This Machine Learning class is designed for students across disciplines such as B.Tech, BCA, BSc Computer Science, MTech, and MSc who are eager to understand and apply the fundamentals and advanced concepts of machine learning. Whether you are a beginner or have some programming and math background, this course will equip you with the skills needed to build intelligent systems and solve real-world problems using data-driven approaches.
Students will learn core concepts including supervised and unsupervised learning, classification, regression, clustering, and dimensionality reduction. The course also delves into practical techniques such as feature engineering, model evaluation, and optimization methods. Hands-on coding sessions using popular tools like Python and libraries such as scikit-learn, TensorFlow, or PyTorch will help students translate theory into practice. Essential mathematical foundations, including linear algebra, probability, and statistics, are also covered to strengthen comprehension.
Participants should bring a laptop with Python installed and basic familiarity with programming and statistics is recommended. A curious mind and problem-solving attitude are most important. By the end of the class, students will be capable of designing, implementing, and evaluating machine learning models for academic projects or industry applications, gaining a competitive advantage in the fast-growing AI domain.
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