Become a Machine Learning Specialist
Offered By: LinkedIn Learning
Course Description
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.
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
-
Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.
-
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.
-
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.
-
Learn how to use design patterns for scalable architecture and tools such as services and containers to deploy machine learning at scale.
-
Expand your data science skills by learning how to leverage the concepts of linear regression to solve real-world problems.
-
Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.
-
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.
-
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.
-
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.
Taught by
Keith McCormick, Dan Sullivan, Lillian Pierson, P.E. and Adam Geitgey
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