Machine Learning for Marketing Specialization
- Offered byCoursera
Machine Learning for Marketing Specialization at Coursera Overview
Duration | 3 months |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Go to Website  |
Credential | Certificate |
Machine Learning for Marketing Specialization at Coursera Highlights
- Earn a certificate from coursera
- Financial aid available
- Learn at your own pace
Machine Learning for Marketing Specialization at Coursera Course details
Marketers
Marketing and business analysts
Managers using data insights to make business decisions
You will create a digital marketing plan and an Ad Campaign on Google Ads using KPIs to evaluate the performance of campaigns
You will use Google Analytics, Google search engine, Python programming language, and Anaconda Navigator to make informed marketing decisions
You will also use text mining techniques for sentiment analysis and customer feedback analysis in marketing
The "Machine Learning for Marketing Specialization" is a comprehensive program designed to equip marketing professionals with the skills needed to harness the power of machine learning in their marketing strategies
This specialization covers the application of machine learning algorithms and techniques to optimize various aspects of marketing, including customer segmentation, personalization, and predictive analytics
Participants will learn how to use data-driven approaches to enhance marketing efficiency, improve customer targeting, and drive higher ROI
The program typically includes hands-on projects where learners can apply machine learning models to real-world marketing problems, using tools like Python and R
By the end of the specialization, participants will be able to leverage machine learning to automate and optimize marketing processes, making them more effective and competitive in the digital landscape
Machine Learning for Marketing Specialization at Coursera Curriculum
Supervised Learning and Its Applications in Marketing
Apply Python as an effective tool for supervised learning techniques
Develop and train supervised machine learning models for classification and regression tasks
Interpret and analyze various applications of supervised learning in marketing
Describe the deployment of machine learning models and the challenges encountered in the deployment
Unsupervised Learning and Its Applications in Marketing
Apply Python as an effective tool for implementing various algorithms
Describe unsupervised learning and list its various algorithms
List the various applications and promising areas for the application of unsupervised learning
Introduction to Decision Science for Marketing
Demonstrate a solid understanding of the decision-making process through data analytics
Visualize and imagine the application of data analytics techniques to real-world marketing problems
Explain how marketing analytics and decision science approaches for marketing can enhance the quality of marketing decision-making
Text Mining for Marketing
Comprehend what text mining is, what it accomplishes, and what use cases it can be put to in the marketing discipline
Examine how theoretical issues are translated into practical applications in text mining for the marketing domain
Identify the potent analytical techniques that you can apply to text and other types of data
Explain what constitutes sound practices and what does not while analyzing texts for decision-making in marketing
Digital Marketing Analytics
Describe consumer journey, intent, and activity on your business website or landing pages
Discuss the various digital marketing platforms, their nature, the business elements to create a digital marketing strategy, and various KPIs
Explain Google Analytics, its importance and data insights that businesses can leverage upon, and how GA4 is different from Universal Analytics
Analyze how to understand and fine-tune the brand image and market objectives from various KPIs
Machine Learning for Marketing Specialization at Coursera Entry Requirements
- No specific cutoff mentioned
Education: 10+2 passed from recognised board
- Not mentioned
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