Neural Networks Demystified for Data Professionals from Pluralsight

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.

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.

What's inside

Syllabus

Course Overview

So, What Is a Neural Network and How Did We Get Here?

The Building Blocks of a Neural Network

Good to know

Know what's good

, what to watch for

, 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

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

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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

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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

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

View Melania on Amazon

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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

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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

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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

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Attend a Workshop on Advanced Neural Network Techniques

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

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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

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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.

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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

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

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.

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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.

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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.

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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

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.

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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.

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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.

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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.

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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.

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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.

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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