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Learner Reviews & Feedback for Introduction to Artificial Intelligence (AI) by IBM

4.7
stars
19,303 ratings

About the Course

Artificial Intelligence (AI) is all around us, seamlessly integrated into our daily lives and work. Enroll in this course to understand key AI terminologies and applications, launch your AI career, or transform your existing one. This course covers core AI concepts, including deep learning, machine learning, and neural networks. You’ll examine generative AI models, including large language models (LLMs) and their capabilities. Further, you’ll analyze the applications of AI across domains, such as natural language processing (NLP), computer vision, and robotics, uncovering how these advancements drive innovation and use cases. The course will help you discover how AI, especially generative AI, is reshaping business and work environments. You’ll also explore emerging career opportunities in this rapidly evolving field and gain insights into ethical considerations and AI governance that shape responsible innovation. The course includes hands-on labs and a project, providing a hands-on opportunity to explore AI’s use cases and applications. You will also hear from expert practitioners about the capabilities, applications, and ethical considerations surrounding AI. This course is suitable for everyone, including professionals, enthusiasts, and students interested in learning the fundamentals of AI....

Top reviews

SC

Apr 9, 2020

The course design is excellent specially for beginners to study and understand the basic concepts in Artificial Intelligence. The lessons and course material are perfect and apt for this course-level.

VK

Sep 12, 2023

This Course is Very much Beneficial for the Beginners.In particular introducing Mr.Tanmay Bakshi & Mr. Polong Lin and sharing their Knowldge and Expertise is worth notable.Thank you IBM AI Team!

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By Patel U C

Jan 19, 2025

The gap between the supply and demand for generative AI-literate employees can be attributed to several factors: ### **Reasons for the Gap** 1. **Rapid Advancement of Technology**: Generative AI has evolved at a breakneck pace, and many education systems and training programs haven't kept up with the speed of change. 2. **Specialized Knowledge Requirements**: Generative AI involves complex concepts such as neural networks, prompt engineering, large language models (LLMs), and domain-specific adaptations, which require a strong foundation in mathematics, programming, and machine learning. 3. **Limited Expertise Pool**: The field of AI is relatively new, and there are fewer professionals with advanced expertise in generative AI as compared to traditional software development or data science roles. 4. **High Demand Across Industries**: As more industries recognize the transformative potential of generative AI, demand for these skills has skyrocketed, leading to competition for the limited available talent. 5. **Education Lag**: Academic programs and certifications often take time to develop and adapt, meaning there are fewer graduates with direct generative AI training. --- ### **How Organizations Can Address This Gap** 1. **Invest in Upskilling Current Employees**: - **Workshops and Bootcamps**: Conduct intensive training programs focused on generative AI tools, technologies, and practical applications. - **Online Learning Platforms**: Encourage employees to complete courses on platforms like Coursera, Udemy, and edX, which offer specialized AI tracks. - **Internal Mentorship**: Create mentorship programs where experienced AI professionals within the organization can train less experienced staff. 2. **Foster a Learning Culture**: - Encourage experimentation with generative AI tools like ChatGPT, DALL·E, or MidJourney for day-to-day tasks to build familiarity. - Provide incentives for employees to innovate and explore AI applications relevant to their roles. 3. **Partner with Academic Institutions**: Collaborate with universities and research institutions to offer customized training programs or internships that align with organizational needs. 4. **Leverage No-Code and Low-Code Platforms**: Provide employees with access to user-friendly AI tools that don’t require deep technical expertise, allowing non-technical staff to integrate generative AI into their work. 5. **Cross-Disciplinary Training**: Since generative AI intersects with various fields, encourage employees from diverse backgrounds (e.g., marketing, HR, and design) to understand how generative AI can apply to their domains. 6. **Build AI Awareness at All Levels**: Offer high-level sessions for leadership and strategic teams to understand the potential and limitations of generative AI, enabling better decision-making and strategic alignment. By adopting a multifaceted approach, organizations can close the skills gap and build a workforce capable of leveraging the full potential of generative AI.

By Robert B

Jun 17, 2025

Once again, an entire course on AI has NO MENTION AT ALL about the training datasets using already copywrited or trademarked intellectual property. These companies will go to ANY length to avoid mentioning what they have done: stealing millions of images from hard-working creative people to train their models. They perform incredibly delicate, elaborate dances around the subject, even within their own AI modules on the ethics of AI (!!) supposedly to address those ethical concerns, but they frame it as "who owns the images AI produces?" instead of asking "should these AI companies recompense artists for using their works without their permission?" This dance is sickening to me -- late-stage capitalism at its absolute worst. Shame. Shame on IBM.