Artificial Intelligence & Machine Learning

COURSE DESCRIPTION

This course introduces participants to foundational concepts, algorithms, and applications of AI and ML. Through hands-on projects and practical exercises, participants will learn how to analyze data, develop predictive models, and apply machine learning techniques, empowering them to solve real-world problems and make informed decisions in diverse domains.

TOPICS COVERED INCLUDE

• Introduction: Success stories, Job market, Course Applications, Institute/work ethics, Introduction to Artificial Intelligence, A brief history of AI. AI terminology, State of the art techniques, Lab Installation for python language.
• Machine Learning Fundamentals: What is Data, What is Machine Learning, Supervised vs. Unsupervised learning, Evaluation Train-Test split, Validation.
• Regression: Univariate linear regression, Multivariate regression.
• Classification Algorithms: KNN, Naïve Bayes, Decision Trees, SVMs, Decision Trees, SVMs.
• Clustering: Clustering Classification vs. Clustering K-means Clustering.
• Time Series Analysis: Time Series Analysis, MLPFeed Forward neural networks.
• Neural Networks: Applications with computer vision, Classification and Detection.