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This course excels by providing a dual approach: it delves into the theoretical underpinnings of machine learning, explaining the ‘why’ behind algorithms, and then immediately reinforces these concepts with hands-on coding examples. You’ll learn to utilize essential libraries like Scikit-Learn, NumPy, Pandas, and Matplotlib/Seaborn to analyze data, create compelling visualizations, and build predictive models. The curriculum covers foundational topics such as the difference between classification and regression, model evaluation metrics, supervised learning, and key algorithms like Linear Regression and Logistic Regression, complete with practical Python implementations.
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