Machine Learning Essentials and Backpropagation Algorithm

Have you been wondering what a machine learning system can do? Chatbots, predictive text, apps for language translation, Netflix's recommendations, and social media feeds are all machine learning products. The course will illustrate the components of artificial neural networks with the steps for performing the perceptron learning algorithm and acquaint you with the elements of a perceptron, like bias, weights and activation functions. We explain how the perceptrons solve linearly separable problems.

The course then explains sigmoidal activation function and backpropagation in multilayer neural networks. We lay out the terminating conditions for multilayer networks and show you how to calculate weights for hidden nodes. We discuss how backpropagation works and how to update weights in backpropagation. We identify the techniques for building the different types of neural networks and the methods for unsupervised learning. We cover clustering with approaches like partitional clustering before explaining similarity and dissimilarity.

Finally, the course will analyze the K-means clustering algorithm to solve clustering problems. We show examples of instance-based learning and describe K-Nearest Neighbor learning, learning by analogical reasoning, reinforcement and evolutionary learning. We outline the application and challenges of machine learning. Did you know that self-driving automobile technology is dependent on machine learning? This fascinating free online course will enhance your understanding of the subjects and jump-start your career. Why wait? Start learning today!