Duke University: Introduction to Machine Learning

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).

Learning Outcomes:

• Understand Core ML Concepts – Learn supervised and unsupervised learning, classification, regression, and clustering.
• Implement ML Algorithms – Apply key algorithms like linear regression, decision trees, and neural networks using Python.
• Evaluate Model Performance – Use metrics such as accuracy, precision, recall, and F1-score to assess models.
• Work with Real-World Data – Preprocess, clean, and analyze datasets for effective machine learning applications.
• Apply ML Ethically – Understand bias, fairness, and ethical considerations in machine learning models.

Additional Features:

• No prerequisites required
• Interactive exercises & real-world examples
• Ivy School Certificate Available
• Duke University Certificate (Optional) Additional Cost $60

Duke University Instructors: