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Deep Learning Courses

Deep learning courses can help you learn neural networks, convolutional networks, and recurrent networks, along with their applications in image recognition and natural language processing. You can build skills in model training, hyperparameter tuning, and performance evaluation, which are crucial for developing effective AI solutions. Many courses introduce tools like TensorFlow and PyTorch, allowing you to implement algorithms and optimize models, making your learning experience hands-on and relevant to current industry practices.


Popular Deep Learning Courses and Certifications


  • Status: Free Trial
    Free Trial
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    S

    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Transfer Learning, Machine Learning, Jupyter, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Data Preprocessing

    4.9
    Rating, 4.9 out of 5 stars
    ·
    38K reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    Illinois Tech

    Deep Learning

    Skills you'll gain: Recurrent Neural Networks (RNNs), Deep Learning, Generative AI, Convolutional Neural Networks, Transfer Learning, Image Analysis, Artificial Neural Networks, Keras (Neural Network Library), Generative Adversarial Networks (GANs), Artificial Intelligence and Machine Learning (AI/ML), PyTorch (Machine Learning Library), Network Architecture, Computer Vision, Natural Language Processing, Model Deployment, Model Evaluation

    Build toward a degree

    4.5
    Rating, 4.5 out of 5 stars
    ·
    33 reviews

    Beginner · Course · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
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    EDUCBA

    Deep Learning with Python: CNN, ANN & RNN

    Skills you'll gain: Model Evaluation, Convolutional Neural Networks, Data Preprocessing, Keras (Neural Network Library), Image Analysis, Deep Learning, Tensorflow, Computer Vision, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Data Manipulation, Data Transformation, Financial Forecasting, Data Visualization, Time Series Analysis and Forecasting, Exploratory Data Analysis, Python Programming, Customer Analysis, Applied Machine Learning, Cloud Computing

    4.6
    Rating, 4.6 out of 5 stars
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    37 reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    M

    MathWorks

    Deep Learning for Computer Vision

    Skills you'll gain: Model Evaluation, Computer Vision, Model Deployment, Anomaly Detection, Convolutional Neural Networks, Image Analysis, Transfer Learning, Matlab, Deep Learning, Artificial Neural Networks, Applied Machine Learning, PyTorch (Machine Learning Library), Data Preprocessing, Classification Algorithms, Data Visualization, Data Synthesis, Performance Tuning, Data Analysis

    4.9
    Rating, 4.9 out of 5 stars
    ·
    34 reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
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    IBM

    Introduction to Artificial Intelligence (AI)

    Skills you'll gain: Responsible AI, Generative AI, Natural Language Processing, Robotics, Business Logic, Risk Mitigation

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

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    Imperial College London

    Mathematics for Machine Learning

    Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Data Preprocessing, Unsupervised Learning, Feature Engineering, Machine Learning Algorithms, Jupyter, Advanced Mathematics, Statistics, Artificial Neural Networks, Algorithms, Mathematical Modeling, Python Programming, Derivatives

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

    Beginner · Specialization · 3 - 6 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    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, Data Visualization, NumPy, Machine Learning Algorithms, Keras (Neural Network Library), Pandas (Python Package), Seaborn, Artificial Intelligence and Machine Learning (AI/ML), Data Science, Applied Machine Learning, Tensorflow, Data Analysis, Artificial Intelligence, Machine Learning

    4.3
    Rating, 4.3 out of 5 stars
    ·
    7 reviews

    Beginner · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    M

    MathWorks

    Deep Learning for Object Detection

    Skills you'll gain: Computer Vision, Model Evaluation, Image Analysis, Convolutional Neural Networks, Deep Learning, Matlab, Data Preprocessing, Data Visualization, Transfer Learning, Applied Machine Learning, Data Analysis

    4.9
    Rating, 4.9 out of 5 stars
    ·
    13 reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Preview
    Preview
    D

    DeepLearning.AI

    AI For Everyone

    Skills you'll gain: AI Product Strategy, Responsible AI, Data Ethics, AI Enablement, Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Artificial Neural Networks

