Description for Project Planning & ML
Features of Course
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of Colorado Boulder
Duration: 17 hours (approximately)
Schedule: Flexible
Pricing for Project Planning & ML
Use Cases for Project Planning & ML
FAQs for Project Planning & ML
Reviews for Project Planning & ML
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Project Planning & ML
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
Learn to train and develop image classification and object detection systems using machine learning, and deploy these models to microcontrollers.
Gain a comprehensive understanding of AI terminology, applications, development, and strategy, while navigating ethical and societal considerations in a non-technical context.
Learn to develop, assess, and enhance machine learning models using Python libraries, covering introductory deep learning, supervised, and unsupervised learning algorithms.
The course's topics including the distinction between deep learning, machine learning, and artificial intelligence, the process of developing machine learning models, the difference between supervised and unsupervised learning, and the use of metrics for evaluating classification models.
Gain the skills needed for a machine learning engineering role and prepare for the Google Cloud Professional Machine Learning Engineer certification exam by learning to design, build, and productize ML models using Google Cloud technologies.