Ementorhub

What you'll learn

Apply different optimization methods while training and explain different behavior.

Use cloud tools and deep learning libraries to implement CNN architecture and train for image classification tasks.

Apply deep learning package to sequential data, build models, train, and tune.

Skills you'll gain

Natural Language Processing

Image Analysis

Computer Vision

Artificial Neural Networks

Python Programming

Generative AI

Deep Learning

Unsupervised Learning

Keras (Neural Network Library)

There are 5 modules in this course

Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. We recommend taking the two previous courses in the specialization, Introduction to Machine Learning: Supervised Learning and Unsupervised Algorithms in Machine Learning, but they are not required. College-level math skills, including Calculus and Linear Algebra, are needed. Some parts of the class will be relatively math intensive. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image by Ryan Wallace on Unsplash.

Training Neural Networks

Deep Learning on Images

Deep Learning on Sequential Data

Unsupervised Approaches in Deep Learning