For the two months that I have sacrificed my spare time to do this :-)

A bit summery for main topics I’ve learned from this machine learning course:

supervised learning:

  • linear regression
  • logistic regression
  • neural networks
  • SVMs

unsupervised learning:

  • kmeans
  • PCA
  • anomaly detection

special topics:

  • recommender system
  • large scale machine leaning(mapReduce, parallel computing, CUDA)


  • bias/variance, regularization
  • deciding what to do next: evaluation of leaning algorithms
  • learning curves, error analysis, ceiling analysis.

What’s Next?

Next step is deciding what you would like to focus on. Someone just gave a few that are interesting to pursue but they’re by no means encompassing all that is going on in the field right now.

1. Computer Vision

For building vision system, whether for photos or videos.


What you will learn in the course:

  • the state-of-art convolutional neural networks
  • set up GPU instance in AWS

Follow up would be like:

  • scene labeling by combining with Recurrent Neural networks such as LSTM.

2. Natural Language Processing

For machine translation, question and answering, sentiment analysis, etc.


What you will learn in the course:

  • word embedding (word2vec, GLove)
  • bi-directional LSTMs for translation, etc.

3. Deep Reinforcement Learning

Others You Could Do

Suggested by サービスをつくるエンジニアが機械学習を学ぶべき3つの理由, you could probably read the following resources.

See What Other Engineers Read