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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: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Deep Learning

    Skills you'll gain: Computer Vision, Deep Learning, Image Analysis, Natural Language Processing, Artificial Neural Networks, Tensorflow, Generative AI, Supervised Learning, Large Language Modeling, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, Applied Machine Learning, PyTorch (Machine Learning Library), Machine Learning, MLOps (Machine Learning Operations), Debugging, Performance Tuning, Python Programming, Data-Driven Decision-Making, Machine Learning Algorithms

    Build toward a degree

    4.8
    Rating, 4.8 out of 5 stars
    ·
    147K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Neural Networks and Deep Learning

    Skills you'll gain: Deep Learning, Artificial Neural Networks, Supervised Learning, Artificial Intelligence, Machine Learning, Python Programming, Linear Algebra, Calculus

    4.9
    Rating, 4.9 out of 5 stars
    ·
    123K reviews

    Intermediate · Course · 1 - 4 Weeks

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

    Packt

    Deep Learning with TensorFlow

    Skills you'll gain: Tensorflow, Artificial Neural Networks, Keras (Neural Network Library), Deep Learning, Time Series Analysis and Forecasting, Image Analysis, Natural Language Processing, Computer Vision, Forecasting, Classification And Regression Tree (CART), Supervised Learning, Machine Learning, Text Mining, Predictive Analytics, NumPy, Network Architecture, Data Processing, Data Science

    Intermediate · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    D
    S

    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Responsible AI, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Python Programming

    4.9
    Rating, 4.9 out of 5 stars
    ·
    36K reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    Status: AI skills
    AI skills
    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, Artificial Neural Networks, Machine Learning Methods, Generative AI, Tensorflow, Artificial Intelligence and Machine Learning (AI/ML), Image Analysis, Computer Vision, Artificial Intelligence, Machine Learning, Regression Analysis, Network Architecture, Network Model, Predictive Modeling, Natural Language Processing, Probability & Statistics

    4.5
    Rating, 4.5 out of 5 stars
    ·
    4K reviews

    Intermediate · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    Introduction to Deep Learning & Neural Networks with Keras

    Skills you'll gain: Keras (Neural Network Library), Deep Learning, Artificial Neural Networks, Tensorflow, Machine Learning Methods, Image Analysis, Computer Vision, Regression Analysis, Network Architecture, Network Model, Natural Language Processing, Machine Learning

    4.7
    Rating, 4.7 out of 5 stars
    ·
    2K reviews

    Intermediate · Course · 1 - 3 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    I

    IBM

    Deep Learning with PyTorch

    Skills you'll gain: PyTorch (Machine Learning Library), Deep Learning, Artificial Neural Networks, Computer Vision, Supervised Learning, Machine Learning, Network Architecture

    4.4
    Rating, 4.4 out of 5 stars
    ·
    68 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    IBM AI Engineering

    Skills you'll gain: Prompt Engineering, Large Language Modeling, PyTorch (Machine Learning Library), Unsupervised Learning, Generative AI, Supervised Learning, Keras (Neural Network Library), Feature Engineering, Reinforcement Learning, LLM Application, Deep Learning, Generative AI Agents, Applied Machine Learning, Natural Language Processing, Tensorflow, Artificial Neural Networks, Machine Learning Methods, Machine Learning, Python Programming, Data Science

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    21K reviews

    Intermediate · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    Imperial College London

    TensorFlow 2 for Deep Learning

    Skills you'll gain: Tensorflow, Generative Model Architectures, Data Pipelines, Keras (Neural Network Library), Deep Learning, Image Analysis, Computer Programming, Program Development, Data Validation, Applied Machine Learning, Bayesian Statistics, Supervised Learning, Natural Language Processing, Data Processing, Predictive Modeling, Computer Vision, Machine Learning Methods, Artificial Neural Networks, Machine Learning, Unsupervised Learning

    4.8
    Rating, 4.8 out of 5 stars
    ·
    715 reviews

    Intermediate · Specialization · 3 - 6 Months

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

    Pearson

    Learning Deep Learning

    Skills you'll gain: Large Language Modeling, Deep Learning, Prompt Engineering, Image Analysis, PyTorch (Machine Learning Library), Tensorflow, LLM Application, Computer Vision, Responsible AI, Natural Language Processing, Generative AI, Artificial Neural Networks, Data Ethics, Multimodal Prompts, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning Methods, Artificial Intelligence, Application Deployment, Time Series Analysis and Forecasting

    Intermediate · Specialization · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    Illinois Tech

    Deep Learning

    Skills you'll gain: Deep Learning, Generative AI, Image Analysis, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), PyTorch (Machine Learning Library), Network Architecture, Tensorflow, Computer Vision, Natural Language Processing, Machine Learning, Performance Tuning

    Build toward a degree

    4.5
    Rating, 4.5 out of 5 stars
    ·
    24 reviews

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Mathematics for Machine Learning and Data Science

    Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Linear Algebra, Statistical Inference, A/B Testing, Statistical Analysis, Applied Mathematics, NumPy, Probability, Calculus, Dimensionality Reduction, Numerical Analysis, Mathematical Modeling, Machine Learning, Machine Learning Methods, Jupyter

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

    Intermediate · Specialization · 1 - 3 Months

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

  • Deep Learning: DeepLearning.AI
  • Neural Networks and Deep Learning: DeepLearning.AI
  • Deep Learning with TensorFlow: Packt
  • Machine Learning: DeepLearning.AI
  • IBM Deep Learning with PyTorch, Keras and Tensorflow: IBM
  • Introduction to Deep Learning & Neural Networks with Keras: IBM
  • Deep Learning with PyTorch: IBM
  • IBM AI Engineering: IBM
  • TensorFlow 2 for Deep Learning: Imperial College London
  • Learning Deep Learning: Pearson

Skills you can learn in Machine Learning

Python Programming (33)
Tensorflow (32)
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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