Course Description:
Unlock the potential of data science with a focused journey into supervised machine learning using Python. This course explores the foundational concepts, algorithms, and practical applications of machine learning models to prepare learners for real-world problem-solving. From understanding AI basics to implementing algorithms like linear regression, logistic regression, and decision trees, this course equips participants with the skills to analyze data, build predictive models, and enhance decision-making processes.

Who Will Learn:
This course is ideal for data enthusiasts, aspiring data scientists, software developers, and professionals from diverse fields looking to enhance their analytical and programming expertise. A basic understanding of Python and statistics is recommended.

What Will Be Learned?

• Gain foundational insights into AI and machine learning concepts, types of ML, and the model development process, setting a solid groundwork for further exploration.
• Master linear regression, model evaluation, and feature engineering. Apply these skills in a hands-on project focused on domain-specific analysis and data preprocessing.
• Learn about logistic regression and its use in classification problems. Dive into exploratory data analysis, preprocessing techniques, and implementation using Python.
• Understand the principles of KNN, including Euclidean distance and lazy learning. Use Python to build and refine KNN models, employing techniques like SMOTE for data balancing.
• Explore decision trees and SVM algorithms, including attribute selection measures and different types of SVM. Understand their application in complex decision-making scenarios.
• Design and implement an AI chatbot to enhance learning. Learn chatbot setup, data preparation, and integration for interactive problem-solving and user assistance.