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  • Unsupervised Learning

Results for "unsupervised learning"


  • D

    DeepLearning.AI

    Unsupervised Learning, Recommenders, Reinforcement Learning

    Skills you'll gain: Unsupervised Learning, Applied Machine Learning, Responsible AI, Data Ethics, Machine Learning, Supervised Learning, Artificial Intelligence, Reinforcement Learning, Artificial Neural Networks, Deep Learning, Anomaly Detection, Dimensionality Reduction

    4.9
    Rating, 4.9 out of 5 stars
    ·
    5.7K reviews

    Beginner · Course · 1 - 4 Weeks

  • I

    IBM

    Unsupervised Machine Learning

    Skills you'll gain: Unsupervised Learning, Dimensionality Reduction, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Applied Machine Learning, Data Preprocessing, Text Mining, Machine Learning, Big Data, Model Evaluation, Performance Metric

    Coursera Plus

    Included with Coursera Plus

    4.7
    Rating, 4.7 out of 5 stars
    ·
    368 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    U

    University of Colorado Boulder

    Introduction to Learning

    Skills you'll gain: Model Evaluation, Supervised Learning, Machine Learning Methods, Unsupervised Learning, Machine Learning Algorithms, Machine Learning, Reinforcement Learning, Model Training, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Decision Tree Learning, Artificial Neural Networks, Artificial Intelligence, Algorithms, Data-Driven Decision-Making

    Coursera Plus

    Included with Coursera Plus

    Beginner · Course · 1 - 4 Weeks

  • Status: New
    New
    U

    University of Colorado Boulder

    Introduction to Machine Learning: Unsupervised Learning

    Skills you'll gain: Machine Learning Methods, Feature Engineering, Supervised Learning, Model Evaluation

    Coursera Plus

    Included with Coursera Plus

    5
    Rating, 5 out of 5 stars
    ·
    12 reviews

    Intermediate · Course · 1 - 3 Months

  • D
    S

    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Training, Applied Machine Learning, Machine Learning Algorithms, Transfer Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Model Evaluation, Responsible AI, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning

    4.9
    Rating, 4.9 out of 5 stars
    ·
    39K reviews

    Beginner · Specialization · 1 - 3 Months

  • U

    University of Michigan

    Applied Unsupervised Learning in Python

    Skills you'll gain: Unsupervised Learning, Embeddings, Applied Machine Learning, Data Quality, Unstructured Data, Machine Learning Methods, Anomaly Detection, Data Preprocessing, Data Transformation, Python Programming, Exploratory Data Analysis, Model Evaluation

    Coursera Plus

    Included with Coursera Plus

    4.9
    Rating, 4.9 out of 5 stars
    ·
    7 reviews

    Advanced · Course · 1 - 4 Weeks

  • G

    Google

    The Nuts and Bolts of Machine Learning

    Skills you'll gain: Feature Engineering, Decision Tree Learning, Applied Machine Learning, Supervised Learning, Advanced Analytics, Statistical Machine Learning, Machine Learning, Machine Learning Algorithms, Unsupervised Learning, Analytics, Model Training, Random Forest Algorithm, Model Optimization, Predictive Modeling, Model Evaluation, Python Programming, Performance Tuning, Classification Algorithms

    Coursera Plus

    Included with Coursera Plus

    4.8
    Rating, 4.8 out of 5 stars
    ·
    630 reviews

    Advanced · Course · 1 - 3 Months

  • U

    University of Colorado Boulder

    Trees, SVM and Unsupervised Learning

    Skills you'll gain: Model Evaluation, Applied Machine Learning, Unsupervised Learning, Decision Tree Learning, Artificial Neural Networks, Machine Learning Methods, Classification Algorithms, Supervised Learning, Statistical Machine Learning, Machine Learning Algorithms, Random Forest Algorithm, Predictive Modeling, Applied Mathematics, Dimensionality Reduction, Statistics

    Coursera Plus

    Included with Coursera Plus

    Build toward a degree

    4.4
    Rating, 4.4 out of 5 stars
    ·
    9 reviews

    Intermediate · Course · 1 - 4 Weeks

  • O

    O.P. Jindal Global University

    Unsupervised Learning and Its Applications in Marketing

    Skills you'll gain: Anomaly Detection, Dimensionality Reduction, Unsupervised Learning, Customer Analysis, Marketing Analytics, Data Mining, Customer Insights, Autoencoders, Data-Driven Marketing, Applied Machine Learning, Machine Learning Algorithms, Machine Learning Methods, Marketing, Statistical Machine Learning, Target Audience, Supervised Learning, Python Programming, Algorithms

    Coursera Plus

    Included with Coursera Plus

    Beginner · Course · 1 - 3 Months

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  • P

    Packt

    Deep Learning with Real-World Projects

    Skills you'll gain: Recurrent Neural Networks (RNNs), Artificial Neural Networks, Deep Learning, Matplotlib, Convolutional Neural Networks, Linear Algebra, Image Analysis, Plot (Graphics), Data Visualization, NumPy, Scientific Visualization, Machine Learning Algorithms, Keras (Neural Network Library), Statistical Visualization, Pandas (Python Package), Model Training, Applied Machine Learning, Data Science, Artificial Intelligence, Machine Learning

