October 1, 2019

417 words 2 mins read



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
language Python
size (curr.) 46 kB
stars (curr.) 162
created 2019-02-10

Google Machine Learning Course Notes


  • 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
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