Introduction to machine learning: Definitions, history, applications, and basic concepts.

Types of machine learning and problems

Supervised, unsupervised, and reinforcement learning.

Data preprocessing

Data types, data cleaning, scaling, normalization, featurization / feature engineering.

Model validation and evaluation

Cross-validation, performance metrics (accuracy, precision, recall, F1-score).

Principles of regularization and hyperparameter tuning.

Basic regression and classification models

Linear and logistic regression, their applications and evaluation for use in machine learning, K-Nearest Neighbors, Naive Bayes.

Clustering and dimensionality reduction and their use in machine learning.

K-means, hierarchical clustering, PCA (Principal Component Analysis).

Regression and decision trees.

Ensemble learning

Random Forests, Bagging, Boosting.

Support Vector machines.

Applications and case studies: Real-world applications of machine learning in various industries.