This short course on Artificial Intelligence (AI) and Big Data Analysis provides a comprehensive introduction to the fundamental concepts, techniques, and tools used in the industry. The curriculum covers key areas such as machine learning, data mining, neural networks, natural language processing, and big data technologies. Practical applications and case studies are integrated to ensure students can apply their knowledge to real-world scenarios.
Candidates having Matriculation / O-Levels and above are eligible to apply for this course.
Lectures, Practical Sessions, Case Studies, Group Projects, Assessments
Foundational Knowledge: Equip students with a strong foundation in AI and Big Data Analysis concepts and techniques. Technical Skills: Develop practical skills in using various tools and technologies for data analysis and machine learning. Application: Enable students to apply theoretical knowledge to real-world problems and datasets. Innovation: Encourage innovative thinking and problem-solving using AI and Big Data methodologies.
By the end of the course, students will be able to Understand Key Concepts: Explain fundamental concepts of AI, machine learning, and big data technologies. Data Processing: Perform data preprocessing, cleaning, and exploratory data analysis. Model Building: Develop and evaluate machine learning models using appropriate algorithms and tools. Big Data Handling: Utilize big data frameworks like Hadoop and Spark for processing large datasets. Application of Techniques: Apply AI and big data techniques to solve real-world problems through case studies and projects. Critical Thinking: Analyze and interpret data-driven results to make informed decisions. Collaboration: Work effectively in teams to develop and present comprehensive projects. Continuous Learning: Demonstrate the ability to continue learning and adapting to new AI and big data technologies and methodologies.
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