The Power of ML: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
A structured method for the effective application of machine learning, while also taking into account ethical considerations and business value.
Description for The Power of ML: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
Machine Learning Implementation: Acquire the skills necessary to recognize and execute machine learning solutions that generate value for your organization.
Predictive Modeling Methods: Acquire a comprehensive understanding of predictive modeling, particularly decision trees, and comprehend the data requirements necessary for the development of effective applications.
Performance and Profit Reporting: Acquire the ability to assess the predictive capabilities of machine learning models and to communicate their influence on the profitability of businesses.
AI Ethics and Avoiding Hype: Understand the ethical implications of machine learning, which include the management of expectations regarding artificial intelligence and the prevention of bias in predictive models.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by SAS
Duration: 3 weeks at 4 hours a week
Schedule: Flexible
Pricing for The Power of ML: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
Use Cases for The Power of ML: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
FAQs for The Power of ML: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
Reviews for The Power of ML: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for The Power of ML: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
From fundamental concepts to advanced methods such as deep learning and ensemble techniques, this program provides a comprehensive examination of machine learning techniques.
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.
Gain proficiency in the automation of software development processes through the utilization of generative artificial intelligence, AI-assisted programming, MLOps, and Amazon Web Services.
This program instructs instructors on the ethical and successful integration of AI, while promoting innovation and critical thinking among students.
A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.
To enhance machine learning models, this course offers fundamental understanding of artificial intelligence, machine learning methods like classification, regression, and clustering.
Preparing students for a future in artificial intelligence security, this course offers AI hacking, vulnerability discovery, and attack mitigating techniques.
To address OpenAI Gym challenges and real-world problems, this course offers pragmatic artificial intelligence methods like Genetic Algorithms, Q-Learning, and neural network implementation.
An extensive study of the applications of AI in marketing, ranging from competitive analysis to content optimization and conversion enhancement.
A practical guide to the use of generative AI for the purpose of composing, refining, and planning, utilizing structured and context-driven inputs.
Featured Tools
This program instructs instructors on the ethical and successful integration of AI, while promoting innovation and critical thinking among students.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
To enhance machine learning models, this course offers fundamental understanding of artificial intelligence, machine learning methods like classification, regression, and clustering.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.