Neural Networks and Deep Learning

Machine learning , artificial intelligence, ai, deep learning blockchain neural network concept.

Next Offering 

Start Date: January 9th, 2023
End Date: February 27, 2023

 

Neural Networks and Deep Learning is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. This course can be taken individually or as one of four courses required to receive the CPDA certificate of completion.

Neural Networks and Deep Learning can be taken after Statistics, Data Mining, and Machine Learning in the CPDA program. After studying the application of various machine learning algorithms, students take a deeper dive in the field of neural networks, a subset of Machine Learning.  

The course is taught by faculty from the College of Engineering at The Ohio State University. The course is delivered in 100% distance learning format and includes instructional material equivalent a one semester credit hour class.  


Course Description

Deep learning (DL) is an important subset of machine learning (ML) methods that is based on artificial neural networks (ANNs), which are biologically-inspired function representations that enable a computer to learn directly from observational data. In this course, students will learn the foundations of DL, the most powerful ANN architectures, practical and efficient methods for training large-scale and complex ANN structures, and about important applications of DL in a variety of fields such as computer vision, speech recognition, drug discovery, healthcare, chemical engineering, and many others. 

4 CEUs are granted upon successful completion of the course.


Students Will Learn to:

  1. Understand the key technology trends driving the field of deep learning
  2. Be able to construct, train, and apply deep neural networks
  3. Recognize important parameters in the architecture of a neural network
  4. Apply regularization and cross-validation methods to avoid overfitting data
  5. Identify which deep learning methods are best suited for a given task
  6. Use Python and TensorFlow to build flexible and efficient deep/machine learning models

Instructor Introduction

Below is a video of the instructor for this course explaining what students will learn and can expect from this course. 

 

 

 

 

 

 

 

 


Prerequisites 

This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion. However, it is required that participants will have taken Introductory Statistics for Data Analytics, Data Mining, and Applied Machine Learning before this course if pursuing the entire certification. Students need familiarity with basic probability and statistics (random variables, expectation, mean and covariance, characteristic functions, central limit theorem, etc.) and data science best practices (data mining methodology, data preparation, transformation, etc.).

Students must have an understanding of Python before taking this course. If you don't have experience with Python, it's required that students complete this free training before starting the Neural Networks class: https://www.kaggle.com/learn/python


Click Here to learn more about how this course is delivered 100% online!


Expected Time Commitment to Complete this Course

Each course is equivalent to a one semester credit hour class. Therefore each class consists of approximately 40 hours of class time that includes 12-13 hours of recorded faculty lectures and 23-24 hours of additional course work. Each course is seven weeks in length, so each week there is 5.7 hours of combined class time (40 hrs / 7 weeks). The average student should allow a 2:1 study-to-class-time ratio to complete the course. This means you should plan to study two hours for each one hour of class time. This equates to 11-12 hours each week to complete all course work. (5.7 hrs X 2 = 11-12 hrs).  Based on a person's own personal strengths and experience, you should increase or decrease the ratio. 


Course Grading

The course is graded pass/fail. Students must receive an 80% or better on all graded items to pass and receive the certificate of completion for the course. 


Cancellations and Refunds

A full refund minus a $75 administrative fee will be made if cancellation is received three weeks prior to the start of the course. No refunds within three weeks of the course start date. Click here to see the complete schedule of all courses.

COURSE REGISTRATION

Registration ends on January 6, 2023.

Click below to register:

PRICE

Course Fee:

$799 per person
 

Early Bird Discount:

$675 per person if registered & paid by December 20th, 2022

 

Save more when you bundle your CPDA courses! Learn more

ONLINE INFORMATION SESSION

Learn more by attending the next online information session.

Thursday, December 1st, 2022 @ 12 pm EST

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