Course Objective: This course is designed to introduce professionals to the foundational and practical aspects of Artificial Intelligence (AI), covering essential techniques in machine learning, deep learning, natural language processing, and computer vision. It emphasizes hands-on experience using Python-based tools and frameworks to build and evaluate AI models for real-world applications across industries such as business, healthcare, and finance. Participants will develop a strong conceptual and practical grasp of AI, empowering them to design, implement, and evaluate AI-driven solutions in a responsible and ethical manner.
Learning Outcomes:
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Define key concepts in Artificial Intelligence and distinguish AI from Machine Learning and Deep Learning.
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Apply Python programming and essential libraries to process and analyze data for AI tasks.
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Clean, transform, and visualize data for effective exploratory data analysis.
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Build and evaluate basic machine learning models for supervised and unsupervised tasks using scikit-learn.
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Understand the fundamentals of neural networks and develop simple deep learning models using TensorFlow or Keras.
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Apply Natural Language Processing techniques for tasks like sentiment analysis and text classification.
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Understand the principles of computer vision and implement simple CNN models for image recognition.
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Explore real-world AI applications and use cases across different sectors.
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Complete an AI capstone project, from problem definition to model development and presentation.
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Understand ethical considerations and the impact of AI on society and industry.