[i + 1] years and counting ... / Shut up and code!

[Hitchhiker's Guide To...]

Thought I'd start this to track my progress in becoming a Coding God.

Likely to be infrequently updated, but the overall thread might be interesting as a path to going from knowing no coding to an eventual plan of being high level competent.

My personal goal in order of chronological aim:

1. Get to very competent in python initially
2. Get to reasonably competent in machine learning techniques
3. Add some related coding skills - e.g. R
4. Do some research projects with the new skills
5. Do some fintech/investment startup work using the skills
6. Get a ridiculously wellpaid research position in a hedge fund and ditch the neckbeards of academia

Time to completion of the six steps: by September 2018

Lets see how that works out

[Hitchhiker's Guide To...]

Step 1.1: Have finished this coursera course: Learn to Program: The Fundamentals.

This was a brill course. Really well delivered. Went for the certified version (€46) as have seen on CVs a lot of people now including Coursera certified courses and those courses being accepted.

You start the course with nothing and end with great basic knowledge of how python works.

[Hitchhiker's Guide To...]

Step 1.2: Have enrolled in the follow-on course: Learn to Program: Crafting Quality Code.

Step 1.3: Got this highly recommended book: Automate the Boring Stuff with Python.

Aim is to work through both of these by the end of May. The course brings you from basics of how Python works to actually programming. The book shows what seems to be some of the breadth of possibility of Python.

Think by the end of May should therefore be at the stage of being at the last stages of beginner.

[shrapnel]

i have a soft copy of "Learn Python the hard way" by Zed Shaw if you want, which was recommended to me by Tar a couple of years ago.

[Flushdraw]

gl Hitch. Hope you have better luck finishing it than this

[Hitchhiker's Guide To...]

Step 1.2: Have enrolled in the follow-on course: Learn to Program: Crafting Quality Code.

Step 1.3: Got this highly recommended book: Automate the Boring Stuff with Python.

Aim is to work through both of these by the end of May. The course brings you from basics of how Python works to actually programming. The book shows what seems to be some of the breadth of possibility of Python.

Think by the end of May should therefore be at the stage of being at the last stages of beginner.

Got through that course fine. Not as good as the first one, but complimented the first one very well.

The book is really good. Up as far as the web scraping chapter.

[Hitchhiker's Guide To...]

Next plans for June:

Step 1.3: Finish off Automate the Boring Stuff book

Step 1.4: Enrolled in this Michigan uni course: Introduction to Data Science in Python. Which is the first of five courses from Michigan on data analysis in Python. Great reviews for the course and think I've enough basic knowledge of Python at this stage.

Step 1.5: Will also do this follow-on course from Michigan in June. Applied Plotting, Charting & Data Representation in Python. That'll get me about ready to do a machine learning course in July.

[Hitchhiker's Guide To...]

i have a soft copy of "Learn Python the hard way" by Zed Shaw if you want, which was recommended to me by Tar a couple of years ago.

Downloaded this. Looks like a great system of learning, but see there's a Python 3 version coming out in July, so going to hang on for that and maybe use it as a bit of a refresher course.

[Hitchhiker's Guide To...]

Step 4 is: Do some research projects with the new skills


Step 4.1: Write a code to retrieve newspaper articles from a database and then analyse the sentiment in those articles to come up with a determinant of pricing model for a car sharing website.

Started this during the month by linking up with a marketing guy to do some textual analysis using python. The idea is to see if general fear levels in a country influence what price someone charges for a seat in their car on a big car sharing website. The thought being that fear levels increase the price someone needs to share space with an unknown stranger. This will be presented at a fintech conference in December in Taiwan all going well, so gives a nice deadline to act as impetus and motivation.

My initial task in the project is to create a mini-program that collects data from a newspaper website. Its a painfully slow process of learning. I'm using selenium which opens a phantom browser and automates entering search terms in a search box and retreiving the results. Only in the middle of it now, with the following code. I'd estimate for each line below it has taken about an hour to write. The line to click the 'I accept the terms and conditions' button easily took about five hours!

