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LinkedIn Learning

Become a Machine Learning Specialist

via LinkedIn Learning Path

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Overview

Are you ready to take a deeper dive into mastering the concepts and techniques involved in machine learning? This learning path shows how machine learning algorithms work and how to design them yourself. There's a lot to learn in this rapidly growing (and highly recuited-for) field, and these courses will give you an extremely solid skill set.
  • Explore the concepts and techniques behind designing machine learning algorithms
  • Learn how recommendation systems work and how to build them
  • Master how to design machine solutions for different applications

Syllabus

Courses under this program:
Course 1: Machine Learning and AI Foundations: Decision Trees with SPSS
-Establish a strong foundation in ML by exploring the IBM SPSS Modeler and learning about CHAID and C&RT. This course is designed to help expand your data science skills.

Course 2: Deploying Scalable Machine Learning for Data Science
-Learn how to use design patterns for scalable architecture and tools such as services and containers to deploy machine learning at scale.

Course 3: Building a Recommendation System with Python Machine Learning & AI
-Discover how to use Python to build programs that can make recommendations. This hands-on course explores different types of recommendation systems, and shows how to build each one.

Course 4: Machine Learning and AI Foundations: Clustering and Association
-Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.

Course 5: Machine Learning and AI: Advanced Decision Trees with SPSS
-Work toward a mastery of machine learning by exploring advanced decision tree algorithm concepts. Learn about the QUEST and C5.0 algorithms and a few advanced topics.

Course 6: Machine Learning and AI Foundations: Classification Modeling
-Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.

Course 7: Machine Learning and AI Foundations: Value Estimations
-Discover how to solve value estimation problems with machine learning. Learn how to build a value estimation system that can estimate the value of a home.

Course 8: Machine Learning & AI Foundations: Linear Regression
-Expand your data science skills by learning how to leverage the concepts of linear regression to solve real-world problems.

Course 9: Machine Learning and AI Foundations: Recommendations
-This project-based course shows programmers of all skill levels how to use machine learning to build programs that can make recommendations—like recommending new products.

Courses

Taught by

Keith McCormick, Dan Sullivan, Lillian Pierson, P.E. and Adam Geitgey

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