Introduction to Neural Networks

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Anybody interested in learning artificial neural networks and understanding perceptron concepts can learn from this Neural Networks course.
 

Enrolling in the Introduction to Neural Network course is a 2-step process. You first need to pick the course you are interested in learning, enter your E-mail ID and set a password. You can dive into the modules and start learning them online.
 

Although Artificial Neural Networks is half an hour-long course. You can learn it at your leisure since the course is self-paced. 

Yes. Once you enroll in this Artificial Neural Networks course, you will have free lifetime access. 
 

After completing this course, you can either learn the machine learning and deep learning concepts individually or register for the Artificial Intelligence Degree Program and master essential concepts and gain skills under a single roof. 

Many Artificial Intelligence and Deep Learning techniques are based on neural networks, often known as Artificial Neural Networks (ANN). Deep learning uses neural networks to simulate the activity of the layers of neuron cells in the neocortex region of the brain. While artificial neural networks may include hundreds of hidden layers to help solve problems and produce outputs, regular neural networks may just have a handful. Artificial neural networks give computers the time and space required to tackle more complex problems and provide sophisticated answers.
 

The universal approximation theorem is the mathematical foundation for neural networks' superior classification abilities, which states that on a small subset, an artificial neural network may roughly estimate any continuous real-valued function. The quantity of neurons affects how accurate the estimate is. Because of its grip in terms of accuracy when taught with large volumes of data, deep learning fields are becoming increasingly popular. The software sector is evolving toward artificial intelligence, and every industry now relies on machine learning to give machines intelligence. 
 

Every artificial intelligence, machine learning, and deep learning professional must be proficient in working with neural networks. The prevalent careers for the subject include:

• Machine Learning Engineer
• Data Engineer
• Research Analyst
• Neuroinformatics.
• Bioinformatician.
• Image Recognition
• Software Engineer
• Software Developer
• Designer in Human-Centered Machine Learning
• Data Scientist
• Computational Linguist
   

Yes. The course includes various modules for different topics in neural networks and perceptrons. Qualify in the quiz after gaining knowledge from the course to gain a course completion certificate.