By the end of this course, students will have a foundational understanding of key Machine Learning algorithms like Decision Trees, Random Forests, and KNN. They will gain practical skills in using these models for ML tasks, fine-tuning hyperparameters, and utilizing environments like Google Colab and Anaconda. Students will also explore AI project cycles, regression models, and integrate tools like PictoBlox and Quarky for hands-on projects, including robot movements.