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This Course Machine Learning offers a comprehensive, hands-on introduction to building and deploying machine learning models using Python. It is designed for learners with a foundational understanding of Python programming and familiarity with basic data analysis concepts. The course begins with a quick review of essential Python libraries such as NumPy, pandas, and Matplotlib, which form the foundation for data manipulation and visualization in data science. Learners are then introduced to core machine learning concepts, including supervised learning techniques such as classification and regression. The course places a strong emphasis on practical implementation using the scikit-learn package, enabling learners to build, train, and evaluate various models effectively. It also covers artificial neural networks and delves into deep learning through TensorFlow, where participants apply regression and classification techniques on real-world datasets. With the growing importance of unstructured data, the course explores neural network-based models for analyzing text and image data, equipping learners to handle diverse data types. By the end of the course, participants will have the ability to design and implement machine learning workflows, drawing actionable business insights from both structured and unstructured data. This skill set supports careers in data analysis, data engineering, and data science across industries.