In this course you will learn advanced topics related to machine learning for more accurate neural network predictive models. You will also learn different types of neural networks and their implementations using open source machine learning framework ENCOG.
Are you worried about your neural network model prediction accuracy? Are you not sure about your neural network model selection for your machine learning problem? This course will introduce you to more advanced topics in machine learning. The previous introductory course, "Introduction to Machine Learning with ENCOG 3," laid out a solid foundation of machine learning and neural networks. This course will build upon that foundation for more advanced machine learning implementations. In this course, you will learn about various neural network optimization techniques to overcome the problems of underfitting and overfitting and to create more accurate predictive models. This course will also provide an overall picture of various neural network architectures and reasons for their existence. This course will be focused towards implementation of various supervised feed forward and feedback networks. During the whole course, we will be using open source machine learning framework ENCOG to implement various concepts discussed in this course. Although the implementations in this course are ENCOG-based, concepts discussed in this course are widely applicable in other frameworks or even in custom development.
Abhishek Kumar is a data science consultant, author, and Google Developers Expert (GDE) in machine learning. He holds a master’s degree from the University of California, Berkeley, and has been featured in the "Top 40 under 40 Data Scientist" list.