Learn AI: Computer Vision, NLP, Tabular Data – build powerful models with Google AutoML & Apple CreateML
β±οΈ Length: 3.6 total hours
β 4.51/5 rating
π₯ 113,048 students
π October 2023 update
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- Course Overview
- Embark on a streamlined and highly practical exploration into the realm of Artificial Intelligence and Machine Learning, specifically engineered for those with no prior programming expertise.
- Uncover the profound capabilities of Automated Machine Learning (AutoML), a groundbreaking methodology that democratizes AI development by abstracting away the intricacies of traditional coding.
- Acquire hands-on proficiency with two of the industry’s most prominent no-code platforms: Google Cloud AutoML for robust, scalable cloud-based solutions, and Apple CreateML for seamless integration into Apple’s ecosystem.
- Grasp the paradigm shift from complex, code-centric ML development to intuitive, visual interfaces that drastically accelerate the creation, training, and deployment of intelligent models.
- Navigate a meticulously curated curriculum, designed to deliver maximum impact and comprehensive understanding within a remarkably short duration of just 3.6 hours, making advanced AI principles accessible to a broad audience.
- Delve into diverse application areas of AI, including sophisticated image recognition, natural language comprehension, and predictive analytics for structured tabular data, all without writing code.
- Position yourself as a modern innovator by learning to conceptualize, develop, and integrate AI solutions into real-world scenarios, leveraging readily available and cutting-edge automation tools.
- Discover a direct path to transforming ideas into functional AI prototypes, bypassing the steep learning curve often associated with traditional machine learning development.
- Requirements / Prerequisites
- A foundational enthusiasm for Artificial Intelligence and its potential to solve real-world problems is the primary and most essential prerequisite for this course.
- Basic computer literacy, including confidence in operating system navigation, web browsing, and fundamental file management, will ensure a smooth learning experience.
- Consistent and reliable internet access is necessary for engaging with cloud-based platforms like Google Cloud AutoML and for downloading any required software for Apple CreateML.
- While not strictly mandatory, ownership of an Apple device (specifically a Mac running macOS) is strongly recommended to fully participate in the Apple CreateML sections and practice app integration.
- A Google account will be required for accessing Google Cloud services; the course is structured to primarily utilize free-tier resources, though users should be aware of potential charges for extended usage.
- No advanced mathematical background, statistical expertise, or prior exposure to data science concepts is necessary, as the AutoML tools intelligently handle these complexities.
- Skills Covered / Tools Used
- Automated Model Training: Configure and initiate sophisticated machine learning model training processes using Google’s cloud infrastructure and Apple’s on-device capabilities, entirely without code.
- Data Preparation and Annotation: Master the critical steps of collecting, labeling, and structuring diverse datasets to optimize them for effective utilization within AutoML platforms.
- Model Evaluation Interpretation: Develop the ability to critically analyze and understand various model performance metrics (e.g., precision, recall, F1-score) provided by automated tools.
- Cloud Model Deployment: Learn best practices for deploying trained AI models via Google Cloud, enabling scalable, API-driven integration into existing web services or new applications.
- Edge AI Integration with CreateML: Understand how to export CreateML models and integrate them into native Apple applications (iOS, macOS), facilitating on-device inference for enhanced privacy and offline functionality.
- Intuitive Interface Navigation: Become proficient in navigating the user-friendly interfaces of the Google Cloud Console (specifically AutoML Vision, Natural Language, and Tables services) and Apple Xcode’s CreateML environment.
- Practical Problem Decomposition: Cultivate the analytical skill to deconstruct real-world challenges into identifiable machine learning problems amenable to an AutoML solution, from data input to desired output.
- Resource Management: Gain awareness of cloud resource usage within Google Cloud, understanding how to manage projects and monitor costs effectively, even within the free tier.
- Benefits / Outcomes
- Rapid Prototyping Capability: Significantly reduce the time and technical expertise traditionally needed to translate an AI concept into a functional model, fostering agility in innovation.
- Empowered AI Innovation: Unlock your potential to design and build intelligent solutions and AI-powered features, irrespective of your coding or deep technical background.
- Enhanced Career Readiness: Acquire highly sought-after, contemporary skills in applied AI development using industry-standard platforms, elevating your value in various professional domains.
- Tangible Project Portfolio: Develop practical, showcase-ready AI models and potentially simple application prototypes that can significantly bolster your personal or professional portfolio.
- Strategic Problem-Solving Mindset: Cultivate a keen ability to identify real-world opportunities where AI can introduce significant value, transforming complex problems into manageable automated solutions.
- Bridging Business & Technology: Gain the confidence to articulate and contribute meaningfully to AI initiatives, effectively communicating requirements and capabilities with technical teams.
- Foundation for Future Learning: Establish a robust practical and conceptual foundation in applied machine learning, serving as an ideal springboard for more advanced studies in AI theory or specialized engineering.
- Cross-Platform Versatility: Achieve proficiency in developing AI solutions compatible with both scalable cloud environments (Google) and popular consumer devices (Apple), broadening your deployment options.
- Creative Product Development: The integrated course project encourages you to ideate and conceptualize your own AI-centric product, fostering an entrepreneurial spirit and practical application of new skills.
- PROS
- Universal Accessibility: This course uniquely removes the formidable coding barrier, making sophisticated AI development genuinely accessible for absolute beginners across all backgrounds.
- Dual-Platform Mastery: Provides invaluable hands-on experience with two leading AutoML platforms (Google & Apple), equipping learners with a versatile skill set for diverse AI deployment scenarios.
- Highly Practical Focus: Emphasizes tangible project outcomes and real-world problem-solving, ensuring learners can immediately apply their newly acquired knowledge and skills.
- Efficient Learning Path: Delivers a substantial amount of practical AI knowledge and capability within a remarkably concise 3.6-hour duration, ideal for busy professionals and students.
- Exceptional Peer Endorsement: Boasts an outstanding 4.51/5 rating from over 113,000 students, consistently demonstrating its high quality, effectiveness, and strong learner satisfaction.
- Continuously Updated Content: Regularly refreshed (last updated October 2023), guaranteeing that the material remains current with the latest advancements and best practices in the rapidly evolving AI landscape.
- CONS
- Limited Theoretical Depth: While excelling in practical application, the course intentionally streamlines complex concepts, foregoing deep dives into underlying mathematical algorithms or advanced machine learning theory.
Learning Tracks: English,Development,Data Science
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