AI & Machine Learning: From Scratch to Advanced Models
5 days ago
This course covers everything from AI fundamentals to advanced neural networks with hands-on learning. You'll explore machine learning types, key algorithms, deep learning, and model optimization.
The course includes training neural networks, data preprocessing, and techniques for performance improvement. You'll work on real-world datasets with a personalized final project.
This course is hands-on and practical, ensuring that you not only grasp the concepts but also apply them in real-world scenarios.
While many of our examples are from naval and ocean engineering, the principles and techniques are universal and relevant to any engineering or technical field. Plus, in the final project, you'll have the opportunity to work on a dataset of your choice, making the learning experience truly personalized.
What will you learn?
- have a deep understanding of how machine learning models work;
- be able to build, train, and evaluate your own AI models;
- have a working machine learning pipeline using real-world data;
- be prepared to explore more advanced AI topics.
More concretely, you will learn:
- AI, Machine Learning, and Deep Learning, why it's booming, and how it applies to engineering.
- Key machine learning concepts: data handling, models, evaluation, and optimization.
- Core algorithms: linear regression, logistic regression, decision trees, K-nearest neighbors, and support vector machines, each with real-world applications.
- Deep Learning fundamentals: how neural networks learn; you'll even build one from scratch.
- Implementation with PyTorch: data preprocessing, model training, evaluation, and optimization.
- Advanced architectures: convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
- Practical insights and coding tips based on our hands-on experience working with large datasets and complex models.
Capstone Project
You will take on a capstone project where you can choose from a variety of datasets, bring your own, or work with the ShipD dataset from MIT to analyze 30,000 ship hull geometries and their wave resistance.
Prerequisites
- No prior knowledge of Artificial Intelligence (AI) or Machine Learning is required.
- Although many of our examples are drawn from naval and ocean engineering, the principles and techniques taught in this course are applicable to any engineering or technical field.
- To fully benefit from the material, a basic understanding of Python programming, fundamental mathematical concepts, and a general familiarity with data analysis will be helpful.
- To ensure you're well-prepared, we've created the Introduction to Python course, which covers everything you need to know about Python—from the basics to more advanced topics like classes, with a focus on data analysis.
Course Organization
The course is video-based and on-demand, allowing you to learn at your own pace, wherever and whenever you want. It includes videos, quizzes, and the course book and provides access to the course's virtual private classroom, where you can interact with the instructor and other students. You will receive the Course Certificate (Certificate of Achievement) upon completing all lessons, passing the quizzes, and having your course assignment approved.
Welcome aboard
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