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  • Neural Networks

Results for "neural networks"


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    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, 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, Classification Algorithms, Statistical Inference

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    Intermediate · Specialization · 3 - 6 Months

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    DeepLearning.AI

    Neural Networks and Deep Learning

    Skills you'll gain: Deep Learning, Artificial Neural Networks, Convolutional Neural Networks, Applied Machine Learning, Supervised Learning, Recurrent Neural Networks (RNNs), Python Programming, Linear Algebra, Calculus

    4.9
    Rating, 4.9 out of 5 stars
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    124K reviews

    Intermediate · Course · 1 - 4 Weeks

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    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, Image Analysis, Classification And Regression Tree (CART), Regression Analysis, Network Architecture, Natural Language Processing, Machine Learning, Model Evaluation

    4.7
    Rating, 4.7 out of 5 stars
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    2K reviews

    Intermediate · Course · 1 - 3 Months

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    Johns Hopkins University

    Foundations of Neural Networks

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

    4.3
    Rating, 4.3 out of 5 stars
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    10 reviews

    Intermediate · Specialization · 3 - 6 Months

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    DeepLearning.AI

    Deep Learning

    Skills you'll gain: Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Computer Vision, Transfer Learning, Deep Learning, Image Analysis, Hugging Face, Natural Language Processing, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Tensorflow, Applied Machine Learning, Embeddings, Supervised Learning, Keras (Neural Network Library), Machine Learning, Debugging, Performance Tuning, PyTorch (Machine Learning Library), Data Preprocessing

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    4.8
    Rating, 4.8 out of 5 stars
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    147K reviews

    Intermediate · Specialization · 3 - 6 Months

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    IBM

    Introduction to Neural Networks and PyTorch

    Skills you'll gain: PyTorch (Machine Learning Library), Logistic Regression, Tensorflow, Artificial Neural Networks, Classification Algorithms, Deep Learning, Predictive Modeling, Probability & Statistics, Machine Learning Methods, Model Evaluation, Data Preprocessing, Regression Analysis

    4.4
    Rating, 4.4 out of 5 stars
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    1.9K reviews

    Intermediate · Course · 1 - 3 Months

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    DeepLearning.AI

    Convolutional Neural Networks

    Skills you'll gain: Convolutional Neural Networks, Computer Vision, Image Analysis, Transfer Learning, Deep Learning, Artificial Neural Networks, Keras (Neural Network Library), Tensorflow, PyTorch (Machine Learning Library), Data Preprocessing

    4.9
    Rating, 4.9 out of 5 stars
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    43K reviews

    Intermediate · Course · 1 - 4 Weeks

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    Johns Hopkins University

    Introduction to Neural Networks

    Skills you'll gain: Convolutional Neural Networks, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Image Analysis, Machine Learning, Computer Vision, Model Evaluation, Supervised Learning, Algorithms, Statistical Methods, Linear Algebra, Probability

    4.3
    Rating, 4.3 out of 5 stars
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    6 reviews

    Intermediate · Course · 1 - 3 Months

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    Packt

    Deep Learning: Recurrent Neural Networks with Python

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

    Beginner · Specialization · 1 - 3 Months

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    IBM

    Deep Learning with PyTorch

    Skills you'll gain: PyTorch (Machine Learning Library), Deep Learning, Convolutional Neural Networks, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Applied Machine Learning, Supervised Learning, Logistic Regression, Classification Algorithms, Model Evaluation

    4.5
    Rating, 4.5 out of 5 stars
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    77 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
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    DeepLearning.AI

    Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

    Skills you'll gain: Tensorflow, Deep Learning, Performance Tuning, Machine Learning Methods, Data Preprocessing, Artificial Neural Networks, Model Evaluation, Verification And Validation

    4.9
    Rating, 4.9 out of 5 stars
    ·
    63K reviews

    Intermediate · Course · 1 - 4 Weeks

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    Packt

    Deep Learning with TensorFlow

    Skills you'll gain: Recurrent Neural Networks (RNNs), Transfer Learning, Tensorflow, Artificial Neural Networks, Embeddings, Keras (Neural Network Library), Deep Learning, Time Series Analysis and Forecasting, Image Analysis, Classification Algorithms, Convolutional Neural Networks, Natural Language Processing, Computer Vision, Forecasting, Supervised Learning, Machine Learning Algorithms, Machine Learning, Predictive Analytics, Model Evaluation, Predictive Modeling

    Intermediate · Specialization · 1 - 3 Months

What brings you to Coursera today?

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

  • Practical Machine Learning: Foundations to Neural Networks: Dartmouth College
  • Neural Networks and Deep Learning: DeepLearning.AI
  • Introduction to Deep Learning & Neural Networks with Keras: IBM
  • Foundations of Neural Networks: Johns Hopkins University
  • Deep Learning: DeepLearning.AI
  • Introduction to Neural Networks and PyTorch: IBM
  • Convolutional Neural Networks: DeepLearning.AI
  • Introduction to Neural Networks: Johns Hopkins University
  • Deep Learning: Recurrent Neural Networks with Python: Packt
  • Deep Learning with PyTorch: 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 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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