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Netta Tzin
This course will teach you how Deep learning is integrated into the daily practices of digital marketing, in order to improve digital marketing practices in the data driven era, and maintain better connections with existing and potential users.
Read more
This course will teach you how Deep learning is integrated into the daily practices of digital marketing, in order to improve digital marketing practices in the data driven era, and maintain better connections with existing and potential users.
Read more
This course will teach you how Deep learning is integrated into the daily practices of digital marketing, in order to improve digital marketing practices in the data driven era, and maintain better connections with existing and potential users.
This Path is designed to have learners experience how Deep learning is integrated into our day to day lives. The Learning outcome for this course is to review case studies related to Deep Learning application for Marketing. The course will cover approaches and algorithms that are effective in simplifying processes for Marketing. Business case study examples provided are indicative.
Register for this course and see more details by visiting:
OpenCourser.com/course/hkg8tg/deep
What's inside
Syllabus
Course Overview
Introducing Deep Learning as Part of Artificial Intelligence and Its Usage in Digital Marketing
Exploring Deep Learning Applications in Marketing through Various Business Use-cases
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops professional skills in deploying Deep Learning in the marketing world
Suitable for intermediate learners seeking to strengthen their Deep Learning knowledge in a marketing context
Teaches approaches and algorithms to simplify marketing processes using Deep Learning
Covers case studies related to Deep Learning applications in marketing, providing practical insights for learners
Save this course
Save Deep Learning Application for Marketing to your list so you can find it easily later:
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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 Deep Learning Application for Marketing with these
activities:
Strengthen Python Programming Skills
Show steps
Deep learning heavily relies on Python programming. Reviewing and strengthening Python skills will ensure a solid foundation for implementing and understanding deep learning algorithms.
Browse courses on
Python Programming
Show steps
-
Review basic Python syntax, data types, and control flow.
-
Practice using data structures like lists, dictionaries, and NumPy arrays.
-
Implement basic algorithms like sorting, searching, and filtering.
Review Linear Algebra
Show steps
Deep learning relies heavily on linear algebra for operations such as matrix multiplication and decomposition. Reviewing linear algebra will provide a strong foundation for understanding deep learning concepts.
Browse courses on
Linear Algebra
Show steps
-
Review the concepts of vectors, matrices, and vector spaces.
-
Practice solving systems of linear equations using various methods (e.g., Gaussian elimination, Cramer's rule).
-
Understand the concept of eigenvalues and eigenvectors and their applications in linear transformations.
Follow Tutorials on Deep Learning Frameworks
Show steps
Hands-on experience with deep learning frameworks like PyTorch or TensorFlow is crucial. Guided tutorials provide a structured approach to learning the basics and practicing implementation.
Browse courses on
Tensors
Show steps
-
Choose a deep learning framework (e.g., PyTorch or TensorFlow) and find beginner-friendly tutorials.
-
Follow the tutorials step-by-step to build and train simple neural networks.
-
Experiment with different hyperparameters and architectures to observe their impact on model performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in Deep Learning Study Groups
Show steps
Engaging in discussions and working with peers helps reinforce concepts, foster collaboration, and provide diverse perspectives on deep learning.
Show steps
-
Find or create a study group with other students taking the course or interested in deep learning.
-
Set regular meeting times to discuss course material, share knowledge, and work on projects together.
-
Take turns presenting concepts, leading discussions, and providing feedback to each other.
Solve Deep Learning Coding Challenges
Show steps
Solving coding challenges specifically focused on deep learning algorithms enhances problem-solving skills and deepens understanding of implementation details.
Show steps
-
Find online platforms or resources that provide deep learning coding challenges.
-
Choose a challenge that aligns with your skill level and interests.
-
Implement the algorithm in Python, considering efficiency and accuracy.
Explain Deep Learning Concepts to a Non-Technical Audience
Show steps
Explaining complex technical concepts to a non-technical audience requires a deep understanding. This activity encourages students to solidify their knowledge by articulating it clearly.
Show steps
-
Choose a specific deep learning concept (e.g., neural networks, backpropagation).
-
Research and gather information to build a strong foundation on the concept.
-
Write a blog post, create a presentation, or record a video explaining the concept in simple and engaging terms.
