Toward Data Science & Machine Learning

Near the end of my PhD study (2018), I decided to learn more about data science and machine learning. Below is a summary of the effort at that period.

Taiwan AI Academy #

• Taiwan AI Academy Technical Leader Program, Taipei, March - May 2018（台灣人工智慧學校，技術領袖班第一期）
  • Receive lectures on a variety of machine learning topics including traditional ML (regression, classification, clusrering) and deep learning (NN, RNN, CNN, RL, etc).
  • Hands-on experience in python (pandas, scikit-learn, tensorflow, keras), as well as data science competitions.
  • Participate in IBM x FET AI Contest held during the school. Team won the 1st place.
  • Final project: Improving Weather Forecast

MOOC Courses #

Machine Learning Specilizations #

1. Machine Learning with TensorFlow on Google Cloud Platform [Course link], a 5-course specialization by Google Cloud on Coursera. Specialization Certificate earned on October 2, 2018

   Individual Courses

   1. How Google does Machine Learning
   2. Launching into Machine Learning
   3. Intro to TensorFlow
   4. Feature Engineering
   5. Art and Science of Machine Learning

     [Certificates to all courses]

2. Machine Learning [Course link], a 4-course specialization by University of Washington on Coursera. Specialization Certificate earned on August 24, 2017

   Individual Courses

   1. Machine Learning Foundations: A Case Study Approach
   2. Machine Learning: Regression
   3. Machine Learning: Classification
   4. Machine Learning: Clustering & Retrieval

     [Certificates to all courses]

Others #

1. Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming by Stanford University on Coursera. Certificate earned at Sunday, January 1, 2017
2. Machine Learning by Stanford University on Coursera. Certificate earned at Saturday, November 12, 2016
3. Algorithms: Design and Analysis, Part 1 by Stanford University on Coursera. Certificate earned at Thursday, October 20, 2016