Overview
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This specialization is designed for post-graduate students interested in mastering social computing techniques to solve real-world problems. Through four in-depth courses, learners will explore key topics such as social network analysis, chatbot development, crowdsourcing, and AI performance optimization. You will learn to analyze social networks using R programming, create functional chatbots with AWS, and enhance AI models by leveraging crowdsourced data and machine learning techniques. By the end of the specialization, you will have practical experience applying advanced tools and methodologies across domains like social media analytics, conversational interfaces, and human-AI collaboration. This hands-on, industry-relevant learning path equips you with the skills needed to excel in social computing, artificial intelligence, and data-driven innovation.
Syllabus
Course 1: Introduction to Social Computing
- Offered by Johns Hopkins University. The course, "Introduction to Social Computing" offers a comprehensive exploration of the intersection ... Enroll for free.
Course 2: Social Network Analysis
- Offered by Johns Hopkins University. The "Social Network Analysis" course offers a comprehensive exploration of the intricate relationships ... Enroll for free.
Course 3: Training AI with Humans
- Offered by Johns Hopkins University. In the course "Training AI with Humans", you'll delve into the intersection of machine learning and ... Enroll for free.
Course 4: Chatbots
- Offered by Johns Hopkins University. The course "Chatbots" offers a deep dive into the world of chatbots, equipping learners with the skills ... Enroll for free.
- Offered by Johns Hopkins University. The course, "Introduction to Social Computing" offers a comprehensive exploration of the intersection ... Enroll for free.
Course 2: Social Network Analysis
- Offered by Johns Hopkins University. The "Social Network Analysis" course offers a comprehensive exploration of the intricate relationships ... Enroll for free.
Course 3: Training AI with Humans
- Offered by Johns Hopkins University. In the course "Training AI with Humans", you'll delve into the intersection of machine learning and ... Enroll for free.
Course 4: Chatbots
- Offered by Johns Hopkins University. The course "Chatbots" offers a deep dive into the world of chatbots, equipping learners with the skills ... Enroll for free.
Courses
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The course "Chatbots" offers a deep dive into the world of chatbots, equipping learners with the skills to design, build, and optimize conversational interfaces. You will explore the evolution of chatbot technology and understand the fundamental mechanics that drive their functionality. Through hands-on projects using Amazon Lex and AWS, you'll not only learn to create chatbots but also how to evaluate their performance using machine learning classifiers. What sets this course apart is its practical approach, allowing you to apply theoretical knowledge in real-world scenarios. Collaborating with peers, you’ll tackle challenges together, enhancing your problem-solving skills while fostering a supportive learning environment. By the end of the course, you’ll have the confidence to develop functional chatbots tailored for various applications, from customer service to personal assistants. Whether you are a novice looking to enter the tech field or an experienced professional aiming to expand your skill set, this course provides invaluable insights and practical tools to advance your career in the rapidly growing chatbot landscape. Join us to unlock the potential of conversational AI!
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The course, "Introduction to Social Computing" offers a comprehensive exploration of the intersection between technology and society, equipping learners with essential skills in social media analytics and influence. By covering a range of topics from data pre-processing to feature extraction and model evaluation, students will gain practical experience in applying machine learning techniques to real-world social media scenarios. Through hands-on modules, learners will delve into the dynamics of socio-technical systems and responsible AI, understanding how digital platforms shape human interactions and behaviors. The unique combination of theoretical insights and practical applications prepares students to navigate the complexities of social media, including analyzing firestorms and mitigating misinformation. What sets this course apart is its focus on gamification and cognitive biases in online environments, providing students with innovative strategies to enhance user engagement and promote critical thinking. Whether you are looking to advance your career in tech or simply understand the social implications of technology, this course will empower you to effectively analyze and leverage social computing in today’s digital landscape.
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The "Social Network Analysis" course offers a comprehensive exploration of the intricate relationships within social networks, emphasizing the theoretical and practical applications of network analysis. Through engaging modules, learners will delve into advanced topics in graph theory, centrality measures, and statistical modeling, equipping them with the skills to analyze and interpret social structures effectively. By completing this course, learners will gain a solid understanding of how to identify key influencers, measure network cohesion, and conduct hypothesis testing using empirical data. What sets this course apart is its blend of theoretical foundations and hands-on experience using R programming for network analysis, specifically with tools like 'statnet' and 'RSiena.' Whether you’re looking to enhance your skills in data analysis or seeking to understand the dynamics of social behavior, this course will serve as a vital resource. With a focus on real-world applications, learners will emerge equipped to tackle complex social phenomena, making significant contributions to their fields.
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In the course "Training AI with Humans", you'll delve into the intersection of machine learning and human collaboration, exploring how to enhance AI performance through effective data annotation and crowdsourcing. You’ll gain a comprehensive understanding of machine learning principles and performance metrics while developing practical skills in using platforms like Amazon Mechanical Turk (AMT) for crowdsourced tasks. This unique approach combines theoretical knowledge with hands-on experience, allowing you to implement Inter-Annotator Agreement (IAA) techniques to ensure high-quality annotated data. By completing this course, you will be well-equipped to design and conduct impactful crowdsourcing studies, improving AI models in real-world applications such as healthcare and research. Whether you're looking to enhance your skills in machine learning, optimize data collection processes, or understand the ethical implications of crowdsourcing, this course offers invaluable insights and tools.
Taught by
Ian McCulloh