# AntonioErdeljac/Google-Machine-Learning-Course-Notes

Notes taken from Google Machine Learning Course provided to public for practice & correction.

repo name | AntonioErdeljac/Google-Machine-Learning-Course-Notes |

repo link | https://github.com/AntonioErdeljac/Google-Machine-Learning-Course-Notes |

homepage | |

language | Python |

size (curr.) | 46 kB |

stars (curr.) | 162 |

created | 2019-02-10 |

license | |

# Google Machine Learning Course Notes

### Wiki

- Framing
- In this section we learn the basics of Machine Learning Terminology

- Descending into Machine Learning
- In this section we work with linear regression, learn about MSE, loss caculation and the basics of how training a model works

- Reducing Loss
- In this section we explore loss reduction methods by explaining gradient descent, batches, iterative learning and other effective learning methods

- First Steps With Tensorflow
- In this section we learn the basics of TensorFlow and Pandas. Through practices linked we develop our own linear regression code

- Generalization
- In this article we discuss the problem of overfitting, learn the difference between a good and a bad model, learn about subsets used in model training & generalization

- Training and test sets
- In this section we learn about data splitting, dangers of training on test data & test data characteristics

- Validation
- In this section we cover the importance of validation, a 3rd partition in a dataset

- Representation
- In this section we discuss qualities of features, learn about feature engineering and mapping values to useful features

- Feature Crosses
- In this section we look into feature crosses, a synthetic feature used to improve model’s learning & encode non-linear data into useful features

- Regularization: Simplicity
- In this section we look into ways of penalizing the model for being too complex using L2 regularization

- Logistic Regression
- In this section we look into Logistic Regression to calculate probabilty, and dive deeper into it’s loss function

- Classification
- In this section we dive into evaluation precision and recall of logistic regression, as well as ROC & AUCs curves

- Regularization: Sparsity
- In this section we learn the differences between L1 & L2 and how they bring uninformative weights to 0 or close to 0

- Neural Networks
- In this section we learn how to solve non-linear problems with Neural Networks. We dive into basics of Neural Networks structure & how it all works

- Training Neural Networks
- In this article we dive into backpropagation, an algorithm used to traing Neural Networks

- Multi Class Neural Networks
- In this article we look into multi class neural networks which are the closest to real world example of machine learning usage such as recognizing cars, faces, poses etc.

- Embeddings
- Learn about embeddings & how they are used to translate large sparse-vectors to a lower dimensional space