AI Skills: Basic and Advanced Techniques in Machine Learning Professional Certificate

What you will learn:
- Apply common operations (pre-processing, plotting, etc.) to datasets using Python.
- Explain the concept of supervised, semi-supervised, unsupervised machine learning and reinforcement learning.
- Explain how various supervised learning models work and recognize their limitations.
- Analyze which factors impact the performance of learning algorithms.
- Apply learning algorithms to datasets using Python and Scikit-learn and evaluate their performance.
- Optimize a machine learning pipeline using Python and Scikit-learn.
- Describe the main classes of clustering techniques.
- Implement k-means and hierarchical clustering.
- Motivate the need and choice of dimensionality reduction techniques.
- Implement Principal Component Analysis (PCA) for feature extraction.
- Explain how deep neural networks work and their advantages.
- Train deep neural networks for classification and regression tasks.
- Explain the basic concepts and techniques of reinforcement learning.
- Describe how reinforcement learning could be applied in real world applications.