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    • Neural Networks

    Neural Networks Courses Online

    Study neural networks for deep learning applications. Learn about network architectures, training algorithms, and model evaluation.

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    Explore the Neural Networks Course Catalog

    • J

      Johns Hopkins University

      Mastering Neural Networks and Model Regularization

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

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Diabetes Disease Detection with XG-Boost and Neural Networks

      Skills you'll gain: Artificial Neural Networks, Exploratory Data Analysis, Applied Machine Learning, Predictive Modeling, Classification And Regression Tree (CART), Data Analysis, Predictive Analytics, Machine Learning Algorithms, Machine Learning, Statistical Analysis, Feature Engineering

      4.9
      Rating, 4.9 out of 5 stars
      ·
      8 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: Free
      Free
      N

      National Taiwan University

      機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations

      Skills you'll gain: Supervised Learning, Machine Learning, Classification And Regression Tree (CART), Theoretical Computer Science, Applied Mathematics, Mathematical Modeling, Probability & Statistics, Regression Analysis, Algorithms

      4.9
      Rating, 4.9 out of 5 stars
      ·
      931 reviews

      Beginner · Course · 1 - 3 Months

    • D

      DeepLearning.AI

      Generative Deep Learning with TensorFlow

      Skills you'll gain: Generative AI, Tensorflow, Image Analysis, Deep Learning, Keras (Neural Network Library), Artificial Neural Networks, Computer Graphics, Unsupervised Learning

      4.9
      Rating, 4.9 out of 5 stars
      ·
      305 reviews

      Intermediate · Course · 1 - 4 Weeks

    • I

      IBM

      IBM Cybersecurity Analyst

      Skills you'll gain: Cyber Threat Hunting, Incident Response, IT Service Management, Identity and Access Management, Cloud Computing, Information Technology Infrastructure Library, Endpoint Security, Malware Protection, Penetration Testing, Linux, Computer Security Incident Management, Database Management, Network Security, Networking Hardware, TCP/IP, Cybersecurity, Intrusion Detection and Prevention, Cyber Threat Intelligence, Interviewing Skills, Cyber Security Assessment

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      26K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • D

      DeepLearning.AI

      Generative Adversarial Networks (GANs)

      Skills you'll gain: Generative AI, PyTorch (Machine Learning Library), Image Analysis, Deep Learning, Artificial Neural Networks, Data Ethics, Applied Machine Learning, Computer Vision, Machine Learning, Unsupervised Learning, Artificial Intelligence, Information Privacy, Performance Testing, Machine Learning Algorithms

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.3K reviews

      Intermediate · Specialization · 1 - 3 Months

    • Status: AI skills
      AI skills
      M

      Microsoft

      Microsoft Cybersecurity Analyst

      Skills you'll gain: Threat Modeling, MITRE ATT&CK Framework, Penetration Testing, Azure Active Directory, Network Security, Computer Systems, Security Information and Event Management (SIEM), Security Testing, Encryption, Cybersecurity, System Testing, Cloud Security, Threat Management, Vulnerability Scanning, Cloud Computing, Business Software, Cyber Attacks, Authentications, Active Directory, Data Management

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.5K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: New
      New
      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, Image Analysis, Data Manipulation, Tensorflow, Verification And Validation, Generative AI, Artificial Neural Networks, Data Processing, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Computer Vision, Artificial Intelligence, Scientific Visualization, Time Series Analysis and Forecasting, Predictive Modeling, Natural Language Processing

      4.5
      Rating, 4.5 out of 5 stars
      ·
      3.7K reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • D

      DeepLearning.AI

      AI for Medicine

      Skills you'll gain: Deep Learning, Statistical Analysis, Clinical Trials, Feature Engineering, Risk Modeling, Treatment Planning, Data Analysis, Precision Medicine, Decision Tree Learning, Predictive Modeling, Patient Treatment, Image Analysis, Machine Learning Methods, Applied Machine Learning, AI Personalization, Machine Learning, Random Forest Algorithm, Artificial Intelligence and Machine Learning (AI/ML), Data Processing, Medical Imaging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.4K reviews

      Intermediate · Specialization · 1 - 3 Months

    • P

      Palo Alto Networks

      Palo Alto Networks Cybersecurity

      Skills you'll gain: Network Security, Cloud Security, DevSecOps, Cybersecurity, Cloud-Native Computing, General Networking, TCP/IP, Zero Trust Network Access, Cryptography, Endpoint Security, Cyber Threat Intelligence, Cloud Computing Architecture, Malware Protection, Firewall, Cloud Computing, Encryption, Network Infrastructure, Security Information and Event Management (SIEM), Application Security, Infrastructure Security

      4.6
      Rating, 4.6 out of 5 stars
      ·
      769 reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • I

      IBM

      IT Fundamentals for Cybersecurity

      Skills you'll gain: IT Service Management, Cloud Computing, Information Technology Infrastructure Library, Malware Protection, Identity and Access Management, Linux, Incident Response, Networking Hardware, TCP/IP, Cybersecurity, Linux Administration, Network Security, Network Protocols, Computer Security Incident Management, Cloud Security, Operating Systems, Endpoint Detection and Response, Cyber Risk, Data Ethics, Cyber Governance

