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)
- Linear - https://colab.research.google.com/drive/1ekd7raM6lBgRYzrQTntXD1gegHlubXYK
- Logistic - https://colab.research.google.com/drive/1Qva6AwESqgjHkviIVwjUgy-NGRXoa-R0
Intro to Tensorflow (sessions, graphs and basic principles + hands on)
- TF Basic - https://colab.research.google.com/drive/1-pIkqipJKiYU01cH3U2ndrCnnaaL_0hR
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)
- https://colab.research.google.com/drive/1Kof7J312znH1NAMrjMhpbh8fZ0AXxSS2
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