This course offers an introduction to machine learning (ML) concepts and techniques. Students will be introduced to ML classifications related to supervised, unsupervised, and reinforcement learning, along with regression and classification methods. The ML process, including data preparation, exploratory data analysis (EDA), model selection, training, validation, and testing, will be covered. The course will cover data preprocessing methods, such as handling missing data, managing categorical data, and scaling features. The course will also delve into dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Students will gain insights into best practices for model evaluation and hyperparameter tuning. Additionally, the course will introduce basic models from the Scikit-Learn library, equipping students with practical skills for implementing ML solutions