Certificates

The courses behind my work, in the order I took them. Move over a frame to tilt it.

01 / Kaggle

Intro to Machine Learning

This course taught me the full loop of a first model: load the data, fit a tree, check it on data it has not seen, and know when it is overfitting.

• Decision trees
• Random forests
• Model validation
• Overfitting and underfitting

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02 / Kaggle

Intermediate Machine Learning

This course taught me to handle messy real data properly and to stop my own pipeline from leaking the answer into training.

• Missing values
• Categorical encoding
• Pipelines
• Cross-validation
• XGBoost
• Data leakage

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03 / Kaggle

Feature Engineering

This course taught me to find which features matter and to build new ones that make a model's job easier.

• Mutual information
• Creating features
• K-means features
• PCA
• Target encoding

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04 / Kaggle

Machine Learning Explainability

This course taught me to look inside a model and see why it made a decision before I trust it.

• Permutation importance
• Partial dependence plots
• SHAP values

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05 / Kaggle

Intro to Deep Learning

This course taught me how neural networks learn and how to keep them from memorising the training data.

• Neural networks with Keras
• Stochastic gradient descent
• Dropout
• Batch normalization

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06 / Kaggle

Computer Vision

This course taught me how convolutional networks see images, and how to reuse a pretrained model instead of starting from zero.

• Convolution and ReLU
• Max pooling
• Custom convnets
• Data augmentation
• Transfer learning

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07 / IBM SkillsBuild

AI Fundamentals

This course gave me a clear map of the whole field, from machine learning to language and vision, and how to use AI responsibly.

• Core AI concepts
• Machine learning basics
• NLP
• Computer vision
• AI ethics

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