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    • Unsupervised Learning

    Unsupervised Learning Courses Online

    Study unsupervised learning for machine learning. Learn about clustering, dimensionality reduction, and anomaly detection techniques.

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    Explore the Unsupervised Learning Course Catalog

    • G

      Google Cloud

      Preparing for Google Cloud Certification: Machine Learning Engineer

      Skills you'll gain: Feature Engineering, MLOps (Machine Learning Operations), Prompt Engineering, Google Cloud Platform, Generative AI, Tensorflow, Keras (Neural Network Library), Apache Airflow, Cloud Infrastructure, CI/CD, Artificial Intelligence and Machine Learning (AI/ML), Data Pipelines, Dataflow, Systems Design, Cloud Platforms, Data Management, Data Governance, Hybrid Cloud Computing, Workflow Management, Application Deployment

      4.4
      Rating, 4.4 out of 5 stars
      ·
      4.9K reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • I

      IBM

      IBM Deep Learning with PyTorch, Keras and Tensorflow

      Skills you'll gain: PyTorch (Machine Learning Library), Keras (Neural Network Library), Deep Learning, Reinforcement Learning, Unsupervised Learning, Image Analysis, Data Manipulation, Tensorflow, Verification And Validation, Generative AI, Artificial Neural Networks, Data Processing, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Computer Vision, Artificial Intelligence, Scientific Visualization, Time Series Analysis and Forecasting, Predictive Modeling, Natural Language Processing

      4.5
      Rating, 4.5 out of 5 stars
      ·
      3.8K reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • I

      IBM

      Introduction to Artificial Intelligence (AI)

      Skills you'll gain: Large Language Modeling, Artificial Intelligence, Generative AI, Data Ethics, Artificial Intelligence and Machine Learning (AI/ML), ChatGPT, Deep Learning, Machine Learning, Artificial Neural Networks, Prompt Engineering, Governance, Automation, Business Transformation, Applied Machine Learning, Computer Vision, Ethical Standards And Conduct, Natural Language Processing

      4.7
      Rating, 4.7 out of 5 stars
      ·
      19K reviews

      Beginner · Course · 1 - 4 Weeks

    • I

      IBM

      IBM Generative AI Engineering

      Skills you'll gain: Prompt Engineering, Data Wrangling, Large Language Modeling, Unit Testing, Supervised Learning, Feature Engineering, Generative AI, Keras (Neural Network Library), Deep Learning, Artificial Intelligence, ChatGPT, Natural Language Processing, Data Cleansing, Jupyter, Data Analysis, Unsupervised Learning, Data Manipulation, PyTorch (Machine Learning Library), Data Import/Export, Exploratory Data Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      86K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • I

      Imperial College London

      Mathematics for Machine Learning

      Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Probability & Statistics, Feature Engineering, Data Transformation, Jupyter, Data Science, Advanced Mathematics, Statistics, Machine Learning Algorithms, Machine Learning Methods, Statistical Analysis, Artificial Neural Networks, Algorithms, Data Manipulation, Python Programming

      4.6
      Rating, 4.6 out of 5 stars
      ·
      15K reviews

      Beginner · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      AI For Everyone

      Skills you'll gain: Data Ethics, Market Opportunities, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, Team Building, Machine Learning, Strategic Thinking, Data Science, Needs Assessment, Deep Learning, Business Ethics, Artificial Neural Networks, Engineering Management

      4.8
      Rating, 4.8 out of 5 stars
      ·
      48K reviews

      Beginner · Course · 1 - 4 Weeks

    • D

      Duke University

      MLOps | Machine Learning Operations

      Skills you'll gain: MLOps (Machine Learning Operations), Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Data Ethics, Application Deployment, Data Manipulation, Exploratory Data Analysis, Containerization, Data Pipelines, CI/CD, Software Testing, Data Import/Export, Amazon Web Services, Feature Engineering, Cloud Solutions, Artificial Intelligence and Machine Learning (AI/ML), Docker (Software), Rust (Programming Language)

      4.2
      Rating, 4.2 out of 5 stars
      ·
      458 reviews

      Advanced · Specialization · 3 - 6 Months

    • I

      IBM

      IBM AI Developer

      Skills you'll gain: Prompt Engineering, HTML and CSS, Software Development Life Cycle, Software Architecture, Large Language Modeling, Unit Testing, Generative AI, Artificial Intelligence, Software Design, ChatGPT, Jupyter, Scrum (Software Development), Software Engineering, Agile Methodology, Bootstrap (Front-End Framework), Web Design, Application Deployment, Automation, Interviewing Skills, Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      69K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • I

      IBM

      IBM Introduction to Machine Learning

      Skills you'll gain: Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Feature Engineering, Regression Analysis, Dimensionality Reduction, Data Cleansing, Data Access, Data Analysis, Predictive Modeling, Statistical Inference, Statistical Hypothesis Testing, Data Quality, Machine Learning, Data Science, Machine Learning Algorithms, Classification And Regression Tree (CART), Scikit Learn (Machine Learning Library), Statistical Modeling, Applied Machine Learning