Develop a Deep Learning Project
Show steps
Building a deep learning project from scratch allows students to apply their knowledge comprehensively, develop end-to-end solutions, and showcase their skills.
Show steps
-
Define the problem or business case for the deep learning project.
-
Gather and prepare the necessary data for training and testing the model.
-
Choose an appropriate deep learning architecture and implement the model.
-
Train and evaluate the model, optimizing its performance.
-
Deploy and monitor the model in a real-world scenario.
Strengthen Python Programming Skills
Show steps
Deep learning heavily relies on Python programming. Reviewing and strengthening Python skills will ensure a solid foundation for implementing and understanding deep learning algorithms.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review basic Python syntax, data types, and control flow.
- Practice using data structures like lists, dictionaries, and NumPy arrays.
- Implement basic algorithms like sorting, searching, and filtering.
Review Linear Algebra
Show steps
Deep learning relies heavily on linear algebra for operations such as matrix multiplication and decomposition. Reviewing linear algebra will provide a strong foundation for understanding deep learning concepts.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the concepts of vectors, matrices, and vector spaces.
- Practice solving systems of linear equations using various methods (e.g., Gaussian elimination, Cramer's rule).
- Understand the concept of eigenvalues and eigenvectors and their applications in linear transformations.
Follow Tutorials on Deep Learning Frameworks
Show steps
Hands-on experience with deep learning frameworks like PyTorch or TensorFlow is crucial. Guided tutorials provide a structured approach to learning the basics and practicing implementation.
Browse courses on
Tensors
Show steps
Show steps
Show steps
- Choose a deep learning framework (e.g., PyTorch or TensorFlow) and find beginner-friendly tutorials.
- Follow the tutorials step-by-step to build and train simple neural networks.
- Experiment with different hyperparameters and architectures to observe their impact on model performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in Deep Learning Study Groups
Show steps
Engaging in discussions and working with peers helps reinforce concepts, foster collaboration, and provide diverse perspectives on deep learning.
Show steps
Show steps
Show steps
- Find or create a study group with other students taking the course or interested in deep learning.
- Set regular meeting times to discuss course material, share knowledge, and work on projects together.
- Take turns presenting concepts, leading discussions, and providing feedback to each other.
Solve Deep Learning Coding Challenges
Show steps
Solving coding challenges specifically focused on deep learning algorithms enhances problem-solving skills and deepens understanding of implementation details.
Show steps
Show steps
Show steps
- Find online platforms or resources that provide deep learning coding challenges.
- Choose a challenge that aligns with your skill level and interests.
- Implement the algorithm in Python, considering efficiency and accuracy.
Explain Deep Learning Concepts to a Non-Technical Audience
Show steps
Explaining complex technical concepts to a non-technical audience requires a deep understanding. This activity encourages students to solidify their knowledge by articulating it clearly.
Show steps
Show steps
Show steps
- Choose a specific deep learning concept (e.g., neural networks, backpropagation).
- Research and gather information to build a strong foundation on the concept.
- Write a blog post, create a presentation, or record a video explaining the concept in simple and engaging terms.
Develop a Deep Learning Project
Show steps
Building a deep learning project from scratch allows students to apply their knowledge comprehensively, develop end-to-end solutions, and showcase their skills.
Show steps
Show steps
Show steps
- Define the problem or business case for the deep learning project.
- Gather and prepare the necessary data for training and testing the model.
- Choose an appropriate deep learning architecture and implement the model.
- Train and evaluate the model, optimizing its performance.
- Deploy and monitor the model in a real-world scenario.
Career center
Learners who complete Deep Learning Application for Marketing will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is a professional who designs, builds, and maintains machine learning models. Deep learning is a subset of machine learning that is used to build and train models that can learn from data without being explicitly programmed. This course will provide you with the skills and knowledge to apply deep learning to marketing problems, which will make you a more valuable asset to any organization.
Data Scientist
Data Scientist
A Data Scientist is a professional who uses data to solve business problems. Deep learning is a powerful tool for data analysis, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
AI Engineer
AI Engineer
An AI Engineer is a professional who designs, builds, and maintains AI models. Deep learning is a subset of AI that is used to build and train models that can learn from data without being explicitly programmed. This course will provide you with the skills and knowledge to apply deep learning to marketing problems, which will make you a more valuable asset to any organization.
