Machine Learning Fundamentals is an intensive course designed to provide a comprehensive understanding of the core principles and techniques in machine learning. The course covers essential concepts, algorithms, and tools necessary for building and evaluating machine learning models for regression and classification tasks. Students will gain hands-on experience through practical exercises and projects using popular libraries such as NumPy, Pandas, and data visualization tools.

• Introduction to Machine Learning
• Data Processing with NumPy and Pandas
• Data Visualization
• Statistics for Machine Learning
• Regression Models
• Classification Models
• Model Evaluation and Validation

Throughout the course, students will work on hands-on exercises and projects that involve real-world datasets to reinforce their understanding of the topics covered. By the end of the course, participants will have a solid foundation in machine learning, including data preprocessing, visualization, statistical analysis, and modeling for both regression and classification problems.

Prerequisites: Basic programming knowledge in Python and familiarity with fundamental concepts of statistics will be helpful but not mandatory.

Note: This course is designed for individuals with a strong interest in machine learning and data analysis. It serves as an excellent starting point for students, data scientists, software engineers, and professionals seeking to develop their skills in machine learning and its applications.