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.9K reviews

      Intermediate · Specialization · 3 - 6 Months

    • M

      Meta

      Meta Front-End Developer

      Skills you'll gain: Jest (JavaScript Testing Framework), Version Control, UI/UX Research, Usability Testing, Responsive Web Design, Data Structures, Unix Commands, HTML and CSS, Git (Version Control System), Persona (User Experience), GitHub, Interaction Design, User Experience Design, User Interface and User Experience (UI/UX) Design, Bootstrap (Front-End Framework), React.js, Mobile Development, Front-End Web Development, Algorithms, Web Development Tools

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      22K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • I

      IBM

      IBM Machine Learning

      Skills you'll gain: Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Feature Engineering, Generative AI, Regression Analysis, Dimensionality Reduction, Reinforcement Learning, Data Cleansing, Data Access, Deep Learning, Data Analysis, Applied Machine Learning, Predictive Modeling, Statistical Analysis, Statistical Inference, Statistical Hypothesis Testing, Machine Learning Algorithms, Classification And Regression Tree (CART), Scikit Learn (Machine Learning Library)

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3.1K reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • D

      DeepLearning.AI

      Generative AI for Everyone

      Skills you'll gain: Prompt Engineering, Generative AI, Large Language Modeling, OpenAI, Artificial Intelligence, Business Process Automation, Automation, Ethical Standards And Conduct, Machine Learning, Feasibility Studies, Cloud Applications

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.6K reviews

      Beginner · Course · 1 - 4 Weeks

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    1234…402

    In summary, here are 10 of our most popular unsupervised learning courses

    • Preparing for Google Cloud Certification: Machine Learning Engineer: Google Cloud
    • IBM Deep Learning with PyTorch, Keras and Tensorflow: IBM
    • Introduction to Artificial Intelligence (AI): IBM
    • IBM Generative AI Engineering: IBM
    • Mathematics for Machine Learning: Imperial College London
    • AI For Everyone: DeepLearning.AI
    • MLOps | Machine Learning Operations: Duke University
    • IBM AI Developer: IBM
    • IBM Introduction to Machine Learning: IBM
    • Meta Front-End Developer: Meta

    Skills you can learn in Machine Learning

    Python Programming (33)
    Tensorflow (32)
    Deep Learning (30)
    Artificial Neural Network (24)
    Big Data (18)
    Statistical Classification (17)
    Reinforcement Learning (13)
    Algebra (10)
    Bayesian (10)
    Linear Algebra (10)
    Linear Regression (9)
    Numpy (9)

    Frequently Asked Questions about Unsupervised Learning

    Unsupervised learning is a type of AI-based machine learning that lets people get information from untargeted data sets. The machines find and manage unlabeled data so people are able to take advantage of complex tools, such as dimension reduction algorithms and clustering. The result of unsupervised learning is that users are able to identify patterns and results that emerge from a raw data set. Instead of entering a data set or goal, someone using unsupervised learning gets to let the data sets and patterns emerge to find the algorithm that makes the most sense.‎

    If you enjoy solving complex puzzles and detecting hidden information to answer questions, learning about unsupervised learning puts it within your grasp to identify and present data in a coherent, usable manner. Learning how tools like dimensionality reduction algorithms and clustering let you break raw data into subsets and categories or clusters that can be used to create models. This makes the information usable for decision-making purposes as well as for directing supervised AI to explore specific data subsets. The categorized information becomes more relevant, and the characteristics of data clusters become more recognizable.‎

    Data scientists and machine learning (AI) specialists are two career opportunities that can emerge from picking up the study of unsupervised learning. Before studying unsupervised learning, it helps to have Python programming knowledge and know the basics of calculus, data cleaning, probability, statistics, linear algebra, and exploratory data analysis because unsupervised learning builds upon these skills. You might work exclusively in the field of IBM machine learning strategies, or you may work in another field, such as engineering, customer market segmentation, data mining, or predictive analysis. Studying unsupervised learning helps you stay on top of developments in machine learning to stay relevant as well as advance in your career if you're already in the field.‎

    The online unsupervised learning courses on Coursera give you the knowledge and practical experience about machine learning so you're able to use tools like k-means clustering, cluster analysis, and principle component analysis (PCA) to apply unsupervised machine learning techniques in a business setting. Apply these new skills and newly learned information to real-life business situations. Our online unsupervised learning courses provide the information and opportunity for you to develop a skill set that helps you reach your career goals or earn college credits at your own pace, and on your own terms.‎

    Online Unsupervised Learning courses offer a convenient and flexible way to enhance your knowledge or learn new Unsupervised Learning skills. Choose from a wide range of Unsupervised Learning courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in Unsupervised Learning, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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