Introduction to Machine Learning with ENCOG 3
Pluralsight
Course Summary
This course is focused on implementation and applications of various machine learning methods.
-
+
Course Description
This course is focused on implementation and applications of various machine learning methods. As machine learning is a very vast area, this course will be targeted more towards one of the machine learning methods which is neural networks. The course will try to make a base foundation first by explaining machine learning through some real world applications and various associated components. In this course, we'll take one of the open source machine learning framework for .NET, which is ENCOG. The course will explain how ENCOG fits into the picture for machine learning programming. Then we'll learn to create various neural network components using ENCOG and how to combine these components for real world scenarios. We'll go in detail of feed forward networks and various propagation training methodologies supported in ENCOG. We'll also talk about data preparation for neural networks using normalization process. Finally, we will take a few more case studies and will try to implement tasks of classification & regression. In the course I will also give some tips & tricks for effective & quick implementations of neural networks in real world applications.
-
+
Course Syllabus
Introduction to Machine Learning- 5m 6s
—Introduction 0m 14s
—Why Machine Learning ? 0m 24s
—Why This Course ? 1m 43s
—Key Concepts 0m 49s
—Spam Filtering 0m 36s
—Course Structure 1m 20sApplications of Machine Learning- 7m 12s
—Introduction 0m 16s
—Internet 1m 38s
—Financial Sector 1m 9s
—e-Commerce 1m 24s
—Process Industry 0m 54s
—Others 1m 3s
—Summary 0m 48sMachine Learning Tasks- 12m 37sIntroduction to Neural Networks- 23m 21sIntroduction to ENCOG 3- 5m 47sNeural Network Components in ENCOG for .NET- 17m 31sPropagation Training- 14m 58sData Normalization- 15m 14sCase Studies (Classification and Regression Task)- 37m 32s