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