Machine Learning and AI Foundations: Clustering and Association
This course provides an introduction to the fundamentals of machine learning and AI, focusing on unsupervised learning techniques such as clustering, association rules, and anomaly detection. Gain the skills to apply these algorithms to real-world problems. ▼
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Course Feature
Cost:
Free Trial
Provider:
LinkedIn Learning
Certificate:
No Information
Language:
English
Start Date:
Self Paced
Course Overview
❗The content presented here is sourced directly from LinkedIn Learning platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
This course provides an introduction to the fundamentals of machine learning and artificial intelligence (AI). It covers the use of leading machine-learning techniques such as cluster analysis, anomaly detection, and association rules to get accurate, meaningful results from big data. Participants will learn how to apply these techniques to real-world problems and gain an understanding of the underlying principles and algorithms. The course also covers the use of popular machine-learning libraries and frameworks, as well as best practices for data preparation and model evaluation. By the end of the course, participants will have the skills and knowledge to apply machine learning and AI to their own projects.
[Applications]
The application of this course can be seen in a variety of industries, such as marketing, finance, healthcare, and retail. By understanding the fundamentals of clustering and association, professionals can use machine learning to identify patterns in data and make more informed decisions. Additionally, this course can be used to develop predictive models that can be used to forecast future trends and outcomes. Finally, this course can be used to develop automated systems that can detect anomalies in data and alert users to potential issues.
[Career Paths]
1. Data Scientist: Data Scientists use machine learning and AI techniques to analyze large datasets and uncover meaningful insights. They use clustering and association rules to identify patterns and trends in data, and develop predictive models to forecast future outcomes. Data Scientists are in high demand as organizations increasingly rely on data-driven decision making.
2. Machine Learning Engineer: Machine Learning Engineers design, develop, and deploy machine learning models to solve complex problems. They use clustering and association rules to identify patterns and trends in data, and develop algorithms to automate decision making. Machine Learning Engineers are in high demand as organizations look to leverage the power of AI to gain a competitive edge.
3. AI Researcher: AI Researchers use machine learning and AI techniques to develop new algorithms and models to solve complex problems. They use clustering and association rules to identify patterns and trends in data, and develop new algorithms to automate decision making. AI Researchers are in high demand as organizations look to leverage the power of AI to gain a competitive edge.
4. Business Intelligence Analyst: Business Intelligence Analysts use machine learning and AI techniques to analyze large datasets and uncover meaningful insights. They use clustering and association rules to identify patterns and trends in data, and develop predictive models to forecast future outcomes. Business Intelligence Analysts are in high demand as organizations increasingly rely on data-driven decision making.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including algorithms, data structures, programming languages, and software engineering. Additionally, students will learn about machine learning and AI techniques, such as clustering and association rules, and how to apply them to real-world problems. This degree path is becoming increasingly popular as the demand for AI and machine learning professionals continues to grow.
2. Master of Science in Artificial Intelligence: This degree path provides students with a deeper understanding of AI and machine learning techniques, such as clustering and association rules. Students will learn how to develop and implement AI and machine learning algorithms, as well as how to evaluate and optimize their performance. This degree path is becoming increasingly popular as AI and machine learning technologies become more widely used in industry.
3. Doctor of Philosophy in Machine Learning: This degree path provides students with an in-depth understanding of machine learning algorithms and techniques, such as clustering and association rules. Students will learn how to develop and implement machine learning algorithms, as well as how to evaluate and optimize their performance. This degree path is becoming increasingly popular as machine learning technologies become more widely used in industry.
4. Master of Science in Data Science: This degree path provides students with a comprehensive understanding of data science fundamentals, including data mining, data visualization, and machine learning. Additionally, students will learn about machine learning and AI techniques, such as clustering and association rules, and how to apply them to real-world problems. This degree path is becoming increasingly popular as the demand for data scientists continues to grow.
Course Syllabus
Introduction
Welcome
What you should know
Using the exercise files
What is unsupervised machine learning?
Course Provider
Provider LinkedIn Learning's Stats at AZClass
Machine Learning and AI Foundations: Clustering and Association introduces the fundamentals of machine learning and artificial intelligence with a focus on unsupervised learning techniques such as clustering, association rules, and anomaly detection. It covers topics such as cluster analysis, anomaly detection, and association rules, which are critical to obtaining accurate and meaningful results from big data. Learners will develop an understanding of the algorithms and techniques used in predictive modeling, as well as the principles of artificial intelligence. They will also learn how to apply these techniques to real-world problems and develop their own machine learning models. Additionally, the course will provide learners with an overview of the field of data science and the tools and techniques used within it.
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