This course provides a comprehensive introduction to essential machine learning techniques, focusing on foundational concepts before exploring deep learning. Participants will learn about linear and logistic regression, classification algorithms such as k-NN and SVM, decision trees, and random forests. The course also covers clustering techniques like k-means and DBSCAN, as well as important model evaluation methods, including cross-validation and confusion matrices. With practical examples and intuitive explanations, this course equips learners with the knowledge needed to tackle machine learning tasks and understand how different algorithms and models can be applied to real-world data.

• Introduction to Machine Learning: Overview of machine learning, types of learning (supervised, unsupervised, and reinforcement) 
• Linear and Logistic Regression: Understanding the basics of regression, using linear regression for contnuous outcomes and logistc regression for binary classification.
• Classification Algorithms:  Introduction to common classification methods, including k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), and their applications.
• Decision Trees and Random Forests: Explanation of decision tree construction, advantages and disadvantages, and how random forests improve decision tree models by using an ensemble approach. 
• Clustering Techniques: Introduction to unsupervised learning methods like k-means and DBSCAN for grouping data based on similarity. 
• Model Evaluation Methods: Discussing techniques to assess model performance, including cross-validation, confusion matrices, accuracy, precision, recall, and F1 score

By the end of this course, participants will be able to: 

• Understand and apply key machine learning concepts
• Implement linear and logistic regression models
• Use classification algorithms like k-NN and SVM 
• Build and interpret decision trees and random forests
•  Apply clustering techniques such as k-means and DBSCAN
• Evaluate models using cross-validation and confusion matrices
•  Apply machine learning techniques to real-world datasets

The course follows a traditional classroom format with slide presentations, fostering interactive engagement and active participation. Participants will also apply machine learning algorithms using Python, with guidance on implementing them in practical scenarios.