Unsupervised Machine Learning Course
Free Unsupervised Machine Learning Course Online
About this Free Unsupervised Machine Learning Course
Welcome to our “Free Unsupervised Machine Learning Certification Course Online”. This course is designed to make the complex world of unsupervised learning accessible to beginners.
What you’ll learn
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Understanding Unsupervised Learning: This course will provide a deep understanding of unsupervised learning concepts and techniques such as clustering, dimensionality reduction, and association rules.
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Proficiency in Python: You'll gain hands-on coding skills in Python, particularly focusing on the usage of libraries like Scikit-Learn, Pandas, and NumPy, which are pivotal in the data science and machine learning landscape.
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Model Development and Evaluation: Learn how to design, train, and validate unsupervised learning models, as well as understand the unique challenges in evaluating these types of models.
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Real-world Applications: By working on practical projects, you'll acquire the ability to apply unsupervised learning techniques to solve a variety of real-world problems.
Course Content
Certificate for Free Unsupervised Machine Learning Course
Instructor of this course
- Co-Founder & Principal Instructor, Applied AI & AppliedRoots
- Senior ML Scientist @ Amazon, Palo Alto and Bangalore
- Co-Founder, Matherix Labs
- Research Engineer, Yahoo! Labs
- Masters from IISc Bangalore, Gate 2007(AIR 2)
- 13 years of experience in AI and Machine Learning
Pre-requisites for free Unsupervised Learning certification course
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Basic Programming Knowledge: A foundational understanding of any programming language is recommended, with Python being particularly useful due to its prominence in data science and machine learning.
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Understanding of Mathematics: A basic knowledge of mathematics, especially areas like algebra, statistics, and probability, is beneficial since many machine learning concepts are grounded in these disciplines.
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Familiarity with Data Structures: A rudimentary understanding of data structures such as arrays, lists, and dictionaries is advantageous, as they are commonly used in coding machine learning algorithms.
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Curiosity and Eagerness to Learn: The primary prerequisite for this course is a willingness to learn and explore new concepts. Even without a solid background in the above areas, the course is designed to be understandable and informative for all.
Who should learn this free Unsupervised Learning course?
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Aspiring Data Scientists: Individuals aiming to build a career in data science will find this course beneficial, as unsupervised learning is a key technique used in the field.
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Software Engineers: Software engineers interested in expanding their skillset to include machine learning and AI can take advantage of this course.
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Data Analysts and Statisticians: Professionals who work with data and aim to augment their analytical capabilities with machine learning techniques can greatly benefit from this course.
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Curious Learners: Anyone interested in understanding machine learning, AI, and data science, regardless of their career or educational background, can join this course to gain an introduction to one of the most significant areas of these fields.