| Sumario: | Embark on a comprehensive journey into the world of machine learning with this expertly designed course. Begin with foundational principles, terminology of machine learning. Explore various types of machine learning problems, including regression and classification, and understand the crucial role of data. Progressing further, the course covers essential statistical techniques and Python programming. Learn to work with different data types, perform descriptive statistics, and understand probability theory and hypothesis testing. The Python modules span from basic syntax to advanced data manipulation with libraries like NumPy and pandas, providing a solid foundation for data analysis and model building. The course culminates with hands-on projects and case studies, blending theoretical knowledge with practical experience. You'll implement algorithms such as linear and logistic regression, Naive Bayes, decision trees, random forests, and support vector machines. The deep learning section offers a glimpse into neural networks. Through these projects, you'll gain the skills to optimize and evaluate models, ensuring their effectiveness in real-world scenarios.
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