
Explore problem solving in AI by analyzing problem statements with their mathematical background, applying algorithms like minimax, and defining states, actions, goals, operators, and representations.
An agent perceives the environment with sensors, processes input, and acts through actuators in a perception cycle; robotic and software agents map input to output.
The lecture categorizes agents into five types—table driven, simple reflex, model based reflex, goal based, and utility based—and explains their environments, sensors, actuators, and percept history.
Explore the main machine learning types—supervised, unsupervised, semi-supervised, and reinforcement learning—and their real-world uses. Understand regression and classification, labeled versus unlabeled data, and clustering algorithms like k-means and DBSCAN.
Walk through a step-by-step machine learning workflow using linear regression to predict student scores, including data import, visualization, train-test split, and mean squared error, mean absolute error, and R2 evaluation.
The course Artificial Intelligence and Deep learning techniques provides the ,Knowledge on problem formulation and solving in AI and ML for real world situation,Impress interviewers by showing an understanding of the Knowledge and Reasoning Approaches with the learning techniques,exemplify the uninformed and informed search technique procedures for real world problems,Scenario based learning for all Artificial Intelligence and Deep learning techniques,Deep knowledge about Learning in Deep Learning and Neural Networks,Provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems. Build your deep learning foundations and learn effective applications,Work on curated industry Projects in cliennt side as a industry Expert, Gain knowledge in problem formulation and building intelligent agents, Understand the search technique procedures applied to real world problems, Understand the types of logic and knowledge representation schemes, Acquire knowledge in planning and learning algorithms, Gain knowledge in AI Applications and advances in Artificial Intelligence, Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications, Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks, Build a goodl portfolio with entire Artificial Intelligence and Deep learning techniques