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Licenses & certifications
Licenses & certifications Exploratory Data Analysis for Machine LearningExploratory Data Analysis for Machine Learning IBMIBMIssued May 2025Issued May 2025Credential ID https://coursera.org/share/f96a683133c0307792215e3b799c4221Credential ID https://coursera.org/share/f96a683133c0307792215e3b799c4221 Show credential Skills: Artificial Intelligence…
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Date - May 17, 2025
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Licenses & certifications
- Exploratory Data Analysis for Machine Learning
IBMIssued May 2025Credential ID https://coursera.org/share/f96a683133c0307792215e3b799c4221
- Skills: Artificial Intelligence and Machine Learning (AI/ML) · Machine Learning · Machine learning models · Data Analysis
- Exploratory Data Analysis for Machine LearningArtificial Intelligence is not new, but it is new in a sense that it is easier than ever to get started using Machine Learning in business settings. In this module, we will go over a quick introduction to AI and Machine Learning and we will visit a…
- IBM Supervised Machine Learning: Classification IBM
IBMIssued May 2025Credential ID https://coursera.org/share/e2f6a71630994dde889dda2de3c34169
- Skills: Machine Learning · Artificial Intelligence and Machine Learning (AI/ML) · Machine learning models
- Supervised Machine Learning: ClassificationI learned to build models, analyze data, and solve problems using Python and machine learning. Key topics like regression, algorithms, and statistics gave me strong technical foundations. This journey sharpened my skills and deepened my interest in AI and data science
- IBM Unsupervised Machine Learning
IBMIssued May 2025Credential ID https://coursera.org/share/ad40cedfb52874dff61eef6f490c2bb3
- Practical Machine Learning
- Skills: Machine Learning
- Practical Machine LearningI learned how to build and apply basic prediction functions, which deepened my understanding of how models use data to make decisions. Working with training and test sets helped me grasp the importance of evaluating model performance and the risks of overfitting
- Exploratory Data Analysis with R
- Supervised Machine Learning: Regression
- Skills: Artificial Intelligence and Machine Learning (AI/ML) · Machine Learning · Machine learning models
- Supervised Machine Learning: RegressionI completed a certification in Supervised Machine Learning: Regression, where I learned how to build and evaluate models that predict continuous outcomes using labeled data. The course covered key concepts such as linear regression, model evaluation metrics, regularization techniques (Ridge, Lasso, Elastic Net), and strategies to prevent overfitting. I also gained hands-on experience with feature scaling, cross-validation, and hyperparameter tuning through practical exercises and assessments
- Regression Models
- Skills: Artificial Intelligence and Machine Learning (AI/ML) · Machine Learning · Machine learning models
- Regression ModelsIn this module, I applied regression analysis techniques including ANOVA and ANCOVA to understand relationships between variables. I also explored residual analysis and gained practical skills in statistical programming and interpreting model outputs.
- Introduction to Software Engineering
IBMIssued Sep 2024Credential ID https://www.coursera.org/account/accomplishments/verify/WAQ8CCQE6LHS
- Python for Data Science, AI & Development
- What is Data Science
