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  • Neural Networks

Neural Networks Courses

Neural networks courses can help you learn the basics of architecture design, backpropagation, activation functions, and optimization techniques. You can build skills in training models, tuning hyperparameters, and evaluating performance metrics. Many courses introduce tools like TensorFlow and PyTorch, that support implementing neural networks in practical applications such as image recognition, natural language processing, and predictive analytics.

Popular Neural Networks Courses and Certifications


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  • D

    DeepLearning.AI

    Neural Networks and Deep Learning

    Skills you'll gain: Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Artificial Neural Networks, Convolutional Neural Networks, Applied Machine Learning, Supervised Learning, Artificial Intelligence, Machine Learning Methods, Python Programming, Model Training, Model Optimization

    4.9 stars, 124K reviews, Intermediate, Course, 1 - 4 Weeks

    ★ 4.9 (124K) · Intermediate · Course · 1 - 4 Weeks

    Status: Free trial
    Free trial
  • D

    DeepLearning.AI

    Deep Learning

    Skills you'll gain: Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Computer Vision, Transfer Learning, Deep Learning, Image Analysis, Model Optimization, Artificial Intelligence and Machine Learning (AI/ML), Hugging Face, Natural Language Processing, Artificial Neural Networks, Tensorflow, Applied Machine Learning, Model Training, Fine-tuning, Generative AI, Embeddings, Supervised Learning, Large Language Modeling, Artificial Intelligence

    4.8 stars, 147K reviews, Intermediate, Specialization, 3 - 6 Months

    ★ 4.8 (147K) · Intermediate · Specialization · 3 - 6 Months

    Status: Top AI program
    Top AI program
    Status: Free trial
    Free trial
  • E

    EDUCBA

    Applied Deep Learning and neural networks

    Skills you'll gain: Computer Vision, Deep Learning, Image Analysis, Convolutional Neural Networks, Exploratory Data Analysis, Feature Engineering, Tensorflow, Model Training, Predictive Modeling, Transfer Learning, Applied Machine Learning, Machine Learning Methods, Application Development, Predictive Analytics, Model Evaluation, Machine Learning, Network Model, Analytics, Network Architecture, Design

    Beginner · Specialization · 1 - 3 Months

    Category: New
    New
    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, Transfer Learning, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Model Optimization, Machine Learning Methods, Image Analysis, Applied Machine Learning, Autoencoders, Model Training, Regression Analysis, Network Architecture, Natural Language Processing, Machine Learning

    4.7 stars, 2.1K reviews, Intermediate, Course, 1 - 3 Months

    ★ 4.7 (2.1K) · Intermediate · Course · 1 - 3 Months

    Status: Free trial
    Free trial
  • J

    Johns Hopkins University

    Foundations of Neural Networks

    Skills you'll gain: Responsible AI, Autoencoders, Model Training, Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Data Ethics, Model Optimization, Deep Learning, Artificial Neural Networks, Reinforcement Learning, Generative AI, Generative Adversarial Networks (GANs), Machine Learning Algorithms, Model Deployment, Generative Model Architectures, Debugging, Machine Learning Methods, Artificial Intelligence, Image Analysis, Unsupervised Learning

    4.5 stars, 24 reviews, Intermediate, Specialization, 3 - 6 Months

    ★ 4.5 (24) · Intermediate · Specialization · 3 - 6 Months

    Status: Free trial
    Free trial

What brings you to Coursera today?

  • I

    IBM

    Introduction to Neural Networks and PyTorch

    Skills you'll gain: PyTorch (Machine Learning Library), Applied Machine Learning, Regression Analysis, Tensorflow, Supervised Learning, Deep Learning, Predictive Modeling, Machine Learning, Statistical Methods, Data Processing, Probability & Statistics

    4.4 stars, 1.9K reviews, Intermediate, Course, 1 - 3 Months

    ★ 4.4 (1.9K) · Intermediate · Course · 1 - 3 Months

    Status: Free trial
    Free trial
  • J

    Johns Hopkins University

    Introduction to Neural Networks

    Skills you'll gain: Convolutional Neural Networks, Model Optimization, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Machine Learning Methods, Model Training, Image Analysis, Machine Learning, Computer Vision, Model Evaluation, Algorithms

    4.2 stars, 10 reviews, Intermediate, Course, 1 - 3 Months

    ★ 4.2 (10) · Intermediate · Course · 1 - 3 Months

    Status: Free trial
    Free trial
  • D

    DeepLearning.AI

    Convolutional Neural Networks

    Skills you'll gain: Convolutional Neural Networks, Computer Vision, Image Analysis, Transfer Learning, Deep Learning, Fine-tuning, Artificial Neural Networks, Tensorflow, Data Preprocessing, Embeddings, Model Training, Network Architecture

    4.9 stars, 43K reviews, Intermediate, Course, 1 - 4 Weeks

    ★ 4.9 (43K) · Intermediate · Course · 1 - 4 Weeks

    Status: Free trial
    Free trial
  • D

    Dartmouth College

    Practical Machine Learning: Foundations to Neural Networks

    Skills you'll gain: Supervised Learning, Bayesian Network, Logistic Regression, Artificial Neural Networks, Machine Learning Methods, Statistical Modeling, Predictive Modeling, Model Evaluation, Convolutional Neural Networks, Statistical Machine Learning, Probability & Statistics, Bayesian Statistics, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Machine Learning Algorithms, Statistical Methods, Artificial Intelligence, Regression Analysis, Statistical Inference

