We may earn an affiliate commission when you visit our partners.

Course Discontinued We've found some alternatives for you:

Top Active Alternatives

Neural Networks Demystified for Data Professionals is no longer accepting new enrollments. We've matched the highest-rated active courses in this subject:

Udemy ★ 5.0

Master Regression and Feedforward Networks [2024]

Read Reviews → Enroll Active

Udemy ★ 5.0

DeepSeek & Generative AI Masterclass for Software engineers

Read Reviews → Enroll Active

Udemy ★ 5.0

Get AWS AI Certified: HandsOn,Quiz & Tests|Zero-to-Hero 2025

Read Reviews → Enroll Active

Neural Networks Demystified for Data Professionals

Amber Israelsen

Neural networks are the foundation for so many applications in the field of machine learning and artificial intelligence. This course will demystify what they are, how they work, and how they can be used in a variety of real-world applications.

In the current era of artificial intelligence, a solid understanding of neural networks is essential for anyone looking to keep their skills fresh.

In this course, Neural Networks Demystified for Data Professionals, you’ll gain an understanding of neural networks and how they’re used in real-world applications.

In the current era of artificial intelligence, a solid understanding of neural networks is essential for anyone looking to keep their skills fresh.

In this course, Neural Networks Demystified for Data Professionals, you’ll gain an understanding of neural networks and how they’re used in real-world applications.

First, you’ll explore the fundamental concepts and terminology behind neural networks and how they came to be.

Next, you’ll discover the basic building blocks and architectures of a neural network and how they learn—such as nodes, layers, input, weights, and activation functions.

Finally, you’ll learn about the applications and impact of deep learning in various industries.

When you’re finished with this course, you’ll have the foundational knowledge of neural networks needed to pursue other studies in artificial intelligence.

This course is no longer available. Find something similar by browsing:

Neural Networks Deep Learning Machine Learning Artificial Intelligence Data Science TensorFlow Keras

What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Expands on the vocabulary and fundamentals of neural networking

Applies neural networks to real-world examples and scenarios

Suitable for those needing a basic understanding of neural networks

Can serve as foundational knowledge for those pursuing artificial intelligence

Instructed by Amber Israelsen, an experienced instructor in the field

Requires some familiarity with the basics of AI and neural networks

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Foundational neural networks for data professionals

According to students, this course excels at demystifying neural networks, making complex concepts accessible and easy to understand. Learners appreciate its focus on providing a solid foundational understanding, essential for anyone entering the AI landscape. While it offers valuable insights into real-world applications and impact across industries, some find that the course maintains a conceptual rather than a deeply technical or coding-intensive approach, which can be a limitation for those seeking extensive hands-on implementation details. Overall, it is widely considered an excellent starting point for further studies in artificial intelligence.

Focuses on understanding rather than deep technical implementation.

"This course is strong on fundamental concepts but lighter on actual coding or hands-on implementation."

"If you're looking for deep mathematical derivations and intricate coding, this course might not fully satisfy."

"It provides a broad, conceptual overview, which means it doesn't dive deeply into advanced architectures or programming."

Maintains an engaging and understandable pace for learners.

"The explanations were very easy to follow, I never felt overwhelmed by the material presented."

"The course moved at a comfortable pace, allowing me to absorb everything without feeling rushed."

"It truly makes a complex subject approachable, even for those not from a heavy math or computer science background."

Serves well as a prerequisite for more advanced studies.

"It gave me the foundational knowledge I absolutely needed before tackling more advanced AI courses."

"This is a perfect stepping stone for someone looking to build a career in AI/ML, setting the right base."

"I now feel confident enough to explore specialized neural network topics and deeper learning paths."

Connects theoretical concepts to practical industry uses.

"I appreciated the examples of deep learning's impact across various industries and real-world scenarios."

"It showed me how neural networks are actually used, not just the theory behind them."

"The focus on real-world applications helped me see the direct value of this knowledge in my professional work."

Provides a highly accessible introduction to neural networks.

"The course truly demystified neural networks for me, making complex ideas easy to grasp."

"I finally understand the basic building blocks and how they function, which was my main goal."

"It's an excellent starting point if you're new to the neural network landscape and need clarity."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Neural Networks Demystified for Data Professionals with these activities:

Experiment with Neural Network Architectures

Show steps

Practice building and training neural networks using different architectures. This will help you develop a deeper understanding of how they work.

Browse courses on Node

Show steps

Create a simple neural network model.
Adjust the architecture of the network.
Train the network on a dataset.
Evaluate the performance of the network.

Read 'Neural Networks and Deep Learning' by Michael Nielsen

Show steps

Read 'Neural Networks and Deep Learning' by Michael Nielsen to gain a comprehensive understanding of neural networks and deep learning.

View Melania on Amazon

Show steps

Purchase or borrow the book.
Read the book thoroughly.
Take notes and highlight important concepts.
Complete the exercises in the book.

Design a Neural Network for a Specific Application

Show steps

Create a neural network model for a specific application. This will help you apply your knowledge of neural networks to a real-world problem.

Browse courses on Deep Learning Applications

Show steps

Identify a specific application for a neural network.
Design the architecture of the neural network.
Train the network on a dataset.
Evaluate the performance of the network.
Document your process and findings.

