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
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 to describe and implement various machine learning algorithms in Python, including classification and regression techniques, and evaluate their performance using appropriate metrics.
Learn fundamental machine learning principles, including K nearest neighbor, linear regression, and model analysis, with prerequisites of Python programming and basic mathematics.
Gain foundational knowledge of Linear Algebra and Machine Learning models, explore the scalability of SparkML and Scikit-Learn, and gain practical experience by adjusting models and analyzing vibration sensor data in a real-world IoT example.
Gain comprehensive knowledge of ML pipelines, model persistence, Spark applications, data engineering, and hands-on experience with Spark SQL and SparkML for regression, classification, and clustering.
This course explores enterprise machine learning applications, assesses the viability of ML use cases, and addresses the prerequisites, data characteristics, and critical factors for developing and managing ML models.
Acquire the ability to create custom Datasets and DataLoaders in PyTorch and train a ResNet-18 model for image classification.
Acquire the ability to differentiate between static and dynamic training and inference, manage model dependencies, establish distributed training for defect tolerance and replication, and generate exportable models.
Explore the intersection of human and machine learning, covering supervised and unsupervised techniques, AI's impact on education, and applications in learning management systems, designed for educators and AI enthusiasts.
Featured Tools
Comprehend generative AI basics, practical applications, ethical implications, and potential impacts on student learning in education.
Generative AI for Your Benefit. Utilize Generative AI to develop and instruct personalized assistants.
Explore the fundamentals, applications, ethical implications, and future trends of generative AI in human resources.
This learning path provides a thorough overview of generative AI. This specialization delves into the ethical considerations that are essential for the responsible development and deployment of AI, as well as the foundations of large language models (LLMs) and their diverse applications.
Begin your professional journey as an AI Product Manager. Develop generative AI and product management skills that are in high demand to be job-ready in six months or less.