Mastering ML:Hyperparameter Optimization & Feature Selection

Explore the fundamentals of hyperparameter optimization to differentiate parameters from hyperparameters, fine tune machine learning models, map the hyperparameter space and distributions, and maximize the objective function.

Explore hyperparameter optimization techniques: grid search, random search, and manual tuning, with Python exercises, cross-validation, and practical guidance on when to use each method.

Explore principal Python libraries for data manipulation, visualization, and machine learning, including pandas, numpy, matplotlib, scikit-learn, scikit-optimize, TensorFlow, Keras, and Optuna, to optimize machine learning and deep learning models.

Access practical exercises for mastering ML through a shared Google Drive folder, with direct links to online notebooks and downloadable .py files for Google Colab or local runs.

Master bayesian optimization theory for hyperparameter tuning and feature selection by using surrogate models like Gaussian processes and acquisition functions to balance exploration and exploitation.

Explore hands-on Bayesian optimization for SVM and XGBoost on breast cancer data, tuning hyperparameters with cross-validated scoring and visualizing optimization via partial dependence plots.

Compute the Pearson correlation between each feature and the target. Rank features by the coefficient, select top k, apply significance thresholds, then train and evaluate the model on selected features.

Kendal rank for feature selection by ranking features relative to the target using Kendal tau, selecting the top k features, assessing significance, and evaluating model performance.

Apply chi-square feature selection by formulating null and alternative hypotheses, building contingency tables, computing expected frequencies and chi-square statistics, then using 0.05 significance to identify top features in cancer data.

Explore recursive feature elimination, rfecv, and meta selector for feature selection using model-based importances, cross validation, and performance-based stopping to build robust machine learning models.

Learn how random forest, an embedded method, performs feature selection by training a model, measuring feature importances via impurity decrease, and retraining with top features to evaluate performance.

Explore wrapper feature selection with recursive feature elimination using a random forest classifier on the breast cancer dataset. Use cross-validation to select top features and evaluate model accuracy.

Practice meta selector for feature selection using Python in Google Colab, applying it to breast cancer data with a random forest classifier selecting five features and achieving 97% accuracy.

Explore embedded feature selection with a random forest, extract feature importances, use numpy argsort to select the top five features, train a classifier on them, and evaluate accuracy.

Set up feature selection workflow in Visual Studio Code, create Python environment, install NumPy and Seaborn, run Pearson correlation, PCA, and recursive feature elimination on breast cancer and iris data.

Explore core binary and multiclass classification concepts, including true positives, true negatives, false positives, and false negatives, and essential metrics such as accuracy, precision, recall, F1, ROC, kappa, and cross-entropy.

Evaluate regression models using metrics such as mean absolute error, mean square error, root mean square error, mean absolute percentage error, and R squared to measure goodness of fit.

Learn clustering evaluation metrics, including silhouette score and adjusted Rand index, to assess cluster quality and agreement beyond chance; apply to stock prediction and neural networks with Keras and TensorFlow.

Bayesian optimization tunes gradient boosting and extreme gradient boosting for stock price prediction using yfinance data, comparing optimized and default models with MAE, MSE, RMSE, and R-squared.

Explore bayesian optimization and grid search of a cnn for image classification using keras tuner to tune hyperparameters on the flowers dataset.

Master hyperparameter optimization with Optuna, tuning SVM and XGBoost on breast cancer and flowers datasets, and evaluate accuracy with objective-driven experiments and visualizations.

Explore non supervised algorithm optimization using silhouette score to determine k-means cluster counts. Evaluate with make_blobs data and adjusted rand index, and visualize results.

Explore foundational and practical ML books covering linear models, SVMs, neural networks, feature engineering, and hyperparameter optimization from theory to practice.

Apply hyperparameter optimization and feature selection to build more precise machine learning models and maximize performance, while looking ahead to future topics like transformer architectures.