Description for Math for ML with R
Algebra Fundamentals, Quadratic Equations, and Functions Review: Develop fundamental algebraic abilities and investigate the significance of functions and quadratic equations.
Foundations of Differential Calculus: Comprehend the fundamental concepts of differentiation and derivatives, which are essential for the examination of rates of change in a variety of mathematical models.
Utilizing Vectors and Matrixes: Acquire the ability to employ vectors and matrices to model and resolve intricate relationships in the fields of artificial intelligence (AI) and machine learning.
Fundamentals of Statistics and Probability: Acquire a comprehensive understanding of statistics and probability, which is crucial for the analysis of data and the formulation of well-informed decisions in the field of artificial intelligence.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On edX provided by IBM
Duration: 6�8 hours per week approx 8 week
Schedule: Flexible
Pricing for Math for ML with R
Use Cases for Math for ML with R
FAQs for Math for ML with R
Reviews for Math for ML with R
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Math for ML with R
Learn to apply advanced machine learning and deep learning models to real-world challenges by immersing yourself in the cutting-edge world of AI-powered finance and insurance.
A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.
In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.
Discover how to use Rust to apply DevOps ideas, automate system chores, and put logging and monitoring in place for effective application deployment and operation.
Study the ethical consequences of AI development and implementation, emphasizing generative AI, AI governance, and pragmatic ethical decision-making in practical contexts.
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
From fundamental concepts to advanced methods such as deep learning and ensemble techniques, this program provides a comprehensive examination of machine learning techniques.
A program that emphasizes the practical implementation of data science and machine learning to overcome obstacles through the use of Python.
Featured Tools
A practical guide to the use of generative AI for the purpose of composing, refining, and planning, utilizing structured and context-driven inputs.
A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.
An insightful introduction of the foundational models, generative AI concepts, and their applications on a variety of platforms.
Learn the fundamental techniques of supervised and unsupervised learning and apply them to real-world problems to unlock the potential of machine learning.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
Compare & Review AI Tool Directory AI Course Marketplace
