Machine Learning in Computer Vision
Using the complete machine learning pipeline in computer vision, this course teaches students how to use MATLAB for object detection and classification in images.
Description for Machine Learning in Computer Vision
Practical Computer Vision Tasks: Concentrates on two fundamental computer vision tasks�image categorization and object detection�implemented in real-world contexts.
Comprehensive Machine Learning Workflow: Encompasses the full spectrum from data preparation to model evaluation, offering an exhaustive methodology for computer vision applications.
MATLAB-Centric Learning: MATLAB, is a prevalent tool in engineering and science, with complimentary access granted throughout the course duration.
Recommended Prerequisite Knowledge: Previous expertise in image processing is advantageous; the course recommends completing the Image Processing for Engineering and Science specialty for novices.
Level: Beginner
Certification Degree: Yes/No
Languages the Course is Available: 21
Offered by: On Coursera provided by MathWorksDuration: 11 hours (approximately)
Schedule: Flexible
Pricing for Machine Learning in Computer Vision
Use Cases for Machine Learning in Computer Vision
FAQs for Machine Learning in Computer Vision
Reviews for Machine Learning in Computer Vision
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Machine Learning in Computer Vision
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.
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.
Investigate the field of artificial intelligence and machine learning. While investigating the transformative disciplines of artificial intelligence, machine learning, and deep learning, enhance your Python abilities.
Featured Tools
Learn to construct end-to-end data pipelines in Databricks, utilize SQL for data manipulation and analysis, and generate valuable business insights with our AI course.
Discover the process of identifying machine learning model types, training and deploying predictive models using Azure Machine Learning's automated capabilities, developing regression, classification, and clustering models with Azure Machine Learning Designer, and deploying models seamlessly without scripting.
Learn to distinguish between different types of machine learning, prepare data for model development, build and evaluate Python-based models for both supervised and unsupervised learning, and choose the right model and metric for a given algorithm.
Explore LLM potential, address limitations, devise business strategies, and stay updated on LLM trends for effective implementation in business operations.
Generative AI facilitates daily tasks, decision-making, and idea generation, emphasizing responsible use, leveraging prompting techniques, and staying updated on AI advancements.