Machine Learning Masterclass with Python, TensorFlow, GCP

Assess cyber security threats in AI-driven ecosystems and how fake data sabotages learning, while exploring defenses like data masking and secure identity verification against BEC and malware.

Master missing data imputation from simple mean or median imputation to regression-based methods, and learn multiple imputation with em algorithm to obtain approximately unbiased estimates under missing at random.

Explore reinforcement learning by examining an agent in an environment that learns from trial and error through rewards and punishments, guided by states, actions, and maximum reward.

Explore how linear regression uses feature engineering and polynomial terms to model horsepower and mpg, and navigate the bias-variance trade-off with increasing model complexity.

Explore variable selection in linear regression, comparing best subset and stepwise approaches to identify the most predictive features. Use mean squared error and information criteria to select a parsimonious model.

Explore statistical inference in linear regression by examining the sampling distribution of regression coefficients. Test beta with the t statistic and p-values under the null beta equals zero.

Explore how impurity gain ratio improves decision tree learning by penalizing high-cardinality attributes like unique identifiers, using entropy and Gini index to measure impurity and guide splits.

Transform numerical attributes into thresholds using queries like length is less than or equal to a constant, illustrated by iris petal length and width; the Gini index guides splits.

Build a decision tree classifier to predict Titanic survival, loading data, preprocessing categorical features, and exploring the survival distribution to estimate a base model.

Identify feature importance in a decision tree and perform feature engineering on Titanic data by deriving family size, extracting titles with regex, and creating a gender/child feature.

Explore hierarchical clustering: compare agglomerative and divisive approaches, visualize with a dendrobium tree, and learn linkage methods using similarity or dissimilarity metrics to merge clusters.

Explore hierarchical clustering and its linkage methods—single, complete, average, centroid, and Ward—explaining distance calculations, weighted sums, and their role in initializing k-means seeds.

Learn python-based image classification with TensorFlow on Google Colab, training on 60,000 fashion images and testing on 10,000 images across clothing categories.

Train a neural network on a 60,000-image 28×28 dataset with TensorFlow, preprocess by scaling pixels to 0–1, and compare training and test accuracy to illustrate overfitting and inference in Colab.