The College of Engineering and Information Technology, in cooperation with the Continuing Education Center, launched a training course titled "Machine Learning and Programming using Python." The three-day course was presented by Prof. Dr. Noor Diya Kadhim Al-Shukurji from the University of Karbala, aiming to enhance the theoretical knowledge and practical skills of the participants in the field of machine learning and its programming applications.On its first day, the course addressed the theoretical foundations of machine learning, clarifying the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). It also introduced basic concepts and terminology, and reviewed the main patterns of machine learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.The second day was dedicated to discussing Classification as one of the most prominent tasks of supervised learning. The lecturer reviewed eight fundamental algorithms, including: Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Support Vector Machines (SVM), Naive Bayes, Artificial Neural Networks (ANN), and Gradient Boosting. This included the practical application of the Fit–Predict mechanism and model performance evaluation methods using accuracy and the Confusion Matrix.The third day covered Regression analysis by introducing several predictive algorithms and error measurement methods, such as Mean Absolute Error (MAE) and Mean Squared Error (MSE). Furthermore, it explained the importance of Feature Scaling in the data preprocessing stage, and distinguished between the states of Overfitting and Underfitting.The course adopted a training approach that combined theoretical explanation, numerical examples, and programming applications using Python. This contributed to enhancing the participants' ability to understand fundamental concepts and employ them practically in building machine learning models, ultimately forming an integrated methodological foundation in the fields of classification and regression, in preparation for expanding into model selection topics and Cross-Validation techniques.