ML Bootcamp 2019
You can find the resources, notes and links that will be used at the Machine Learning Bootcamp here.
Day 1
- Intro to ML
- Problem framing
- Data preparation and Feature Engineering
- Linear and Logistic regression with scikit learn (theory + hands on)
- Intro to Tensorflow (sessions, graphs and basic principles + hands on)
- Loss function / Gradient Descent / Backpropagation
- First Neural Net with Tensorflow and Intro to Keras(training, testing with different hyper params, loss functions etc)
Lunch Break
- Generalizing (overfitting, underfitting, regularization etc.)
- Feature crossing
- TF Estimators
- Practical exercise : New York taxi fare prediction
Day 2
- Recap of day 1
- New York taxi fare prediction solution
- Embeddings
- How to use GCP for ML(train New york taxi fare model on ML Engine)
Lunch Break
- Convolutional neural networks (practical: an image classification)
- Artistic style transfer
- Basic intro to RNN/LSTMs and their use cases
- Convert the image classification model (built above) and deploy on Firebase
- ML Qwiklabs