    Coursera Plus

    Included with Coursera Plus

    4.3
    Rating, 4.3 out of 5 stars
    ·
    7 reviews

    Beginner · Specialization · 3 - 6 Months

  • P

    Packt

    Cluster Analysis and Unsupervised Machine Learning in Python

    Skills you'll gain: Unsupervised Learning, Machine Learning Methods, Machine Learning Algorithms, Applied Machine Learning, Scientific Visualization, Machine Learning, Statistical Machine Learning, Data Mining, Taxonomy, Statistical Methods, Algorithms, Python Programming, Development Environment

    Coursera Plus

    Included with Coursera Plus

    Intermediate · Course · 1 - 3 Months

  • I

    IBM

    Deep Learning and Reinforcement Learning

    Skills you'll gain: Autoencoders, Generative AI, Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Reinforcement Learning, Generative Adversarial Networks (GANs), Generative Model Architectures, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Unsupervised Learning, Machine Learning Methods, Transfer Learning, Model Optimization, Image Analysis, Artificial Neural Networks, Keras (Neural Network Library), Fine-tuning, Machine Learning, Artificial Intelligence, Computer Vision

    Coursera Plus

    Included with Coursera Plus

    4.6
    Rating, 4.6 out of 5 stars
    ·
    300 reviews

    Intermediate · Course · 1 - 3 Months

1234…796

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

  • Unsupervised Learning, Recommenders, Reinforcement Learning: DeepLearning.AI
  • Unsupervised Machine Learning: IBM
  • Introduction to Learning: University of Colorado Boulder
  • Introduction to Machine Learning: Unsupervised Learning: University of Colorado Boulder
  • Machine Learning: DeepLearning.AI
  • Applied Unsupervised Learning in Python: University of Michigan
  • The Nuts and Bolts of Machine Learning: Google
  • Trees, SVM and Unsupervised Learning: University of Colorado Boulder
  • Unsupervised Learning and Its Applications in Marketing: O.P. Jindal Global University
  • Deep Learning with Real-World Projects: Packt

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 machine learning that involves training algorithms on data without labeled outcomes. This approach is crucial because it enables the discovery of hidden patterns and structures within data, allowing for insights that can drive decision-making in various fields. By identifying these patterns, businesses and researchers can make informed predictions, segment data, and enhance their understanding of complex datasets. The importance of unsupervised learning lies in its ability to handle vast amounts of unstructured data, which is increasingly prevalent in today's data-driven world.‎

Careers in unsupervised learning are diverse and can lead to roles such as data scientist, machine learning engineer, and business analyst. These positions often require a strong understanding of data analysis and algorithm development. Additionally, roles in marketing analytics and customer insights leverage unsupervised learning techniques to identify customer segments and improve targeting strategies. As organizations increasingly rely on data to inform their strategies, the demand for professionals skilled in unsupervised learning continues to grow.‎

To effectively learn unsupervised learning, you should focus on developing a solid foundation in statistics, linear algebra, and programming, particularly in Python or R. Familiarity with machine learning concepts and algorithms is essential, as is experience with data manipulation and visualization tools. Understanding clustering techniques, dimensionality reduction, and anomaly detection will also be beneficial. Additionally, gaining practical experience through projects or internships can enhance your skills and make you more competitive in the job market.‎

Some of the best online courses for unsupervised learning include Applied Unsupervised Learning in Python and Unsupervised Machine Learning. These courses provide hands-on experience with algorithms and practical applications, making them ideal for learners looking to deepen their understanding. Other notable options include Cluster Analysis and Unsupervised Machine Learning in Python and Unsupervised Algorithms in Machine Learning, which cover various techniques and their implementations.‎

Yes. You can start learning unsupervised learning on Coursera for free in two ways:

  1. Preview the first module of many unsupervised learning courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in unsupervised learning, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn unsupervised learning, start by selecting a course that aligns with your current knowledge and goals. Engage with the course materials, complete assignments, and participate in discussions to reinforce your understanding. Practice is key, so work on real-world datasets to apply the concepts you've learned. Additionally, consider joining online communities or forums to connect with others in the field, share insights, and seek guidance as you progress.‎

Typical topics covered in unsupervised learning courses include clustering algorithms (like K-means and hierarchical clustering), dimensionality reduction techniques (such as PCA), anomaly detection, and association rule learning. Courses may also explore the applications of these techniques in various domains, including marketing, finance, and healthcare. Understanding the theoretical foundations and practical implementations of these topics is essential for mastering unsupervised learning.‎

For training and upskilling employees in unsupervised learning, courses like Unsupervised Learning and Its Applications in Marketing and Unsupervised Learning, Recommenders, Reinforcement Learning can be particularly beneficial. These courses provide practical insights and applications that can enhance team capabilities in data analysis and decision-making, making them valuable resources for organizations looking to leverage data-driven strategies.‎

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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