I am an experienced "traditional" software engineer switching to a career as a Machine Learning Engineer.
The pieces of my Machine Learning experiences are already quite scattered, so here is a place that brings them all together in one place.
Table of contents
- ml-style-transfer Implementation of Neural Style Transfer that allows easy hyperparameter experimentation.
- ml-style-transfer-samples Some interesting outcomes from experiments with different styles, etc.
- tic-tac-toe Analysis of the original [UCI Kaggle Tic-Tac-Toe End game Dataset] and code that shows how it was created and more.
- tic-tac-toe Kaggle dataset contribution packaged release of Tic Tac Toe complete Dataset to Kaggle.
✅ Machine Learning Specialization (2023 Feb) [Specialization home page] [Certificate]
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✅ Supervised Machine Learning: Regression and Classification [Course home page] [Certificate]
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✅ Advanced Learning Algorithms [Course home page] [Certificate]
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✅ Unsupervised Learning, Recommenders, Reinforcement Learning [Course home page] [Certificate]
✅ Deep Learning Specialization (2023 Jul) [Specialization home page] [Certificate]
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✅ Neural Networks and Deep Learning [Course home page] [Certificate]
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✅ Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization [Course home page] [Certificate]
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✅ Structuring Machine Learning Projects [Course home page] [Certificate]
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✅ Convolutional Neural Networks [Course home page] [Certificate]
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✅ Sequence Models [Course home page] [Certificate]
- ✅ Deep Learning with PyTorch : Image Segmentation (2023 Jun) [Course home page] [Certificate]