# le figaro import from LexisNexus

from selenium import webdriver
from selenium.webdriver.support.ui import Select
from selenium.webdriver.common.keys import Keys
import time


# Login into LexisNexis

chromedriver = 'C:\\Users\\micha\\Dropbox\\Learning\\Python\\chromedriver.exe'
browser = webdriver.Chrome(chromedriver)

browser.get('https://www-nexis-com.dcu.idm.oclc.org')

username = browser.find_element_by_id("username")
password = browser.find_element_by_id("password")

username.send_keys("XXX")
password.send_keys("XXX")

browser.find_element_by_name("_eventId_proceed").click()

browser.find_element_by_xpath("/html/body/table/tbody/tr[2]/td/table/tbody/tr/td[2]/table/tbody/tr[4]/td[2]/table/tbody/tr/td/table/tbody/tr[1]/td/a[1]/img").click()

# Set up search

        # clear search window
browser.find_element_by_name("searchTerms1").clear()

        # set search dates
select1 = Select(browser.find_element_by_xpath('//*[@id="specifyDateDefaultStyle"]'))
select1.select_by_visible_text("Custom date")

browser.find_element_by_name("fromDate").clear()
browser.find_element_by_name("fromDate").send_keys("01/01/2014")
browser.find_element_by_name("toDate").clear()
browser.find_element_by_name("toDate").send_keys("01/01/2014")
        # need some way to generate dates in the above format and also to loop over those dates

        # enter Le Figaro as the source
browser.find_element_by_xpath('//*[@id="rightcolumnlarge"]/fieldset[4]/div[1]/div').click()
browser.find_element_by_id("selected_source").send_keys("Le Figaro Online")
time.sleep(3)
browser.find_element_by_id("selected_source").send_keys(Keys.DOWN)
browser.find_element_by_id("selected_source").send_keys(Keys.ENTER)
time.sleep(2)

        # exclude short articles
browser.find_element_by_name("excludeShortDocsChecked").click()

        # start search
browser.find_element_by_class_name("power_submit").click()

Need to add in the bits for looping over the dates next, and then the actual download data part.

[Hitchhiker's Guide To...]

Noticed theres a few web scraping packages: Selenium as above, BeautifulSoup, and Scrapy.

Is there a good place to really get into the pros and cons of each? I've read quite a few general comparisons, but nothing that delves into a proper level of depth.

[DeadParrot]

Wow... selenium
That takes me back to my testing days

[Hitchhiker's Guide To...]

Wow... selenium
That takes me back to my testing days

ha, read that in some places, that it had been appropriated from much more noble endevours.

[Hitchhiker's Guide To...]

Next plans for June:

Step 1.3: Finish off Automate the Boring Stuff book

Step 1.4: Enrolled in this Michigan uni course: Introduction to Data Science in Python. Which is the first of five courses from Michigan on data analysis in Python. Great reviews for the course and think I've enough basic knowledge of Python at this stage.

Step 1.5: Will also do this follow-on course from Michigan in June. Applied Plotting, Charting & Data Representation in Python. That'll get me about ready to do a machine learning course in July.

Ended up skipping step 1.3 as there was nothing much more needed from the rest of the book.

Did the course in Step 1.4 'Introduction to Data Science in Python', and its a fantastic course. Although I don't know if its my dumbassery or the pacing of the course, but the coursework seemed far too difficult and I'd be spending 10-20 hours on each assignment that was guided for 3-4 hours. Even still I mightily struggled.

I suppose thats what learning is all about, trying to find out the things you don't know.

I think maybe more python than just the two basic courses completed was needed before tackling this. A great course though.

As a result of that and a few other life things (see bbv ad infinitum) I didn't get the second course (step 1.5) finished and am just doing it now. From the first two classes of this second course it seems to take the material of the first course and assume that was piss easy, but hopefully the assignments themselves work out easier than the course lectures make it seem.

[Hitchhiker's Guide To...]

Plans for July:

Step 1.5: Do this data visualisation course from Michigan in June. Applied Plotting, Charting & Data Representation in Python.

Step 1.6: Do the Michigan machine learning course on coursera. Applied Machine Learning in Python.

Might struggle to get the second course done given how tough the slog has turned into, and I've only evenings after everyone is asleep to work on these courses.

[Hitchhiker's Guide To...]

Think then in August I might take a step back and do another course on the topics covered so far. Maybe this course 'Python for Data Science' on edX. Or maybe the Data Scientist with Python package on DataCamp.

Am just conscious that I'm kinda just about 'getting' all the material, but there's too many gaps in my understanding. This type of bedding down step back would then allow a big leap forward into the truely complex stuff afterwards.