    Intermediate · Specialization · 3 - 6 Months

    Status: Free trial
    Free trial
    Category: Build toward a degree
    Build toward a degree
  • E

    EDUCBA

    Deep Learning with Python: CNN, ANN & RNN

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

    4.6 stars, 49 reviews, Beginner, Specialization, 1 - 3 Months

    ★ 4.6 (49) · Beginner · Specialization · 1 - 3 Months

    Status: Free trial
    Free trial
  • P

    Packt

    Deep Learning: Recurrent Neural Networks with Python

    Skills you'll gain: Model Deployment, PyTorch (Machine Learning Library), Model Optimization, Recurrent Neural Networks (RNNs), Tensorflow, Artificial Intelligence, Model Training, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Application Deployment, Text Mining, Artificial Neural Networks, Machine Learning, Natural Language Processing, Deep Learning, Predictive Modeling, Classification Algorithms, Time Series Analysis and Forecasting, Network Architecture, Data Science

    Beginner · Specialization · 1 - 3 Months

    Status: Free trial
    Free trial
  • D

    Dartmouth College

    Machine Learning with Neural Networks

    Skills you'll gain: Bayesian Network, Artificial Neural Networks, Machine Learning Methods, Convolutional Neural Networks, Deep Learning, Tensorflow, Model Training, Model Optimization, Machine Learning, Applied Machine Learning, Bayesian Statistics, Machine Learning Algorithms, Model Evaluation, Network Architecture, Probability Distribution

    Intermediate · Course · 1 - 3 Months

    Status: Free trial
    Free trial
    Category: Build toward a degree
    Build toward a degree

What brings you to Coursera today?

1234…245

Best Neural Networks courses from DeepLearning.AI

Top-rated Neural Networks courses offered by DeepLearning.AI on Coursera.

  1. 1
    Neural Networks and Deep Learning
    DeepLearning.AIIntermediate1 - 4 Weeks4.9(123,813)DeepLearning.AI
  2. 2
    Deep Learning
    DeepLearning.AIIntermediate3 - 6 Months4.8(147,249)DeepLearning.AI
  3. 3
    Convolutional Neural Networks
    DeepLearning.AIIntermediate1 - 4 Weeks4.9(42,604)DeepLearning.AI

Best Neural Networks certificate programs

Earn a certificate in Neural Networks from top universities and companies.

  1. 1
    Applied Deep Learning and neural networks
    EDUCBABeginner1 - 3 Months5.0(1)Specialization
  2. 2
    Foundations of Neural Networks
    Johns Hopkins UniversityIntermediate3 - 6 Months4.5(24)Specialization
  3. 3
    Practical Machine Learning: Foundations to Neural Networks
    Dartmouth CollegeIntermediate3 - 6 Months4.8(4)Specialization

Best Neural Networks courses for beginners

Top-rated beginner-friendly Neural Networks courses with no prerequisites.

  1. 1
    Deep Learning with Python: CNN, ANN & RNN
    EDUCBABeginner1 - 3 Months4.6(49)No prerequisites
  2. 2
    Deep Learning: Recurrent Neural Networks with Python
    PacktBeginner1 - 3 Months4.2(5)No prerequisites

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

A variety of job opportunities exist for those skilled in neural networks. Positions such as machine learning engineer, data scientist, AI researcher, and deep learning engineer are in high demand. These roles often involve developing algorithms, optimizing models, and applying neural networks to solve real-world problems. Additionally, industries like healthcare, finance, and technology are actively seeking professionals who can leverage neural networks to enhance their operations and drive innovation.‎

To effectively learn about neural networks, you should focus on several key skills. A solid understanding of programming languages, particularly Python, is crucial, as it is widely used in machine learning. Familiarity with libraries like TensorFlow and PyTorch will also be beneficial. Additionally, grasping the fundamentals of linear algebra, calculus, and statistics will help you understand how neural networks function. Finally, developing problem-solving skills and a strong analytical mindset will empower you to apply your knowledge effectively.‎

There are numerous online courses available to help you learn about neural networks. Some highly regarded options include the Neural Networks and Deep Learning course, which covers the basics and applications of neural networks, and the Foundations of Neural Networks Specialization, which provides a comprehensive overview of the field. For those interested in specific applications, the Deep Learning: Recurrent Neural Networks with Python Specialization offers targeted training.‎

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

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

To learn neural networks effectively, begin with foundational courses that introduce the core concepts and terminology. Progress to more specialized topics, such as deep learning and specific frameworks like TensorFlow or PyTorch. Engage in hands-on projects to apply your knowledge practically, and consider joining online communities or forums to connect with other learners and professionals. Consistent practice and experimentation will reinforce your understanding and build your confidence.‎

Typically, neural networks courses cover a range of topics, including the architecture of neural networks, activation functions, training algorithms, and optimization techniques. You may also explore advanced subjects like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and techniques for improving model performance. Additionally, courses often include practical applications and case studies to illustrate how neural networks are used in real-world scenarios.‎

For training and upskilling employees in neural networks, courses like the Introduction to Neural Networks and the Deep Learning Frameworks and Neural Networks Simplified are excellent choices. These courses provide foundational knowledge and practical skills that can be directly applied in the workplace. Additionally, specialized courses focusing on specific applications, such as Convolutional Neural Networks, can help employees gain expertise in areas relevant to their roles.‎

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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