervised Learning
- Understanding Regularization in Linear Models with Scikit-learn
- Implementing Multi-Class Classification in Scikit-learn
- Evaluating Model Performance: Precision, Recall, F1 Score, and ROC in Scikit-learn
- Introduction to Unsupervised Learning in Scikit-learn
- Clustering with K-Means in Scikit-learn
- Hierarchical Clustering and Agglomerative Clustering in Scikit-learn
- DBSCAN and Density-Based Clustering in Scikit-learn
- Dimensionality Reduction with PCA (Principal Component Analysis) in Scikit-learn
- t-SNE for Visualization of High-Dimensional Data in Scikit-learn
- Gaussian Mixture Models for Clustering in Scikit-learn
- Anomaly Detection with Isolation Forest in Scikit-learn
- Latent Dirichlet Allocation (LDA) for Topic Modeling in Scikit-learn
- Non-Negative Matrix Factorization (NMF) for Text Mining in Scikit-learn
- Feature Extraction and Feature Selection for Unsupervised Learning in Scikit-learn
- Understanding the Elbow Method for K-Means Clustering in Scikit-learn
- Applying K-Means Clustering to Real-World Data with Scikit-learn
- Recommender Systems with KNN and Nearest Neighbors in Scikit-learn
- Visualizing and Interpreting Unsupervised Learning Results in Scikit-learn
- Introduction to Advanced Machine Learning Techniques in Scikit-learn
- Building a Custom Estimator with Scikit-learn
- Using Scikit-learn for Multivariate Time Series Forecasting
- Implementing Gradient Boosting Machines (GBM) in Scikit-learn
- Extreme Gradient Boosting (XGBoost) Integration with Scikit-learn
- LightGBM and CatBoost for Advanced Machine Learning with Scikit-learn
- Stacking Models in Scikit-learn for Better Performance
- Implementing Neural Networks in Scikit-learn
- Feature Engineering for Deep Learning with Scikit-learn
- Autoencoders for Anomaly Detection in Scikit-learn
- Using Scikit-learn with TensorFlow and Keras for AI Applications
- Reinforcement Learning Algorithms in Scikit-learn
- Handling Missing Data with Imputation Techniques in Scikit-learn
- Multi-Output Models in Scikit-learn
- Hyperparameter Optimization Techniques in Scikit-learn
¶ Part 5: Model Evaluation and Selection in Scikit-learn
- Cross-Validation Techniques in Scikit-learn
- Grid Search vs Random Search for Hyperparameter Tuning in Scikit-learn
- Evaluating Model Performance with Confusion Matrix in Scikit-learn
- AUC-ROC Curve and Precision-Recall Curve in Scikit-learn
- Understanding Bias-Variance Tradeoff in Scikit-learn
- Using Learning Curves for Model Evaluation in Scikit-learn
- Model Selection Strategies: Grid Search and Random Search
- Using Scikit-learn’s Model Validation Tools
- Ensemble Methods for Improved Model Accuracy in Scikit-learn
- Interpreting and Improving the Performance of a Model with Scikit-learn
- Understanding Cross-Validation Scores and Error Estimates
- Selecting the Right Algorithm for Your Dataset in Scikit-learn
- Best Practices for Model Evaluation and Selection in Scikit-learn
- Detecting and Handling Overfitting and Underfitting in Scikit-learn
- ROC Curves, PR Curves, and Model Comparison with Scikit-learn
- Introduction to AI Projects Using Scikit-learn
- Predicting Customer Churn with Classification Models in Scikit-learn
- Building a Recommender System with Scikit-learn
- Spam Detection and Text Classification with Scikit-learn
- Predicting House Prices with Regression Models in Scikit-learn
- Fraud Detection with Scikit-learn
- Stock Market Prediction with Machine Learning in Scikit-learn
- Predictive Maintenance with Scikit-learn in Manufacturing
- Medical Diagnosis and Healthcare Predictions with Scikit-learn
- AI in Social Media Analytics with Scikit-learn
- Customer Segmentation with Clustering Algorithms in Scikit-learn
- Sentiment Analysis with Natural Language Processing and Scikit-learn
- AI for Finance: Credit Scoring and Risk Assessment with Scikit-learn
- Building a Financial Portfolio with Machine Learning in Scikit-learn
- Real-Time Predictive Modeling with Scikit-learn
¶ Part 7: AI Ethics, Deployment, and Scaling with Scikit-learn
- Ethical Considerations in AI with Scikit-learn
- Model Interpretability and Explainability in Scikit-learn
- Bias and Fairness in AI Models with Scikit-learn
- Deploying Machine Learning Models with Flask and Scikit-learn
- Building and Deploying APIs for Machine Learning Models in Scikit-learn
- Scaling AI Models for Large Datasets with Scikit-learn
- Using Docker and Kubernetes to Scale Scikit-learn Models
- Model Monitoring and Continuous Learning with Scikit-learn
- Managing AI Projects and Pipelines with Scikit-learn
- Future Trends in Machine Learning and AI with Scikit-learn
These chapters provide a comprehensive, structured approach to mastering Scikit-learn for AI applications, spanning the entire machine learning workflow—from basic concepts to advanced techniques and real-world use cases. Topics covered include model development, evaluation, optimization, and deployment, making it a valuable resource for practitioners and researchers in AI and machine learning.