Neural Networks and Deep Learning  

This course covered the foundations of deep learning including topics like:

• Understand the major technology trends driving Deep Learning
• Be able to build, train and apply fully connected deep neural networks
• Know how to implement efficient (vectorized) neural networks
• Understand the key parameters in a neural network’s architecture

Algorithmic Toolbox  

This course covered basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming.

Structuring Machine Learning Projects  

This course was concerened on how to build a successful machine learning project.

R Programming  

In this course I learned how to program in R and how to use R for effective data analysis. This course covered practical issues in statistical computing which included programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization  

This course taught me the “magic” of getting deep learning to work well. Rather than the deep learning process being a black box, it explained what drives performance, and the ability to more systematically get good results.