Machine Learning Foundations Course Passed

Yes today I passed my Machine Learning Certificate Course in Machine Learning Foundations a Case Study approach from the University of Washington on Coursera. This course was a great introduction to Graphlab and really fun to do the modules from all 6 weeks. Graphlab allowed me to do regression analysis, classification analysis, sentiment analysis and machine learning with easy to use apis. The lecturers Carlos Guestrin and Emily Fox were fantastically enthusiastic making the course really enjoyable to do. I look forward to rolling this knowledge into my lectures in DBS over the coming months. Hopefully I have the time to complete the Specialization and Capstone project on Coursera too in the coming months.

Washington’s Regression Analysis – Assignment 1

First assignment done for the University of Washington’s Machine Learning Foundations course in regression analysis. There were 9 questions to answer having done the slides and practicals for week 1. An interesting way to pass this assignment – one has until the 9th October to get above 80% – so 8 out of 9 required. One can do the assignment at most 3 times in every 8 hour period. Anyway I got the following 9 questions correct on the first attempt

Q1. Which figure represents an overfitted model?

fitting_samples

Q2. True or false: The model that best minimizes training error is the one that will perform best for the task of prediction on new data.

Q3. The following table illustrates the results of evaluating 4 models with different parameter choices on some data set. Which of the following models fits this data the best?

Model index Parameters (intercept, slope) Residual sum of squares (RSS)
1 (0,1.4) 20.51
2 (3.1,1.4) 15.23
3 (2.7, 1.9) 13.67
4 (0, 2.3) 18.99

Q4. Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

linear_regression

Q5. Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

linear_regression2

Q6. Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

linear_regression3

Q7. Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

linear_regression4

Q8. Would you not expect to see this polot as a plot of training and test error curves?

screen-shot-2016-10-02-at-11-00-50-pm

Q9. True or false: One always prefers to use a model with more features since it better captures the true underlying process.

SFrame and Free GraphLab Create

Why SFrame & GraphLab Create

There are many excellent machine learning libraries in Python. One of the most popular one today is scikit-learn. Similarly, there are many tools for data manipulations in Python; a popular example is Pandas. However, most of these tools do not scale to large datasets.

The SFrame package is available in open-source under a permissive BSD license. So, you will always be able to use SFrames for free. It can be installed with:

The SFrame package is available in open-source under a permissive BSD license. So, you will always be able to use SFrames for free.

GraphLab Create is free on a 1-year, renewable license for educational purposes, including Coursera. This software, however, has a paid license for commercial purposes. You can get the GraphLab Create academic license at the following link:

https://dato.com/learn/coursera/

I was able to signup with my dbs lecturer email address and get a valid license key and then download the product and install. It will work in conjunction with Anaconda and Jupyter Notebooks.

GraphLab Create is very actively used in industry by a large number of companies. This package was created by a machine learning company called Dato. This company is spin off from a popular research project called GraphLab, which Carlos Guestrin and his research group started at Carnegie Mellon University. In addition to being a professor at the University of Washington, Carlos is the CEO of Dato.