Launching into ML en Espa�ol
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 Espa�ol
Features of the Course:
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 Espa�ol
Use Cases for Launching into ML en Espa�ol
FAQs for Launching into ML en Espa�ol
Reviews for Launching into ML en Espa�ol
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 Espa�ol
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
Welcome to the 'Gen AI for Code Generation for Python' course, where you will begin a journey to hone and expand your abilities in the field of code generation using Generative AI.
Utilize Generative AI to optimize marketing creativity. Explore the potential of Generative AI to revolutionize and influence your marketing organization.
Learn to use the latest LLM APIs, the LangChain Expression Language (LCEL), and develop a conversational agent.
Master coding basics and create a Hangman game using generative AI tools like Google Bard in a beginner-friendly, 1.5-hour guided project.
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.
Learn about AI principles and platforms like IBM Watson and Hugging Face, integrate RAG technology for chatbot intelligence, create web apps using Python libraries, and develop interfaces with generative AI models and Python frameworks.
Featured Tools
Master logistic regression for cancer classification, dataset acquisition via Kaggle API, and cloud-based development with Google Colab.
Learn to implement and apply unsupervised learning techniques, focusing on clustering and dimension reduction algorithms, in a business environment.
Gain skills in computer vision, convolutional neural networks, and AI applications through the Deep Learning Specialization to advance your career in AI technology.
The course provides comprehensive coverage of AI and ML's increasing integration, structured into three sections focusing on business strategy, fundamental technologies, and hands-on projects, to aid in strategy development and technical planning.
Gain expertise in Bayesian statistics, Bayesian inference, and R programming through comprehensive courses, active learning, and a culminating real-world data analysis project.