Neural Networks for Machine Learning

Course Overview

This course, Neural Networks for Machine Learning, provides an introduction to the field of artificial neural networks and how they are being used for machine learning. Learners will gain an understanding of the basic algorithms and the practical tricks needed to get them to work well. The course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python). Topics covered include speech and object recognition, image segmentation, modeling language and human motion, etc. The course will be ending soon and the last day for enrollment will be October 10, 2018.

[Applications]
Those who have completed this course can apply their knowledge of neural networks and machine learning to a variety of applications. These applications include speech and object recognition, image segmentation, modeling language and human motion, and more. Learners can also use the practical tricks they have learned to get the algorithms to work well.

[Career Path]
One job position path recommended for learners of this course is a Machine Learning Engineer. A Machine Learning Engineer is responsible for developing and deploying machine learning models and algorithms to solve real-world problems. This involves researching, designing, and developing machine learning models, as well as testing and validating them. The Machine Learning Engineer must also be able to interpret and explain the results of the models to stakeholders.

The development trend for Machine Learning Engineers is to become more specialized in their field. As machine learning technology advances, Machine Learning Engineers will need to become more knowledgeable in specific areas such as natural language processing, computer vision, and robotics. They will also need to be able to work with a variety of data sources and be able to develop and deploy models in a variety of environments. Additionally, Machine Learning Engineers will need to stay up-to-date on the latest trends in machine learning technology and be able to apply them to their work.

[Education Path]
The recommended educational path for learners of this course is to pursue a Bachelor's degree in Computer Science. This degree will provide learners with a comprehensive understanding of the fundamentals of computer science, including programming languages, algorithms, data structures, operating systems, computer architecture, software engineering, and computer networks. Additionally, learners will gain an understanding of the principles of computer science, such as artificial intelligence, machine learning, and natural language processing.

The development trend of this degree is to focus on the application of computer science in various fields, such as business, healthcare, finance, and engineering. This will involve learning about the latest technologies and tools used in these fields, as well as the development of skills in problem-solving, data analysis, and software development. Additionally, learners will gain an understanding of the ethical and legal implications of computer science, as well as the social and economic impact of technology.