Introduction to Deep Learning with Artificial Neural Networks

Transform your proficiency with our exclusive neural network course! Uncover the intricacies of deep learning and position yourself as a pioneer in the realm of AI evolution!

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An outline of this training course

This course is designed to demystify the often misunderstood concept of neutral networks. While the name might suggest a middle ground, it's far from mundane. Dive into a structured journey that begins with the foundational principles, gradually leading you to more advanced topics. 

Understand the contrast between traditional machine learning and neutral networks, and discover their unique applications and advantages.Through a blend of theory and practical examples, you'll gain insights into data analysis techniques, model building, and optimization. 

By the end of this course, you'll have a solid grasp of neutral networks, empowering you to harness their potential in various fields. Whether you're a student, a professional looking to upskill, or just curious about the world of artificial intelligence, this course offers a comprehensive introduction. 

Join us and start on a learning adventure that promises both depth and breadth in the realm of neutral networks.

 

What is needed to take this course 

For this course, a basic understanding of mathematical concepts and familiarity with programming principles is beneficial. While no specific tools are mandatory, access to a computer with internet connectivity will enhance your learning experience. Beginners are encouraged to join; the course is structured to accommodate both novices and those with some foundational knowledge.

 

Who is this course for

Individuals keen on understanding the contrast between traditional machine learning and deep learning, as well as those venturing into data analysis and model optimization, will find this course immensely valuable. This course is especially beneficial for budding data scientists, AI enthusiasts, and professionals looking to integrate neural network concepts into their work.

 

Details of what you will learn in this course

By the end of this course, you will:

  • Understand neural vs. traditional machine learning.
  • Explore foundational neural network principles.
  • Master data analysis and visualization techniques.
  • Build and optimize neural network models.
  • Evaluate model performance using metrics.

 

What you get with the course

  • An hour of self-paced video training
  • Resource Pack
  • An Assessment

 

Program Level

Intermediate

 

Field(s) of Study

Artificial Intelligence, Machine Learning, and  Neural Networks

 

Instruction Delivery Method

QAS Self-study

 

***This course was published in October 2023

 

Enterprise DNA is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org

 

 

What our

Students Say
Curriculum
1

Course Overview


2

Resources


3

Introduction and Data Handling


4

In-depth Exploratory Data Analysis (EDA)


5

Data Preprocessing for Neural Networks


6

Model Foundations and Neural Network Basics


7

Running and Evaluating Neural Networks


8

Practical Application


9

Conclusion and Next Steps


Your

Instructor
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Gaelim Holland

Enterprise DNA Expert

  • Innovative Data Analyst and Digital Channel Optimization Specialist with thorough knowledge of Omni channel analytics and incorporating online and offline data in funnel analysis.
  • Skilled in maximizing online sales, revenue, and call-to-actions through conversion rate optimization, statistical science, and A/B testing. Deep expertise in statistical testing tools, data extraction, and data science.
  • My 15 year career has allowed me to work in multiple data science roles in several industries at organizations from the startup level to Fortune 500 companies across 3 continents.

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