Launching into ML en Espanol
Using Vertex AI and BigQuery ML, the course instructs students on how to improve data quality, construct AutoML models, and optimize models using performance metrics.
Description for Launching into ML en Espanol
Enhancing Data Quality and EDA: Discover how to improve data quality and conduct exploratory data analysis to acquire valuable insights prior to training models.
Building and Training AutoML Models: Learn how to construct, train, and deploy AutoML models without the need for coding using Vertex AI and BigQuery ML.
Model Optimization and Evaluation: Comprehend the process of optimizing machine learning models by utilizing performance metrics and loss functions to evaluate their effectiveness.
Scalable and Repeatable Machine Learning Pipelines: Acquire the ability to generate scalable and repeatable training, evaluation, and test data sets to ensure the consistent development of models.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Google Cloud
Duration: 3 weeks at 4 hours a week
Schedule: Flexible
Pricing for Launching into ML en Espanol
Use Cases for Launching into ML en Espanol
FAQs for Launching into ML en Espanol
Reviews for Launching into ML en Espanol
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Launching into ML en Espanol
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
Gain extensive knowledge in AI technologies relevant to digital marketing, involving precise data analysis, content creation, and tools for optimizing social media and consumer segmentation.
Develop expertise in the exposure and deployment of large language models via application programming interfaces (APIs), configure server environments, and incorporate natural language processing (NLP) functionalities into applications.
Learn proficiency in the construction, deployment, and safeguarding of large language models at scale, utilizing Rust, Amazon Web Services (AWS), and established DevOps best practices.
This course is dedicated to the setting up of GPU-based environments, the deployment of local large language models (LLMs), and their integration into Python applications utilizing open-source tools.
In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.
Gain an extensive understanding of TinyML applications, fundamental principles, and the ethical development of artificial intelligence.
Learn the skills necessary to operate, optimize, and implement large language models through practical experience with state-of-the-art LLM architectures and open-source resources.
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
Featured Tools
Explore the topic of AI-powered personalization by acquiring the skills necessary to utilize LangChain and ChatGPT.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
An insightful introduction of the foundational models, generative AI concepts, and their applications on a variety of platforms.
To enhance machine learning models, this course offers fundamental understanding of artificial intelligence, machine learning methods like classification, regression, and clustering.
A structured guide to the study of business opportunities in the chatbot space, as well as the comprehension, design, and deployment of chatbots using Watson Assistant.
Compare & Review AI Tool Directory AI Course Marketplace