[Hitchhiker's Guide To...]

Plans for July:

Step 1.5: Do this data visualisation course from Michigan in June. Applied Plotting, Charting & Data Representation in Python.

Step 1.6: Do the Michigan machine learning course on coursera. Applied Machine Learning in Python.

Might struggle to get the second course done given how tough the slog has turned into, and I've only evenings after everyone is asleep to work on these courses.

Both of these done. Oh it was a struggle. The machine learning one in particular. I understood all the ideas, but it was just the code to implement them that was mindboggling.

Machine learning is so clearly the future of everything though. Its absolutely amazing in terms of its potential and how it has upended previous ways of looking at how we can know things.

I kinda feel after this course that I've just looked in the shop window and spotted the goodies I'd like to someday have. Beginning of the beginning really.

[Hitchhiker's Guide To...]

Step 1.7: Complete the Data Scientist with Python program on DataCamp.

Read a lot of great things about this. The general view is that its not particularly great for learning from scratch, but if you have a few relevant uni courses under your belt then its excellent for getting the training in and getting you on top of the technicalities.

This is going to take a while. Have only a few spare evenings in August, and September is the start of intensive teaching. The plan though is to get it finished by the end of September.

Also going to pick up this hotly awaited book when the second edition is released at the end of September. Python for Data Analysis. The current edition is pretty much the pandas bible, but its now five years old so way too dated.

After finishing this I should be at an upper-intermediate level in general python and pandas, and maybe somewhere in the middle of the beginning of machine learning.

[Hitchhiker's Guide To...]

After that then it'll be jumping into the famous Stanford Machine Learning course taught by Andrew Ng. Hadn't done this course up to this point as its in MATLAB which is a whole new software to get on top of.

The course strength seems to be in explaining why we're doing all these techniques and how we can choose between the various testing choices.

Then it'll be just getting more and more advanced in the area. At a rough guess then it would be to be at an upper-intermediate level in machine learning by the end of 2017, and some level of decent expertise by the end of the first half of 2018.

[Hitchhiker's Guide To...]

Machine learning also offers many fascinating new ways to design this.

https://www.youtube.com/watch?v=ACmydtFDTGs

[Hitchhiker's Guide To...]

Great article on the potential of machine learning from Harvard Business Review.

[Hitchhiker's Guide To...]

Step 1.7: Complete the Data Scientist with Python program on DataCamp.

Read a lot of great things about this. The general view is that its not particularly great for learning from scratch, but if you have a few relevant uni courses under your belt then its excellent for getting the training in and getting you on top of the technicalities.

This is going to take a while. Have only a few spare evenings in August, and September is the start of intensive teaching. The plan though is to get it finished by the end of September.

Also going to pick up this hotly awaited book when the second edition is released at the end of September. Python for Data Analysis. The current edition is pretty much the pandas bible, but its now five years old so way too dated.

After finishing this I should be at an upper-intermediate level in general python and pandas, and maybe somewhere in the middle of the beginning of machine learning.

Skipped this for a bit as August was stupidly busy, and instead did two courses on Udemy on Tableau, the data visualisation software. You can usually pick Udemy courses up for a tenner when they have one of their regular sales. Did an introductory Tableau 10 A-Z and a more advanced course: Tableau 10 Advanced Training.

These courses are excellently delivered as the training is centred around case studies and Tableau is a phenomenal software. Almost to the extent that you would think anyone still using Excel to get information from data is a simpleton of the highest order.

Will move on to the planned DataCamp course next but it will be slow moving due to having to do some proper work.

[Hitchhiker's Guide To...]

After that then it'll be jumping into the famous Stanford Machine Learning course taught by Andrew Ng. Hadn't done this course up to this point as its in MATLAB which is a whole new software to get on top of.

The course strength seems to be in explaining why we're doing all these techniques and how we can choose between the various testing choices.

Then it'll be just getting more and more advanced in the area. At a rough guess then it would be to be at an upper-intermediate level in machine learning by the end of 2017, and some level of decent expertise by the end of the first half of 2018.

Since posting this, Andrew Ng has launched a new four-part course on deep learning set in Python that has the machine learning world all a flutter. Looks excellent. Might do this instead of his standalone course given its fairly clear from the trends that Python is going to rule the world for machine learning even if R is currently the best overall package.

[Hitchhiker's Guide To...]

