Description for ML con Spark (MLlib) en Databricks
Level: Advanced
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Coursera Project Network
Duration: 2 hours
Schedule: Hands-on learning
Pricing for ML con Spark (MLlib) en Databricks
Use Cases for ML con Spark (MLlib) en Databricks
FAQs for ML con Spark (MLlib) en Databricks
Reviews for ML con Spark (MLlib) en Databricks
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for ML con Spark (MLlib) en Databricks
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.
Master the operations of large language models. Acquire proficiency in the deployment, management, and optimization of extensive language models on a variety of platforms, such as Azure, AWS, Databricks, local infrastructure, and open source solutions, through practical projects.
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.
Featured Tools
Become an expert in the field of artificial intelligence. Develop effective strategies for the application of Artificial Intelligence techniques to address business challenges.
The course encompasses the following topics: the development of a text processing pipeline, the comprehension of Naive Bayes classifier theory, and the assessment of the efficacy of classification models following training.
Develop and evaluate a neural network that can identify handwritten numerals, implement One Hot Encoding for classification, and evaluate the efficacy of the model through practical exercises.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
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.