Deep Learning and Reinforcement Learning

Resumen

Title:

Deep Learning and Reinforcement Learning

Description:

Engage with two of the most advanced disciplines in the field of Machine Learning through this comprehensive course offered by Coursera. Delve into Deep Learning, a key subset of Machine Learning that underpins many current AI applications, touching aspects of both Supervised and Unsupervised Learning.

Begin your journey with the foundational concepts of Neural Networks, exploring contemporary Deep Learning architectures before crafting your own models. The curriculum then pivots to Reinforcement Learning, an emerging facet of Machine Learning gaining prominence for its broad potential applications and its growing importance in AI research.

This course forms part of a larger IBM Specialization series, designed to furnish you with robust expertise across multiple Machine Learning spectrums including Supervised, Unsupervised, Deep, and Reinforcement Learning.

By course end, participants will be equipped to:

• Identify challenges best addressed through Unsupervised Learning.
• Discuss dimensionality issues and their impact on clustering tasks.
• Implement common clustering and dimension-reduction techniques.
• Evaluate and compare the efficacy of per-cluster models.
• Analyze and understand metrics essential for cluster analysis.

Target Audience:

This course is ideal for aspiring data scientists seeking practical experience in Deep Learning and Reinforcement Learning.

Prerequisites:

Participants should be comfortable with programming in a Python environment and have a foundational knowledge in Data Cleaning, Exploratory Data Analysis, Unsupervised and Supervised Learning, along with basic Calculus, Linear Algebra, Probability, and Statistics.

Provider:

Coursera

Categories:

Reinforcement Learning Courses, Deep Learning Courses, Neural Networks Courses, Unsupervised Learning Courses, Transfer Learning Courses.

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Impartido por

Mark J Grover and Miguel Maldonado

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Materias