Applications of TinyML
Gain an extensive understanding of TinyML applications, fundamental principles, and the ethical development of artificial intelligence.
Description for Applications of TinyML
TinyML Application Code: Comprehend the foundational code that underpins widely utilized TinyML applications and their practical applications in real-world scenarios.
Applications of TinyML Across Various Industries: Explore the implementation of TinyML within diverse sectors to facilitate practical solutions.
Fundamental Principles of TinyML: Explore the foundational concepts of Keyword Spotting, Visual Wake Words, and Anomaly Detection within TinyML systems.
Responsible AI Development: Examine the significance of ethical AI development within the framework of TinyML, along with its associated ethical implications.
Level: Intermediate
Certification Degree: yes
Languages the Course is Available: 13
Offered by: On edX provided by HarvardX
Duration: 3�4 hours per week approx 8 weeks
Schedule: Flexible
Pricing for Applications of TinyML
Use Cases for Applications of TinyML
FAQs for Applications of TinyML
Reviews for Applications of TinyML
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Applications of TinyML
The AI tool empowers non-programmers to construct and deploy AI, featuring data transformation, insights generation, identification of critical drivers, and prediction and forecasting functionalities to enhance business decision-making and planning processes.
Legaliser is an AI-powered platform facilitating contract analysis and drafting for personal and business needs, featuring contract evaluation, fairness assessment, risk analysis, and anomaly identification.
Utilize generative AI to advance in the field of data science. Develop hands-on generative AI skills that are in high demand to accelerate your data science career in under one month.
Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.
Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world
Utilize Generative AI to optimize marketing creativity. Explore the potential of Generative AI to revolutionize and influence your marketing organization.
Learn the significance, use cases, history, and pros and cons of generative AI in a business context, with a focus on its relationship to machine learning and services at Amazon.
Acquire practical skills to build a generative AI application by constructing a retrieval augmented generation (RAG) system using data, Qdrant, and LLMs.
Use AI skills to advance your engineering career. Acquire practical knowledge regarding deep learning methodologies for computer vision.
The "Introduction to Vertex AI" course provides a four-hour, practical, and fundamental overview of Vertex AI, ideal for professionals and enthusiasts aiming to leverage AI effectively.
Featured Tools
Develop and deploy AI models for a variety of real-world applications in regression and classification by mastering TensorFlow 2.0.
This course offers a concise summary of essential multivariate calculus for machine learning, including practical tools, vector calculations, function approximation, and neural network applications, to build confidence for advanced studies.
The topics of this AI course include the optimization of policies in reinforcement learning, the utilization of dimensionality reduction in unsupervised learning, and the classification and definition of constraints in supervised learning.
Acquire a basic understanding of digital transformation and cloud computing. Boost your cloud confidence to enable you to engage in discussions with colleagues in technical cloud positions and make informed business decisions regarding cloud technology.
The course concentrates on the development of an HTML framework for a Plotly Dash dashboard that includes interactive scatter plots, bar charts, radio buttons, and dropdowns. It emphasizes the evaluation of model performance and the visualization of dimensionality reduction outcomes.