Graduate Certificate in Machine Learning for Ad Retargeting Campaigns
-- viewing nowMachine Learning for Ad Retargeting Campaigns Unlock the power of data-driven advertising with our Graduate Certificate in Machine Learning for Ad Retargeting Campaigns. Designed for marketing professionals and data analysts, this program teaches you how to build and deploy predictive models that drive real results.
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About this course
Learn how to analyze customer behavior, identify high-value audiences, and create targeted ad campaigns that maximize ROI.
Gain hands-on experience with popular machine learning tools and techniques, including Python, R, and SQL.
Take the first step towards transforming your advertising strategy with data-driven insights.
Explore our Graduate Certificate in Machine Learning for Ad Retargeting Campaigns today and discover a new way to drive business growth.
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Course details
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Machine Learning Fundamentals for Ad Retargeting: This unit covers the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. It provides a solid foundation for understanding the concepts that are used in ad retargeting campaigns. •
Data Preprocessing for Ad Targeting: This unit focuses on data preprocessing techniques, including data cleaning, feature scaling, and feature engineering. It is essential for preparing data for modeling and improving the accuracy of ad retargeting campaigns. •
Ad Targeting with Deep Learning: This unit explores the use of deep learning techniques, such as neural networks and convolutional neural networks, for ad targeting. It covers topics like image recognition, natural language processing, and recommender systems. •
Ad Retargeting with Predictive Modeling: This unit delves into the use of predictive modeling techniques, including decision trees, random forests, and gradient boosting, for ad retargeting. It covers topics like model evaluation, hyperparameter tuning, and model deployment. •
Ad Personalization with Collaborative Filtering: This unit focuses on ad personalization using collaborative filtering techniques. It covers topics like user-based and item-based collaborative filtering, matrix factorization, and recommendation systems. •
Ad Targeting with Natural Language Processing: This unit explores the use of natural language processing techniques, such as text classification, sentiment analysis, and topic modeling, for ad targeting. It covers topics like text preprocessing, feature extraction, and model evaluation. •
Ad Retargeting with Reinforcement Learning: This unit delves into the use of reinforcement learning techniques, including Q-learning, policy gradients, and deep Q-networks, for ad retargeting. It covers topics like model evaluation, hyperparameter tuning, and model deployment. •
Ad Targeting with Transfer Learning: This unit focuses on the use of transfer learning techniques, including pre-trained models and fine-tuning, for ad targeting. It covers topics like model selection, hyperparameter tuning, and model evaluation. •
Ad Retargeting with Explainable AI: This unit explores the use of explainable AI techniques, including feature importance, partial dependence plots, and SHAP values, for ad retargeting. It covers topics like model interpretability, model explainability, and model trustworthiness. •
Ad Targeting with Ad Tech Platforms: This unit covers the use of ad tech platforms, including Google Ads, Facebook Ads, and LinkedIn Ads, for ad targeting. It covers topics like ad format, targeting options, and bidding strategies.
Machine Learning Fundamentals for Ad Retargeting: This unit covers the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. It provides a solid foundation for understanding the concepts that are used in ad retargeting campaigns. •
Data Preprocessing for Ad Targeting: This unit focuses on data preprocessing techniques, including data cleaning, feature scaling, and feature engineering. It is essential for preparing data for modeling and improving the accuracy of ad retargeting campaigns. •
Ad Targeting with Deep Learning: This unit explores the use of deep learning techniques, such as neural networks and convolutional neural networks, for ad targeting. It covers topics like image recognition, natural language processing, and recommender systems. •
Ad Retargeting with Predictive Modeling: This unit delves into the use of predictive modeling techniques, including decision trees, random forests, and gradient boosting, for ad retargeting. It covers topics like model evaluation, hyperparameter tuning, and model deployment. •
Ad Personalization with Collaborative Filtering: This unit focuses on ad personalization using collaborative filtering techniques. It covers topics like user-based and item-based collaborative filtering, matrix factorization, and recommendation systems. •
Ad Targeting with Natural Language Processing: This unit explores the use of natural language processing techniques, such as text classification, sentiment analysis, and topic modeling, for ad targeting. It covers topics like text preprocessing, feature extraction, and model evaluation. •
Ad Retargeting with Reinforcement Learning: This unit delves into the use of reinforcement learning techniques, including Q-learning, policy gradients, and deep Q-networks, for ad retargeting. It covers topics like model evaluation, hyperparameter tuning, and model deployment. •
Ad Targeting with Transfer Learning: This unit focuses on the use of transfer learning techniques, including pre-trained models and fine-tuning, for ad targeting. It covers topics like model selection, hyperparameter tuning, and model evaluation. •
Ad Retargeting with Explainable AI: This unit explores the use of explainable AI techniques, including feature importance, partial dependence plots, and SHAP values, for ad retargeting. It covers topics like model interpretability, model explainability, and model trustworthiness. •
Ad Targeting with Ad Tech Platforms: This unit covers the use of ad tech platforms, including Google Ads, Facebook Ads, and LinkedIn Ads, for ad targeting. It covers topics like ad format, targeting options, and bidding strategies.
Career path
| **Job Title** | **Description** |
|---|---|
| Machine Learning Engineer | Design and develop intelligent systems that can learn from data, with expertise in machine learning algorithms and programming languages such as Python and R. |
| Data Scientist | Extract insights from complex data sets using statistical models, machine learning algorithms, and programming languages like R and Python, to inform business decisions. |
| Artificial Intelligence/Machine Learning Specialist | Develop and implement AI and machine learning solutions to solve real-world problems, with expertise in areas like computer vision, natural language processing, and predictive analytics. |
| Business Intelligence Developer | Design and develop data visualization tools and business intelligence solutions to help organizations make data-driven decisions, with expertise in programming languages like SQL and Python. |
| Data Analyst | Collect, analyze, and interpret complex data sets to inform business decisions, with expertise in statistical software like Excel and R, and programming languages like Python. |
Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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GRADUATE CERTIFICATE IN MACHINE LEARNING FOR AD RETARGETING CAMPAIGNS
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Learner Name
who has completed a programme at
London School of Planning and Management (LSPM)
Awarded on
05 May 2025
Blockchain Id: s-1-a-2-m-3-p-4-l-5-e
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