Description for ML With Big Data
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of California San Diego
Duration: 21 hours (approximately)
Schedule: Flexible
Pricing for ML With Big Data
Use Cases for ML With Big Data
FAQs for ML With Big Data
Reviews for ML With Big Data
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for ML With Big Data
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.
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.
Learn to describe and implement various machine learning algorithms in Python, including classification and regression techniques, and evaluate their performance using appropriate metrics.
Featured Tools
In-depth study of the applications of machine learning and computer vision, as well as practical experience in data analysis and Python programming.
In order to facilitate effective learning, this course provides learners with the necessary skills to develop scalable and resilient ML solutions on AWS, combining theory and practical experience.
Through practical experiments utilizing TensorFlow and Google Cloud Platform, this�course offers a thorough grasp of machine learning, from strategy to deployment.
By providing learners with a practical guide to navigating the ethical complexities of AI and Data Science, this course empowers them to develop responsible and sustainable AI solutions.
Acquire proficiency in machine learning and deep learning methodologies, such as TensorFlow, CNNs, RNNs, LSTMs, and NLP, to facilitate efficient data analysis.