Machine Learning
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Basic understanding of programming (Python recommended).
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Familiarity with basic mathematics, including linear algebra, probability, and statistics.
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A computer capable of running Python and ML libraries (e.g., scikit-learn, pandas).
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No prior machine learning experience is required; this course is suitable for beginners.
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Aspiring data scientists and AI engineers.
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Developers and IT professionals who want to explore machine learning applications.
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Analysts and business professionals looking to leverage ML for data-driven decision-making.
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Students and hobbyists interested in learning machine learning from scratch.
- Description
- Curriculum
- Reviews
Machine Learning is a key technology driving modern AI applications, from recommendation systems to predictive analytics. This course provides a comprehensive introduction to machine learning concepts, algorithms, and practical implementation. Learners will explore supervised and unsupervised learning, regression, classification, clustering, model evaluation, and hands-on projects using popular ML libraries and tools. By the end of this course, participants will be able to build, evaluate, and deploy machine learning models for real-world problems.