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Machine Learning and Artificial Intelligence
April 19 @ 8:00 am - 5:00 pm
A comprehensive machine learning course curriculum typically includes a blend of theoretical knowledge and practical application. Here’s an outline based on current educational standards:
Machine Learning Course Curriculum
Introduction to Machine Learning:
- What is Machine Learning?
- History and evolution of Machine Learning
- Applications and case studies
Fundamentals of Machine Learning:
- Statistics, Probability, and Linear Algebra
- Calculus for Machine Learning
- Programming Languages (Python, R, Java, C++)
Deep Learning:
- Introduction to Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Generative Adversarial Networks (GANs)
Natural Language Processing (NLP):
- Text Preprocessing
- Word Embeddings
- Language Models
- Sequence-to-Sequence Models
Ethics and Future of Machine Learning:
- Privacy and Security in Machine Learning
- Future Trends and Research Directions
This workshop is designed to provide a broad introduction to modern machine learning, including both classical algorithms and state-of-the-art techniques. For those interested in more specialized areas, additional courses or modules can be pursued. For a complete beginner, foundational subjects like statistics, Python, data science, deep learning, and artificial intelligence are crucial