ML: an overview
The topics of this AI course include the optimization of policies in reinforcement learning, the utilization of dimensionality reduction in unsupervised learning, and the classification and definition of constraints in supervised learning.
Description for ML: an overview
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by Politecnico di Milano
Duration: 2 hours (approximately)
Schedule: Flexible
Pricing for ML: an overview
Use Cases for ML: an overview
FAQs for ML: an overview
Reviews for ML: an overview
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for ML: an overview
A program that emphasizes the practical implementation of data science and machine learning to overcome obstacles through the use of Python.
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
This training provides professionals with knowledge and practical advice on AI ethics, compliance issues, and risk management.
Understand foundational knowledge of AI and RegTech, their societal implications, and the discourse around their future integration and obstacles.
Learn how to use AI technologies for personal development and active learning, embrace continuous learning, and cultivate a growth mindset.
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 insightful introduction of the foundational models, generative AI concepts, and their applications on a variety of platforms.
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
To enhance machine learning models, this course offers fundamental understanding of artificial intelligence, machine learning methods like classification, regression, and clustering.
This course covers Python programming, TensorFlow for linear regression, and app development for stock market prediction.
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
This program instructs instructors on the ethical and successful integration of AI, while promoting innovation and critical thinking among students.
Compare & Review AI Tool Directory AI Course Marketplace
