P.01Perform EDA on the Boston Housing dataset using scatter plots and regression lines to identify relationships between features and housing prices.
P.02Explore dimensionality reduction techniques — PCA, LDA, t-SNE, and autoencoders — for improving model performance and data visualization.
P.03Discover the key advantages of Random Forest algorithms — high accuracy, resistance to overfitting, feature importance, and handling missing data.
P.04GridSearchCV trains and cross-validates a model across every hyperparameter combination you define, then picks the best. How it works and where it costs you.
P.05Build a sentiment analysis pipeline with text preprocessing, CountVectorizer token counts, and Multinomial Naive Bayes classification on Twitter data.
P.06Learn how centroid-based clustering algorithms like K-means partition datasets into meaningful groups based on distance metrics.
P.07A guide to clustering algorithm types — partition-based, hierarchical, density-based, and model-based — with use cases and selection criteria.
P.08Explore time series forecasting methods including ARIMA, exponential smoothing, and seasonal decomposition for real-world prediction tasks.
P.09Understand decision tree algorithms for classification and regression, their pros and cons, and build an Iris classifier with Python code.
P.10Learn SVM theory including hyperplanes and the kernel trick, then build a classifier on the breast cancer dataset using scikit-learn.
P.11Build a logistic regression model to predict diabetes outcomes using the Pima Indians dataset, covering sigmoid functions, feature scaling, and evaluation.
P.12Implement K-Nearest Neighbors classification using scikit-learn with data visualization, model training, and performance evaluation on real datasets.