Distributed ML with Google Cloud ML
Obtain proficiency in the extension of the TensorFlow framework, the deployment of models to the Cloud ML Engine, and the repeatable evaluation of predictive models.
Description for Distributed ML with Google Cloud ML
Deep Neural Network Classifier with TensorFlow: Acquire the ability to extend Python TensorFlow frameworks to incorporate deep neural network classifiers for the purpose of advanced model development.
Wide and Deep Model Implementation: Enhance the predictive capabilities of the neural network classifier by incorporating deep and wide models.
Deployment of Cloud Machine Learning Engine: Make predictions through Python API calls and deploy trained models to the Cloud ML Engine.
Model Evaluation and Data Partitioning: Comprehend the process of dividing datasets into training and test sets and assessing predictive models in a consistent manner.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Google Cloud
Duration: 1 hour 30 minutes at your own pace
Schedule: Hands-on learning
Pricing for Distributed ML with Google Cloud ML
Use Cases for Distributed ML with Google Cloud ML
FAQs for Distributed ML with Google Cloud ML
Reviews for Distributed ML with Google Cloud ML
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Distributed ML with Google Cloud ML
NovaceneAI streamlines the organization of unstructured text data with AI algorithms, offering dedicated cloud hosting, adaptability across sectors, and robust data privacy measures.
Posit offers a comprehensive platform with enterprise solutions, cloud applications, community resources, and deployment solutions to enhance productivity in data science teams.
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 use generative AI tools to improve data science workflows, enhance datasets, and refine machine learning models through practical projects.
Gain a comprehensive understanding of AI's potential, ethical considerations, and applications in efficient programming and common coding tasks using various LLMs.
Master Python programming for software development and data science, including core logic, Jupyter Notebooks, libraries like NumPy and Pandas, and web data gathering with Beautiful Soup and APIs.
Understand AI, its applications, concepts, ethical concerns, and receive expert career guidance.
Gain a comprehensive understanding of AI terminology, applications, development, and strategy, while navigating ethical and societal considerations in a non-technical context.
Prepare for a vocation as a data scientist. Acquire hands-on experience and in-demand skills to become job-ready in as little as five months. No prior experience is necessary.
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.
Featured Tools
Master the strategic application of generative AI to improve decision-making and direct industry-specific applications.
Understand and apply statistical techniques to quantify prediction uncertainty, analyze probability distributions, and evaluate machine learning model efficacy using interval estimates and margins of error.
Machine learning mathematics. Find out about the mathematical prerequisites for applications in machine learning and data science.
The course "Building a Generative AI Ready Organization" offers the necessary components for the successful adoption of Generative AI within an organization. This course concentrates on business leaders and other decision-makers who are currently or potentially involved in Generative AI initiatives.
Learn to develop a text preprocessing pipeline, understand the theory behind Naive Bayes classifiers, and evaluate their effectiveness after training.