Two other activities

Expand to see all activities and additional details

Show all five activities

Attend a Workshop on Advanced Neural Network Techniques

Show steps

Attend a workshop to learn about advanced neural network techniques. This will help you expand your knowledge and skills in this field.

Show steps

Research and identify a workshop on advanced neural network techniques.
Register for the workshop.
Attend the workshop and actively participate in the activities.
Apply the techniques you learned in the workshop to your own projects.

Participate in a Neural Network Competition

Show steps

Participate in a neural network competition to test your skills and learn from others. This will help you push your limits and develop your abilities.

Show steps

Identify and register for a neural network competition.
Develop a neural network model for the competition.
Train and test your model.
Submit your model to the competition.
Analyze the results of the competition and identify areas for improvement.

Career center

Learners who complete Neural Networks Demystified for Data Professionals will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their skills in machine learning to build and deploy neural networks for use cases such as forecasting, risk analysis, and fraud detection. These models incorporate large amounts of data to deliver insights that support better decision-making. This course can help build a foundation for working on these models and developing the knowledge needed to become a Data Scientist.

See salaries and explore the career path for Data Scientist

Deep Learning Engineer

Deep Learning Engineers build and maintain deep learning models. These models are used in a wide variety of applications such as image recognition, natural language processing, and speech recognition. This course will help build a strong foundation for becoming a Deep Learning Engineer.

See salaries and explore the career path for Deep Learning Engineer

Machine Learning Engineer

The knowledge gained from Neural Networks Demystified for Data Professionals will be helpful for working on more complex machine learning models, such as those using neural networks. Building and deploying machine learning models is within the Machine Learning Engineer's remit and for someone wishing to transition to this career, this course may be helpful.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Engineer

Artificial Intelligence Engineers, who may also be called AI Engineers, design, deploy, and maintain artificial intelligence systems. This course can provide the foundational knowledge required to pursue advanced studies in artificial intelligence, which is useful for anyone entering the field of AI Engineering.

See salaries and explore the career path for Artificial Intelligence Engineer

Software Engineer

In many domains, including those of business and technology, Software Engineers apply their abilities to machine learning and neural networks to build and maintain software. Neural Networks Demystified for Data Professionals can be useful for someone wishing to become a Software Engineer by providing a foundational understanding of neural networks, useful for building and maintaining this type of sophisticated software.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts may leverage machine learning and neural networks to uncover insights as part of their work, providing value to organizations. This course can help develop the required background knowledge for entry-level employment in this role.

See salaries and explore the career path for Data Analyst

Business Analyst

Neural networks have business applications such as gathering insights into customer behavior, enhancing efficiency of operations, and optimizing marketing efforts. This course is a good choice for Business Analysts who use machine learning and neural networks in their day-to-day work.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts frequently leverage machine learning and neural networks to analyze data sets, often in large volume. As a good fit for junior-to mid-level Quantitative Analysts, this course can help build a deeper understanding of the fundamentals of neural networks and their practical applications.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts with expertise in machine learning can derive valuable insights from complex data sets through the use of neural networks. This course may be helpful for those starting or transitioning their career to an Operations Research Analyst position.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts may use machine learning to build predictive models, neural networks included. This course may be useful to someone in this industry by providing the necessary foundational knowledge and understanding of neural networks, particularly for those in junior to mid-level positions.

See salaries and explore the career path for Financial Analyst

Statistician

Neural networks have specialized applications in the field of statistics, where they are used to model complex relationships between variables and find patterns in data. This course may be useful for a Statistician who wants to move into or advance a career in this domain.

See salaries and explore the career path for Statistician

Actuary

Actuaries who use machine learning may use neural networks to develop models for pricing insurance products, assessing risk, and forecasting financial performance. This course may be helpful by adding to the knowledge base of an Actuary and providing a greater understanding of neural networks.

See salaries and explore the career path for Actuary

Risk Analyst

In the finance and insurance sectors, Risk Analysts may use machine learning to develop models that predict risk. As part of this work, neural network techniques may be applied. This course can be useful to those who are early in their Risk Analyst career or who are looking to make a transition into the industry.

See salaries and explore the career path for Risk Analyst

Insurance Analyst

Insurance Analysts may use machine learning, including neural network techniques, to price insurance products and assess risk. This course may be of interest to someone who wishes to work as an Insurance Analyst, providing them with a foundation to implement these models.

See salaries and explore the career path for Insurance Analyst

Fraud Analyst

Neural networks have practical applications in fraud detection and prevention. This course can be helpful to Fraud Analysts by providing them with a foundation in understanding neural networks, which may be useful in both investigative and data-driven fraud detection roles.

See salaries and explore the career path for Fraud Analyst

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Neural Networks Demystified for Data Professionals.

Deep Learning for Coders with fastai and PyTorch

Save

This good reference book that provides more depth to the course and uses Fastai and PyTorch.

Top Active Alternatives

Neural Networks Demystified for Data Professionals

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Foundational neural networks for data professionals

Activities

Career center

Reading list

Share

Similar courses

Pluralsight Buyer Protection & Guarantee

10-Day Free Trial

Cancel Anytime (Non-Refundable Once Billed)

Verified Academic & Industry Partner