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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

  • Status: New
    New
    Status: Free Trial
    Free Trial
    P

    Packt

    Advanced Machine Learning, Big Data, and Deep Learning

    Skills you'll gain: Apache Spark, Keras (Neural Network Library), Deep Learning, Tensorflow, A/B Testing, Big Data, Data Ethics, Applied Machine Learning, Data Processing, Machine Learning Software, Artificial Neural Networks, Machine Learning Algorithms, Data Cleansing, Machine Learning, MLOps (Machine Learning Operations), Artificial Intelligence, Supervised Learning, Statistical Hypothesis Testing, Dimensionality Reduction, Reinforcement Learning

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    D

    Duke University

    MLOps | Machine Learning Operations

    Skills you'll gain: MLOps (Machine Learning Operations), Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Application Deployment, Responsible AI, Data Manipulation, Exploratory Data Analysis, Containerization, Data Pipelines, CI/CD, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis

    4.2
    Rating, 4.2 out of 5 stars
    ·
    518 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of Illinois Urbana-Champaign

    Deep Learning for Healthcare

    Skills you'll gain: Deep Learning, Health Informatics, Image Analysis, Generative Model Architectures, Machine Learning, Applied Machine Learning, Health Care, Artificial Neural Networks, Supervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Graph Theory, Computer Vision, Tensorflow, PyTorch (Machine Learning Library), Predictive Modeling, Medical Science and Research, Unsupervised Learning, Program Development, Big Data

    3.3
    Rating, 3.3 out of 5 stars
    ·
    48 reviews

    Advanced · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    AI Capstone Project with Deep Learning

    Skills you'll gain: Deep Learning, Keras (Neural Network Library), PyTorch (Machine Learning Library), Computer Vision, Machine Learning Methods, Machine Learning, Artificial Intelligence, Python Programming

    4.5
    Rating, 4.5 out of 5 stars
    ·
    670 reviews

    Advanced · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    G

    Google

    Google Advanced Data Analytics

    Skills you'll gain: Data Storytelling, Data Visualization, Data Ethics, Exploratory Data Analysis, Sampling (Statistics), Data Presentation, Data Visualization Software, Feature Engineering, Regression Analysis, Descriptive Statistics, Statistical Hypothesis Testing, Advanced Analytics, Data Analysis, Tableau Software, Data Science, Statistical Analysis, Machine Learning, Object Oriented Programming (OOP), Interviewing Skills, Python Programming

    Build toward a degree

    4.7
    Rating, 4.7 out of 5 stars
    ·
    9.1K reviews

    Advanced · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    G

    Google

    Google IT Automation with Python

    Skills you'll gain: Git (Version Control System), GitHub, Version Control, Cloud Services, Debugging, Puppet (Configuration Management Tool), Infrastructure as Code (IaC), Bash (Scripting Language), CI/CD, Cloud Computing, Technical Communication, Unit Testing, Web Services, Email Automation, Configuration Management, Automation, Python Programming, Interviewing Skills, Professional Development, Scripting

    4.8
    Rating, 4.8 out of 5 stars
    ·
    51K reviews

    Advanced · Professional Certificate · 3 - 6 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    I

    IBM

    IBM AI Enterprise Workflow

    Skills you'll gain: Feature Engineering, Data Ethics, Exploratory Data Analysis, Unsupervised Learning, Data Presentation, Tensorflow, Application Deployment, Dimensionality Reduction, MLOps (Machine Learning Operations), Probability Distribution, Apache Spark, Statistical Hypothesis Testing, Supervised Learning, Data Visualization Software, Data Pipelines, Design Thinking, Unit Testing, Data Science, Machine Learning, Python Programming

    4.4
    Rating, 4.4 out of 5 stars
    ·
    348 reviews

    Advanced · Specialization · 3 - 6 Months

  • 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

  • Status: Free Trial
    Free Trial
    U

    University of Illinois Urbana-Champaign

    Deep Learning Methods for Healthcare

    Skills you'll gain: Deep Learning, Image Analysis, Applied Machine Learning, Artificial Neural Networks, Computer Vision, Predictive Modeling, Health Informatics, Machine Learning, Unsupervised Learning, Feature Engineering, Medical Imaging, Python Programming, Dimensionality Reduction, Network Architecture

    3.9
    Rating, 3.9 out of 5 stars
    ·
    15 reviews

    Advanced · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    P

    Packt

    Advanced Microservices Architecture, Deployment & Resilience

    Skills you'll gain: Microservices, CI/CD, Continuous Deployment, Containerization, Serverless Computing, Continuous Integration, Docker (Software), Kubernetes, Event-Driven Programming, Transaction Processing, Redis, Software Architecture, Distributed Computing, Scalability, Amazon Web Services, System Monitoring, Software Design Patterns, Data Integrity, Real Time Data

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Illinois Urbana-Champaign

    Advanced Deep Learning Methods for Healthcare

    Skills you'll gain: Deep Learning, Generative Model Architectures, Applied Machine Learning, Machine Learning Methods, Image Analysis, Graph Theory, Tensorflow, Artificial Neural Networks, PyTorch (Machine Learning Library), Health Informatics, Predictive Modeling, Unsupervised Learning, Natural Language Processing, Data Synthesis, Python Programming

    3.5
    Rating, 3.5 out of 5 stars
    ·
    14 reviews

    Advanced · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    Imperial College London

    Probabilistic Deep Learning with TensorFlow 2

    Skills you'll gain: Tensorflow, Generative Model Architectures, Deep Learning, Image Analysis, Bayesian Statistics, Artificial Neural Networks, Machine Learning, Unsupervised Learning, Probability & Statistics, Dimensionality Reduction

    4.7
    Rating, 4.7 out of 5 stars
    ·
    109 reviews

    Advanced · Course · 1 - 3 Months

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

  • Advanced Machine Learning, Big Data, and Deep Learning: Packt
  • MLOps | Machine Learning Operations: Duke University
  • Deep Learning for Healthcare: University of Illinois Urbana-Champaign
  • AI Capstone Project with Deep Learning : IBM
  • Google Advanced Data Analytics: Google
  • Google IT Automation with Python: Google
  • IBM AI Enterprise Workflow: IBM
  • Deep Learning with PyTorch : Siamese Network: Coursera Project Network
  • Deep Learning Methods for Healthcare: University of Illinois Urbana-Champaign
  • Advanced Microservices Architecture, Deployment & Resilience: 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 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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