Neural Network in Google Playground

Today's self study day you will be revisiting the concepts you have learnt over the past 2 weeks. Google Playground crash course provides with more detail explanation of the concepts using tensorflow. In case you have pending things to do from yesterday's codecademy course, we advise you to complete codecademy first, and then go through this course.

Try to reflect upon and see for yourself whether you are completing the learning objective given in the course itself.

1) Introduction to Machine learning

2) Key Machine Learning Terminology

3) ML: Linear Regression

4) ML: Training and Loss

5) Reducing Loss: An Iterative Approach

6) Reducing Loss: Gradient Descent

7) Reducing Loss: Learning Rate

8) Reducing Loss: Optimizing Learning Rate -Feel free to play with the interactive experiment with different learning rates and complete the exercises given in the tutorial

9) Reducing Loss: Stochastic Gradient Descent

10) Introduction to Playground with Reduce Loss exercises

11) Generalization - video lecture

12) Peril of overfitting

13) Splitting Data: Training and Test Sets

14) Validation Set

15) Feature Engineering

16) Encoding Nonlinearity

17) Logistic Regression: Calculating a Probability - Optional

18) Classification: Thresholding

19) Classification: True vs. False and Positive vs. Negative

20) Classification: Accuracy

21) Classification: Precision and Recall

22) Classification: ROC Curve and AUC

23) Neural Network

24) Training Neural Networks: Best Practices