This introductory course provides a foundational understanding of Artificial Intelligence (AI) and Data Science. Students will learn key concepts, techniques, and tools for data analysis, machine learning, and AI applications. Through practical exercises and real-world examples, students will gain the skills needed to start a career in these rapidly evolving fields.
- Build Machine learning models with scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
- Build & train a neural network with TensorFlow to perform multi-class classification.
- Build an unsupervised learning model for clustering & anomaly detection.
- Build recommender systems with a collaborative filtering approach & a content-based deep learning method.
- Understand how to clean and organize data for analysis, and complete analysis and calculations using python & R.
- Learn key analytical skills (data cleaning, analysis, & visualization)
- Learn how to visualize and present data findings in dashboards.
