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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.