Marketing Analyst
Marketing Analyst
A Marketing Analyst is a professional who analyzes data to help businesses make better marketing decisions. Deep learning is a powerful tool for marketing analysis, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Product Manager
Product Manager
A Product Manager is a professional who manages the development and launch of new products. Deep learning is a powerful tool for product development, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Marketing Manager
Marketing Manager
A Marketing Manager is a professional who plans and executes marketing campaigns. Deep learning is a powerful tool for marketing, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Data Engineer
Data Engineer
A Data Engineer is a professional who designs, builds, and maintains data pipelines. Deep learning is a powerful tool for data engineering, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is a professional who uses mathematical and statistical models to analyze financial data. Deep learning is a powerful tool for quantitative analysis, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Actuary
Actuary
An actuary is a professional who uses mathematics and statistics to assess risk. Deep learning is a powerful tool for actuarial science, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Software Engineer
Software Engineer
A Software Engineer is a professional who designs, builds, and maintains software applications. Deep learning is a powerful tool for software development, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Business Analyst
Business Analyst
A Business Analyst is a professional who analyzes business processes and recommends improvements. Deep learning is a powerful tool for business analysis, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Operations Research Analyst
Operations Research Analyst
An Operations research analyst is a professional who uses mathematical and statistical models to solve business problems. Deep learning is a powerful tool for operations research, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Statistician
Statistician
A Statistician is a professional who collects, analyzes, and interprets data. Deep learning is a powerful tool for statistics, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Management Consultant
Management Consultant
A Management Consultant is a professional who advises businesses on how to improve their operations. Deep learning is a powerful tool for management consulting, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
Financial Analyst
Financial Analyst
A Financial Analyst is a professional who analyzes financial data to make investment recommendations. Deep learning is a powerful tool for financial analysis, and this course will provide you with the skills and knowledge to apply deep learning to marketing problems. This will make you a more valuable asset to any organization.
For more career information including salaries, visit:
OpenCourser.com/course/hkg8tg/deep
Reading list
We've selected 11 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
Deep Learning Application for Marketing.
Save
Provides a practical guide to using machine learning for marketing tasks, including customer segmentation, lead scoring, and churn prediction. It valuable resource for anyone interested in using machine learning to improve their marketing campaigns.
Provides a comprehensive overview of deep learning techniques for time series analysis, including forecasting, anomaly detection, and sequence generation. It valuable resource for anyone interested in using deep learning for marketing tasks that involve time series data.
Provides a comprehensive overview of deep learning with R, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone interested in using deep learning for marketing tasks in R.
Provides a comprehensive overview of deep learning with Python, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone interested in building deep learning models for marketing tasks.
Provides a comprehensive overview of deep learning techniques for computer vision, including image classification, object detection, and image segmentation. It valuable resource for anyone interested in using deep learning for marketing tasks that involve images.
Provides a comprehensive overview of natural language processing with Python, including text preprocessing, tokenization, stemming, and lemmatization. It valuable resource for anyone interested in using deep learning for marketing tasks that involve text.
Provides a practical introduction to machine learning using Python and popular libraries such as scikit-learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, model training, and evaluation.
Provides a practical guide to using machine learning for predictive analytics, including regression, classification, and clustering. It valuable resource for anyone interested in using machine learning to make predictions about future events.
Provides a comprehensive overview of machine learning algorithms, including regression, classification, and clustering. It valuable resource for anyone interested in understanding the underlying principles of machine learning.
Save
Provides a gentle introduction to machine learning for beginners. It covers a wide range of topics, including data preprocessing, model training, and evaluation.
Provides a gentle introduction to machine learning with Python for beginners. It covers a wide range of topics, including data preprocessing, model training, and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hkg8tg/deep
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Time
53 hours
Level
Introductory
Via
Pluralsight
Instructor
Netta Tzin
Language
English
Good to know
Develops professional skills in deploying Deep Learning in the marketing world
Suitable for intermediate learners seeking to strengthen their Deep Learning knowledge in a marketing context
Teaches approaches and algorithms to simplify marketing processes using Deep Learning
Covers case studies related to Deep Learning applications in marketing, providing practical insights for learners
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