Machine Learning Projects A-Z : Kaggle and Real World Pro

Explore a real-world medical cancer dataset from a Google case study, analyze and clean data, and build a machine learning solution to classify cancer types; compare Kaggle and industry perspectives.

Frame a multi-class classification problem from real-world data by prioritizing probability-based predictions, minimizing costly medical errors, ensuring interpretability, and balancing latency with algorithm choices.

In this lecture, analyze a categorical gene column and compare one-hot encoding with response encoding, showing how replacing categories with class probabilities reduces dimensionality.

Explore the second categorical column by analyzing its distribution and applying one-hot and response encoding. Train a logistic regression model, tune alpha, and evaluate log loss to judge its usefulness.

Process the X tax data with word-count features, apply one-hot and response encoding, compute log probabilities and log loss, and set up a calibrated logistic regression model.

Learn data pre-processing before building machine learning models. Implement reusable functions for backlog loss and plotting confusion matrices, and apply response encoding with stacking of columns for integrated datasets.

Build a random forest using one-hot encoder and response coding, tuning depth and estimators to minimize log loss and optimize train, test, and cross-validation performance.

Build a stacking model by combining logistic regression, SVM, and neighbors on one hot or response encoded data. Use log loss to select the best Kaggle-ready model.

Derive new text features like frequency, word counts, verbs, and common words, then compare basic and advanced feature engineering to identify meaningful columns.

Learn practical text preprocessing for machine learning: clean web page text, remove tags and punctuation, apply stemming and stop-word removal, and normalize contractions and numbers to improve similarity detection.

Learn advanced feature engineering after preprocessing, including tokenization, stop words, and metrics like common word count (cwc) and ctc to refine text features.

Discover methods for measuring document similarity by focusing on common words, intersections, and metrics like the longest substring ratio and fuzzy wuzzy.

Apply idf-weighted vectors using spaCy and glove within a linear program, to build a 15-feature data frame for duplicate detection.

Explore building a multi-label prediction model for Stack Overflow questions using Kaggle data, with focus on title, body, and tags, and evaluating with precision and recall.

Explore hamming loss as mismatches divided by total samples with a prediction versus actual example. Store data in sqlite, remove duplicates, and prepare a clean dataset for analysis.

Explore transforming multi-label problems into binary relevance (one-vs-rest), classifier chains, or label powerset, and learn data representations with scikit-learn to handle sparse, small datasets.