      4.6
      Rating, 4.6 out of 5 stars
      ·
      22K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: New AI skills
      New AI skills
      G

      Google

      Google Cybersecurity

      Skills you'll gain: Threat Modeling, Network Security, Threat Management, Incident Response, Vulnerability Management, Computer Security Incident Management, Hardening, Intrusion Detection and Prevention, Cyber Threat Intelligence, Cyber Attacks, Cybersecurity, Network Protocols, Cloud Security, Vulnerability Assessments, Network Architecture, Bash (Scripting Language), Operating Systems, Security Controls, Debugging, Python Programming

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      50K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    Neural Networks learners also search

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    1234…151

    In summary, here are 10 of our most popular neural networks courses

    • Mastering Neural Networks and Model Regularization: Johns Hopkins University
    • Diabetes Disease Detection with XG-Boost and Neural Networks: Coursera Project Network
    • 機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations: National Taiwan University
    • Generative Deep Learning with TensorFlow: DeepLearning.AI
    • IBM Cybersecurity Analyst: IBM
    • Generative Adversarial Networks (GANs): DeepLearning.AI
    • Microsoft Cybersecurity Analyst: Microsoft
    • IBM Deep Learning with PyTorch, Keras and Tensorflow: IBM
    • AI for Medicine: DeepLearning.AI
    • Palo Alto Networks Cybersecurity: Palo Alto Networks

    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 Neural Networks

    Neural networks, also known as neural nets or artificial neural networks (ANN), are machine learning algorithms organized in networks that mimic the functioning of neurons in the human brain. Using this biological neuron model, these systems are capable of unsupervised learning from massive datasets.

    This is an important enabler for artificial intelligence (AI) applications, which are used across a growing range of tasks including image recognition, natural language processing (NLP), and medical diagnosis. The related field of deep learning also relies on neural networks, typically using a convolutional neural network (CNN) architecture that connects multiple layers of neural networks in order to enable more sophisticated applications.

    For example, using deep learning, a facial recognition system can be created without specifying features such as eye and hair color; instead, the program can simply be fed thousands of images of faces and it will learn what to look for to identify different individuals over time, in much the same way that humans learn. Regardless of the end-use application, neural networks are typically created in TensorFlow and/or with Python programming skills.‎

    Neural networks are a fundamental concept to understand for jobs in artificial intelligence (AI) and deep learning. And, as the number of industries seeking to leverage these approaches continues to grow, so do career opportunities for professionals with expertise in neural networks. For instance, these skills could lead to jobs in healthcare creating tools to automate X-ray scans or assist in drug discovery, or a job in the automotive industry developing autonomous vehicles.

    Professionals dedicating their careers to cutting-edge work in neural networks typically pursue a master’s degree or even a doctorate in computer science. This high-level expertise in neural networks and artificial intelligence are in high demand; according to the Bureau of Labor Statistics, computer research scientists earn a median annual salary of $122,840 per year, and these jobs are projected to grow much faster than average over the next decade.‎

    Absolutely - in fact, Coursera is one of the best places to learn about neural networks, online or otherwise. You can take courses and Specializations spanning multiple courses in topics like neural networks, artificial intelligence, and deep learning from pioneers in the field - including deeplearning.ai and Stanford University. Coursera has also partnered with industry leaders such as IBM, Google Cloud, and Amazon Web Services to offer courses that can lead to professional certificates in applied AI and other areas. You can even learn about neural networks with hands-on Guided Projects, a way to learn on Coursera by completing step-by-step tutorials led by experienced instructors.‎

    Before starting to learn neural networks, it's important to have experience creating and using algorithms since neural networks run on complicated algorithms. You should also have fundamental math skills at least, but you'll be at a better advantage if you have knowledge of linear algebra, calculus, statistics, and probability. Being proficient at problem-solving is also important before starting to learn neural networks. An understanding of how the human brain processes information is helpful since artificial neural networks are patterned after how the brain works. You'll also benefit from having experience using any programming language, in particular Java, R, Python, or C++. This includes experience using these languages' libraries, which you'll access to apply the algorithms used in neural networks.‎

    People who are best suited for roles in neural networks are innovative, interested in technology, and have the ability to identify patterns in large amounts of data and draw conclusions from them. People who have a desire to make life and work easier for human beings through artificial technology are well suited for roles in neural networks too. Also, people who have good programming skills and data engineering skills like SQL, data analysis, ETL, and data visualization are likely well suited for roles in neural networks.‎

    If you are interested in the field of artificial intelligence, learning about neural networks is right for you. If your current or future position involves data analysis, pattern recognition, optimization, forecasting, or decision-making, you might also benefit from learning neural networks. Neural networks are also used in image recognition software, speech synthesis, self-driving vehicles, navigation systems, industrial robots, and algorithms for protecting information systems, so if you're interested in these technologies, learning neural networks may be helpful to you.‎

    Online Neural Networks courses offer a convenient and flexible way to enhance your knowledge or learn new Neural Networks skills. Choose from a wide range of Neural Networks courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in Neural Networks, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    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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