• Understanding machine learning and its types
• Introduction to supervised learning
• Introduction to unsupervised learning

• Basics of Python programming language
• Data manipulation and analysis using Pandas library
• Introduction to NumPy for numerical computing
• Introduction, Getting Started Mean Median Mode Standard Deviation Percentile Data Distribution, Scatter Linear, Multiple Regression Scale, Train/Test Decision Tree Logistic, Grid Search K-search, Bootstrap Alligation Cross Validation AUC – ROC Curve K-nearest neighbour TensorFlow

• Real-world applications of ML in various industries
• Capstone project: Applying ML concepts to solve a simple problem

• Each week could consist of video lectures, reading materials, and quizzes.
• The syllabus focuses on providing a basic understanding of AI concepts and practical applications within a one-month timeframe.