Develop a solid understanding of the fundamental principles and terminology of Artificial Intelligence, including machine learning, neural networks, and data mining.

Acquire practical skills in implementing basic AI algorithms and models, enabling participants to design simple AI systems.

Evaluate the ethical implications and societal impact of AI technologies, fostering a nuanced perspective on responsible AI development and deployment.

Apply AI techniques to solve diverse problems, from predictive analytics to pattern recognition, enhancing decision-making capabilities in professional contexts.

Demonstrate a comprehensive understanding of neural networks, activation functions, and the fundamentals of deep learning.

Develop and implement various neural network architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), for diverse AI applications.

Utilize optimization algorithms like stochastic gradient descent (SGD), Adam, and RMSprop to enhance the training efficiency and convergence of deep learning models

Analyze and apply deep learning techniques to real-world problems, including image classification, natural language processing, and time-series analysis

Assess the performance of deep learning models through metrics such as accuracy, precision, recall, and F1 score, ensuring robust and reliable AI solutions.