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Study deep learning for advanced machine learning. Learn about neural networks, backpropagation, and deep learning frameworks.

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Explore the Deep Learning Course Catalog

  • C

    Coursera Project Network

    Deep Learning with PyTorch : Image Segmentation

    Skills you'll gain: PyTorch (Machine Learning Library), Image Analysis, Deep Learning, Computer Vision, Python Programming

    4.4
    Rating, 4.4 out of 5 stars
    ·
    218 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera Project Network

    Deep Learning with PyTorch : Generative Adversarial Network

    Skills you'll gain: PyTorch (Machine Learning Library), Generative Model Architectures, Deep Learning, Image Analysis, Python Programming

    4.5
    Rating, 4.5 out of 5 stars
    ·
    94 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • Status: Free
    Free
    M

    Microsoft

    Build a computer vision app with Azure Cognitive Services

    Skills you'll gain: Application Programming Interface (API), Microsoft Azure, Computer Vision, Artificial Intelligence and Machine Learning (AI/ML), User Accounts, Image Analysis, Artificial Intelligence, Cloud Solutions, Cloud Computing, Software Development

    4.5
    Rating, 4.5 out of 5 stars
    ·
    378 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera Project Network

    Deep Learning with PyTorch : Siamese Network

    Skills you'll gain: PyTorch (Machine Learning Library), Artificial Neural Networks, Image Analysis, Deep Learning, Computer Vision

    4.6
    Rating, 4.6 out of 5 stars
    ·
    48 reviews

    Advanced · Guided Project · Less Than 2 Hours

  • C

    Coursera Project Network

    Deep Learning with PyTorch : Object Localization

    Skills you'll gain: PyTorch (Machine Learning Library), Image Analysis, Computer Vision, Applied Machine Learning, Deep Learning

    4.6
    Rating, 4.6 out of 5 stars
    ·
    47 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera Project Network

    Facial Expression Recognition with PyTorch

    Skills you'll gain: PyTorch (Machine Learning Library), Deep Learning, Image Analysis, Computer Vision, Artificial Neural Networks, Feature Engineering, Data Validation

    3.9
    Rating, 3.9 out of 5 stars
    ·
    59 reviews

    Beginner · Guided Project · Less Than 2 Hours

What brings you to Coursera today?

  • C

    Coursera Project Network

    Tweet Emotion Recognition with TensorFlow

    Skills you'll gain: Tensorflow, Python Programming, Natural Language Processing, Artificial Neural Networks, Text Mining, Machine Learning Algorithms, Deep Learning, Machine Learning, Data Processing

    4.5
    Rating, 4.5 out of 5 stars
    ·
    169 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera Project Network

    Preparation for Job Interviews

    Skills you'll gain: Interviewing Skills, Recruitment, Oral Expression, Follow Through, Communication, Business Writing

    4.3
    Rating, 4.3 out of 5 stars
    ·
    884 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera Project Network

    Machine Learning Pipelines with Azure ML Studio

    Skills you'll gain: Applied Machine Learning, Classification And Regression Tree (CART), Predictive Modeling, Microsoft Azure, No-Code Development, Machine Learning, Feature Engineering, Data Pipelines, Data Science, Data Analysis, Data Processing, Application Deployment

    4.5
    Rating, 4.5 out of 5 stars
    ·
    814 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera Project Network

    Create Your First Python Program From UST

    Skills you'll gain: Python Programming, Command-Line Interface, Programming Principles, Computer Programming, Scripting, Scripting Languages, Development Environment

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3K reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera Project Network

    Creating Multi Task Models With Keras

    Skills you'll gain: Keras (Neural Network Library), Tensorflow, Applied Machine Learning, Deep Learning, Machine Learning, Computer Vision

    4.7
    Rating, 4.7 out of 5 stars
    ·
    78 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera Project Network

    Sentiment Analysis with Deep Learning using BERT

    Skills you'll gain: Text Mining, Data Cleansing, PyTorch (Machine Learning Library), Data Manipulation, Data Processing, Performance Tuning, Deep Learning, Natural Language Processing, Machine Learning

    4.4
    Rating, 4.4 out of 5 stars
    ·
    397 reviews

    Intermediate · Guided Project · Less Than 2 Hours

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

  • Deep Learning with PyTorch : Image Segmentation : Coursera Project Network
  • Deep Learning with PyTorch : Generative Adversarial Network: Coursera Project Network
  • Build a computer vision app with Azure Cognitive Services: Microsoft
  • Deep Learning with PyTorch : Siamese Network: Coursera Project Network
  • Deep Learning with PyTorch : Object Localization: Coursera Project Network
  • Facial Expression Recognition with PyTorch : Coursera Project Network
  • Tweet Emotion Recognition with TensorFlow: Coursera Project Network
  • Preparation for Job Interviews: Coursera Project Network
  • Machine Learning Pipelines with Azure ML Studio: Coursera Project Network
  • Create Your First Python Program From UST: Coursera Project Network

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 Deep Learning

Deep learning is a powerful application of machine learning (ML) algorithms modeled after biological systems of information processing called artificial neural networks (ANN). Machine learning is an artificial intelligence (AI) technique that allows computers to automatically learn from data without explicit programming, and deep learning harnesses multiple layers of interconnected neural networks to generate more sophisticated insights.

While this field of computer science is quite new, it is already being used in a growing range of important applications. Deep learning excels at automated image recognition, also known as computer vision, which is used for creating accurate facial recognition systems and safely driving autonomous vehicles. This approach is also used for speech recognition and natural language processing (NLP) applications, which allow for computers to interact with human users via voice commands.

Machine learning algorithms such as logistic regression are key to creating deep learning applications, along with commonly used programming languages such as Tensorflow and Python. These programming languages are generally preferred for teaching and learning in this field due to their flexibility and relative accessibility - an important priority given the relevance of deep learning to a wide range of professionals without a computer science background.‎

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

  • Preview the first module of many deep learning courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  • 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 deep learning, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

Certainly - in fact, Coursera is one of the best places to learn about deep learning. Through partnerships with deeplearning.ai and Stanford University, Coursera offers courses as well as Specializations taught by some of the pioneering thinkers and educators in this field. You can also learn via courses and Specializations from industry leaders such as Google Cloud and Intel, or get a professional certificate from IBM. Guided Projects also offer an opportunity to build skills in deep learning through hands-on tutorials led by experienced instructors, allowing you to learn with confidence.‎

The skills or experience you may need to have before studying deep learning, and which can help you better understand an advanced concept such as deep learning, can include sign language reading, music generation, and natural language processing (NLP), in addition to many others. If you have knowledge of Python 3 and understand the basic concepts of general machine-learning algorithms and deep learning, you may have the necessary skills to learn this specialization. You may also want to know about probability and statistics to study deep learning concepts. Basic math, such as algebra and calculus, is also an important prerequisite to deep learning because it relates to machine learning and data science. Also, if you have worked in the tech or artificial intelligence (AI) fields, you may have the necessary experience to study deep learning.‎

The type of person who is best suited to study deep learning is someone comfortable working with statistics, programming, advanced calculus, advanced algebra, and engineering. Deep learning benefits someone passionate about working in the AI fields which can create types of deep learning networks that help machines perform human functions. A person best suited to learn about deep learning has a vested interest in understanding how the intelligence is built to run everything from driverless cars, mobile devices, stock trading systems, and robotic surgery equipment, for example. Deep learning benefits someone with a goal of working with systems such as computer vision, speech recognition, NLP, audio recognition bioinformatics systems, and medical image analysis.‎

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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