Machine Learning Modelling with RapidMiner

Explore unsupervised machine learning, where algorithms learn from unlabeled data to identify hidden patterns, using transformation with dimensionality reduction and clustering for exploratory data analysis, data preprocessing, and data visualization.

Learn unsupervised dimensionality reduction to reduce features in high dimensional data while preserving information, addressing curse of dimensionality, feature selection, and computational cost, with principal component analysis introduced next.

Learn how principal component analysis rotates data into uncorrelated features to reduce dimensions while preserving maximum variability. Standardize data, compute the covariance matrix, derive eigenvectors and eigenvalues to select components.

Apply principal component analysis in RapidMiner using the iris dataset, including data import, normalization with z transformation, and exploring variance and principal components.

Explore clustering, an unsupervised machine learning technique that groups unlabeled data by similarity to reveal natural patterns and structures, using k-means, hierarchical clustering with dendrograms, density-based, and spectral methods.

Explore k-means clustering, partitioning data into k groups via nearest-centroid assignment and centroid updates, using the elbow method to pick k and distances like Euclidean and cosine similarity.

Use RapidMiner to determine the optimal k for k-means on mall data by normalizing age, income, and spending score and evaluating k from 2 to 10 with centroid distance.

Explore k-medoid clustering, which uses actual data points as medoids instead of a centroid. Compare its robustness to outliers, interpretability, and distance-metric flexibility with k-means.

Implement k medoid clustering on the mall customer dataset in RapidMiner, selecting age, income, and spending score, normalizing data, and evaluate with average within centroid distance for three clusters.

Explore Gaussian mixture models, a probabilistic soft clustering method using multiple Gaussian distributions with mean, variance, and orientation, optimized via the expectation–maximization algorithm.

Identify the key linear regression assumptions governing residuals and linear relationships for reliable predictions. Detect multicollinearity with the variance inflation factor and use residual plots to assess homoscedasticity and autocorrelation.

Assess linear regression performance using root mean square error (rmse), mean absolute error, r-squared, and adjusted r-squared by comparing predictions to actual values, noting sensitivity to outliers and model fit.

Apply linear regression in RapidMiner on a housing dataset. Convert categorical features to numerical with dummy encoding, remove one category to avoid collinearity, and set price as the label.

Explore classification, a supervised learning approach predicting data categories from labeled inputs, and review binary, multi-class, and multi-label types with algorithms like logistic regression, support vector machines, and decision trees.

Explore how logistic regression handles binary classification by predicting class probabilities with a sigmoid function. Use a 0.5 decision boundary to separate classes and interpret higher probabilities as greater confidence.

Explore binary classification performance metrics, including confusion matrix, accuracy, precision, recall, specificity, F1 score, roc and pr curves, and how to choose metrics for imbalanced data and business goals.

Extend binary classification to multi-class problems with one versus rest or one versus one strategies, and compare native multiclass options like softmax regression, decision trees, and neural networks.

Learn how the cost function and gradient descent optimize logistic regression for two-class classification, using h_theta(x)=g(theta^T x) and log loss to minimize misclassifications.

Evaluate model generalization by training and test splits, addressing bias and variance, while using cross-validation, hyperparameter tuning (grid, random, Bayesian), and regularization to prevent overfitting.

Divide data into training and test sets to assess a model's generalization to unseen data. Explore typical splits like 70/30 or 80/20 and prepare for logistic regression in upcoming videos.

Use logistic regression on the Titanic data with a 70/30 train-test split, converting categorical features to numeric and handling missing ages, achieving about 82% training and 80% test accuracy.

Learn how irreducible error from data noise and reducible error from bias and variance shape model performance; avoid underfitting and overfitting to generalize to unseen data.

Master the bias-variance tradeoff to minimize total error and improve generalization. Tune model size, features, regularization, and architecture to balance bias and variance.

Explore grid search in RapidMiner to optimize logistic regression hyperparameters on the Titanic data, including solver and intercept, with a train/test split and performance evaluation.

Apply regularization to regression to prevent overfitting by adding a penalty to the loss function, shrinking coefficients with ridge (L2) and lasso (L1) methods and choosing lambda.

Explore support vector machines, a supervised learning algorithm for classification (binary and multiclass), regression, and outlier detection, and learn how maximizing the margin yields the optimal decision boundary.

Explore how support vector machines generalize to multiclass classification and regression, using epsilon tubes and support vectors to build robust, non-linear boundaries with kernel tricks.

Apply a radial basis function SVM to the Titanic survival task, mirroring logistic regression, after preprocessing, encoding categoricals, and handling missing ages; achieve 86.5% training and 81% test accuracy.

Explore how decision tree algorithms solve classification tasks through top-down recursive splits, using feature thresholds to maximize purity and apply pruning to prevent overfitting.

Traverse the decision tree from root to leaf to predict the class of new data. Test each feature against node conditions and assign the leaf’s majority class as the prediction.

Explore how decision tree regression extends classification by optimizing splits to minimize variance with mean squared error and predict the mean value at leaf nodes.

Build a decision tree in RapidMiner to classify Titanic passenger survival, configuring criteria such as gain ratio and a ten-depth limit, and evaluate training and test performance.

Explore how ensemble methods combine multiple models to improve accuracy and reliability, reducing variance and bias via bagging with random forests, boosting with AdaBoost and XGBoost, and stacking.

Explore bagging to reduce variance by creating multiple bootstrap samples, training numerous models, and averaging predictions, with emphasis on decision trees, out-of-bag estimation, and random forests.

Explore artificial neural networks and deep learning, powerful machine learning methods for regression and classification, and how input data, target variables, metrics, GPUs, and algorithmic improvements enable learning.

Learn how forward propagation moves inputs through a neural network: weighted sums with biases, activations, and passing values from input to hidden to output layers, with initialization and activation choices.

Learn how backward propagation computes the gradient of the loss with respect to weights, guiding gradient descent to adjust weights from output to input and reduce error.

Explore optimization techniques for backpropagation in neural networks, including momentum, stochastic gradient descent, Adagrad, Rmsprop, and Adam, to improve training speed, stability, and learning-rate adaptation.

Explore selecting neural network hyperparameters, including batch size, epochs, and early stopping, while choosing hidden layers, input and output configurations, activation functions, losses, optimizers, and task types.

Explore how neural networks model complex nonlinear patterns in large data set, learn features automatically, scale with GPU-enabled training, and balance benefits with data hunger and interpretability challenges.

Explore how recommendation systems analyze user past behavior to predict missing ratings and deliver personalized item suggestions, using a sparse user-item interaction matrix stored as user, item, rating triples.

Ranked-based popularity recommendations identify most popular items using metrics like average rating or likes, with data cleaning to avoid bias; these systems avoid cold start, are scalable, but lack personalization.

Collaborative filtering personalizes recommendations using user and item vectors and similarity metrics such as cosine similarity and Pearson coefficient, including user-user and item-item approaches.

Explore matrix factorization for collaborative filtering, representing users and items in latent feature spaces with P and Q, and predict ratings via dot products using SVD.

Explore matrix factorization in RapidMiner to build a personalized amazon-based recommendation system, including data cleaning, target variable setup, train-test split, hyperparameter tuning, and evaluating via RMSE and predictions.

Build a user profile from item features to recommend unseen items with similar attributes. Deliver tailored, transparent recommendations while risking over specialization and cold-start for new users.