<ul><li>Supervised learning requires labeled data but excels in prediction tasks.</li><li>Unsupervised learning is ideal for exploratory data analysis but requires domain expertise to interpret results.</li><li>Reinforcement learning is suited for problems involving sequential decision-making but is computationally expensive to train.</li><li>Combining these paradigms and exploring hybrid approaches unlocks the full potential of machine learning.</li></ul>

Day 2: Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

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