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https://github.com/thomd/udacity-ml-with-tensor-flow

Notes for the Udacity course "Intro to ML with Tensor Flow - Scholarship Program Nanodegree Program"
https://github.com/thomd/udacity-ml-with-tensor-flow

machine-learning python udacity

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Notes for the Udacity course "Intro to ML with Tensor Flow - Scholarship Program Nanodegree Program"

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# Machine Learning with TensorFlow

Notes from the Udacity course **Intro to ML with Tensor Flow - Scholarship Program Nanodegree Program**

1. [Linear Regression](https://nbviewer.jupyter.org/github/thomd/udacity-ml-with-tensor-flow/blob/main/1-linear-regression.ipynb) (Linear Regression, Polynomial Regression, L1- & L2-Regularization, Feature Scaling)
2. [Perceptron Algorithm](https://nbviewer.jupyter.org/github/thomd/udacity-ml-with-tensor-flow/blob/main/2-perceptron-algorithm.ipynb)
3. [Decision Trees](https://nbviewer.jupyter.org/github/thomd/udacity-ml-with-tensor-flow/blob/main/3-decision-trees.ipynb)
4. [Naive Bayes](https://nbviewer.jupyter.org/github/thomd/udacity-ml-with-tensor-flow/blob/main/4-naive-bayes.ipynb)
5. [Support Vector Machines](https://nbviewer.jupyter.org/github/thomd/udacity-ml-with-tensor-flow/blob/main/5-support-vector-machines.ipynb)
6. [Ensemble Methods](https://nbviewer.jupyter.org/github/thomd/udacity-ml-with-tensor-flow/blob/main/6-ensemble-methods.ipynb)
7. [Model Evaluation Metrics](https://nbviewer.jupyter.org/github/thomd/udacity-ml-with-tensor-flow/blob/main/7-model-evaluation-metrics.ipynb)
8. [Training and Tuning](https://nbviewer.jupyter.org/github/thomd/udacity-ml-with-tensor-flow/blob/main/8-training-and-tuning.ipynb)