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

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    IBM

    IBM Generative AI Engineering

    Skills you'll gain: Prompt Engineering, Exploratory Data Analysis, Prompt Patterns, LangChain, Large Language Modeling, Retrieval-Augmented Generation, Model Evaluation, Unsupervised Learning, Generative Model Architectures, PyTorch (Machine Learning Library), ChatGPT, Generative AI, Restful API, LLM Application, Keras (Neural Network Library), Data Transformation, Supervised Learning, Responsible AI, Vector Databases, Data Import/Export

    4.6
    Rating, 4.6 out of 5 stars
    ·
    98K reviews

    Beginner · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    M

    MathWorks

    Introduction to Deep Learning for Computer Vision

    Skills you'll gain: Model Evaluation, Computer Vision, Convolutional Neural Networks, Image Analysis, Transfer Learning, Matlab, Artificial Neural Networks, Deep Learning, Applied Machine Learning, Classification Algorithms, Performance Tuning

    4.9
    Rating, 4.9 out of 5 stars
    ·
    18 reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    S

    Simplilearn

    AI & Deep Learning Concepts and Applications

    Skills you'll gain: Reinforcement Learning, Artificial Intelligence, Tensorflow, Artificial Neural Networks, Deep Learning, Machine Learning, Applied Machine Learning, Data Ethics, Supervised Learning, Responsible AI, Unsupervised Learning, Natural Language Processing, Computer Programming

    4.2
    Rating, 4.2 out of 5 stars
    ·
    9 reviews

    Beginner · Course · 1 - 4 Weeks

Searches related to deep learning

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1234…273

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

  • Machine Learning: DeepLearning.AI
  • Deep Learning: Illinois Tech
  • Deep Learning with Python: CNN, ANN & RNN: EDUCBA
  • Deep Learning for Computer Vision: MathWorks
  • Introduction to Artificial Intelligence (AI): IBM
  • Mathematics for Machine Learning: Imperial College London
  • Deep Learning with Real-World Projects: Packt
  • Deep Learning for Object Detection: MathWorks
  • AI For Everyone: DeepLearning.AI
  • IBM Generative AI Engineering: IBM

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 subset of machine learning that utilizes neural networks with many layers (hence the term 'deep') to analyze various forms of data. It is important because it enables computers to perform tasks that typically require human intelligence, such as image recognition, natural language processing, and decision-making. As technology continues to evolve, deep learning is becoming increasingly integral in various industries, driving innovations in automation, healthcare, finance, and more.‎

Pursuing a career in deep learning can open doors to various job opportunities. Some common roles include deep learning engineer, data scientist, machine learning engineer, AI researcher, and computer vision engineer. These positions often involve designing and implementing deep learning models, analyzing data, and developing algorithms that can learn from and make predictions based on data.‎

To succeed in deep learning, you should develop a strong foundation in several key skills. These include programming languages such as Python, understanding of machine learning concepts, proficiency in using deep learning frameworks like TensorFlow and PyTorch, and knowledge of mathematics, particularly linear algebra and calculus. Familiarity with data preprocessing and model evaluation techniques is also beneficial.‎

There are numerous online courses available for those interested in deep learning. Some of the best options include the Deep Learning Specialization and the IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate. These courses provide comprehensive training and hands-on experience in deep learning techniques and applications.‎

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

  1. Preview the first module of many deep 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 deep learning, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn deep learning effectively, start by building a strong foundation in programming and mathematics. Enroll in introductory courses to understand the basics of machine learning and neural networks. Gradually progress to more advanced topics and practical applications by working on projects. Engaging with online communities and forums can also provide support and enhance your learning experience.‎

Deep learning courses typically cover a range of topics, including neural network architectures, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing, and reinforcement learning. Additionally, courses may explore practical applications in fields such as computer vision, healthcare, and finance, providing learners with a well-rounded understanding of how deep learning can be applied in real-world scenarios.‎

For training and upskilling employees in deep learning, specialized courses such as the AI ML with Deep Learning and Supervised Models Specialization and the Deep Learning for Healthcare Specialization can be particularly beneficial. These programs focus on practical skills and applications, making them suitable for workforce development.‎

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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