Instructor: Andrew Ng Course Link: Coursera - Machine Learning Specialization
The Machine Learning Specialization offered by DeepLearning.AI and Stanford University on Coursera provides a comprehensive series of courses covering various aspects of machine learning. Here’s an overview of the sections within the specialization, along with descriptions for each:
- Supervised Machine Learning introduces the fundamental concepts and techniques of supervised learning, where the model learns from labeled data to make predictions or decisions. You’ll explore popular supervised learning algorithms such as linear regression, logistic regression, support vector machines (SVMs), and decision trees. Additionally, you’ll learn about model evaluation, cross-validation, and regularization techniques to improve model performance.
- Advanced Learning Algorithm dive deeper into advanced machine learning algorithms and techniques beyond the basics covered in supervised learning. You’ll explore topics such as neural networks, deep learning, kernel methods, and ensemble methods. You’ll also learn about optimization algorithms, hyper-parameter tuning, and strategies for training deep neural networks.
- Unsupervised Learning This section focuses on unsupervised learning, where the model learns from unlabeled data to discover patterns, structures, or relationships within the data. You’ll learn about clustering algorithms such as K-means clustering, hierarchical clustering, and Gaussian mixture models. Additionally, you’ll explore dimensionality reduction techniques such as principal component analysis (PCA) and independent component analysis (ICA).