Making progress on the DataCamp 'Data Scientist with Python' track. Its 20 modules, and have six done in September, so will be finished at this rate around mid-November.

DataCamp have come up with something very smart. Very nicely delivered and designed. My path to date was the two intro uni of Toronto python courses followed by three applied data science courses from Michigan U. I'd highly recommend slotting this in the middle between Toronto and Michigan, as Michigan was far too advanced compared to the beginning skillset you would have at this stage.

[Hitchhiker's Guide To...]

In not quite related news also read two machine learning general books:

The Master Algorithm (how machine learning will save us all)

Weapons of Math Destruction (how machine learning will destroy us all)

Nice to have that bit of a broader perspective on what all that means, but The Master Algorithm despite being interesting, went into way too much detail on the technicalities of the various machine learning choices.

[Hitchhiker's Guide To...]

Going to round out the year with a small dive into Excel VBA (got this course on Udemy for a tenner yesterday) and then a deepish dive in R.

My thinking is just to be familiar enough with all the main softwares that are used for some sort of data analysis. Excel VBA is still shockingly popular especially in Europe, so spending 4 hours getting up to speed on the main essence of it seems like a small thing. While R is highly popular in finance coding, even if trends suggest that Python will become the one language to rule them all. Already have Tableau (as noted above), not that thats anything to do with coding; and Stata from my research. So can feel quite assured then that I'm pretty much up to speed on just about every type of base software for data science. (Matlab seems to have been ditched by nearly everyone except academia).

[Hitchhiker's Guide To...]

Making progress on the DataCamp 'Data Scientist with Python' track. Its 20 modules, and have six done in September, so will be finished at this rate around mid-November.

DataCamp have come up with something very smart. Very nicely delivered and designed. My path to date was the two intro uni of Toronto python courses followed by three applied data science courses from Michigan U. I'd highly recommend slotting this in the middle between Toronto and Michigan, as Michigan was far too advanced compared to the beginning skillset you would have at this stage.

Ugh. There will be nothing done on this until December. Working so many hours just to stand still at the moment and doing the French driving theory test has eaten up nearly every free hour. Was delaying it week-to-week but decided today it was best just to defer even thinking about doing more courses until December. Then it's nine glorious months of freedom and can hopefully become expert in everything machine learning.

[Hitchhiker's Guide To...]

So pretty much nothing done on this September-December. Just finished this short Excel VBA course in order to have a flavour of the language.

Kicking it all off again properly starting next week. Quite content with the 2017 progress though.

[Murdrum]

So pretty much nothing done on this September-December. Just finished this short Excel VBA course in order to have a flavour of the language.

Kicking it all off again properly starting next week. Quite content with the 2017 progress though.

That's good timing.

I literally popped in to check whether you recommended any VBA course as I need it for the modelling class and I have no experience with it.
I was told that because I can write SQL I should pick it up relatively quick but I'm not sure how true that is.

[Hitchhiker's Guide To...]

That's good timing.

I literally popped in to check whether you recommended any VBA course as I need it for the modelling class and I have no experience with it.
I was told that because I can write SQL I should pick it up relatively quick but I'm not sure how true that is.

It's actually very straightforward. That course gives all the basics, but you would need something extra to actually apply it in a finance context. A surprisingly nice language, given its Microsoft.

[Hitchhiker's Guide To...]

So a lot of progress on this.

1. Done about five more courses on DataCamp specialism, but it's speeding up rapidly, and the rest of the courses in their python for data science specialism will be done in next two weeks.

2. Invited to write a book chapter on potential for machine learning in fintech. That should add a nice bit of public perception of expertise, as it's going to be a very well received book.

3. Invited myself, and the editor accepted, to write a journal article in a very good academic journal on potential for machine learning in finance research.

4. There's a team in work out fundraising for a chair I'll run in machine learning and finance (chair is the French word for research centre). Think that'll get in a fair bit of cash to fund diligent PhDs and postdocs.

5. Proposed and got approved a new python module for finance masters in work, that means all my teaching next year will be on machine learning applications to finance. That's first module of its type in France as far as can see.

So this is all quite positive. Essentially by end of year I'll have a very strong public identity as a machine learning expert in finance, with CV to match.

The issue then becomes how to translate that into cash. A big move up in uni rankings would be the logical step as every business school will soon be realising they have to have an expert in this area, and there's almost no-one they can hire. But that doesn't quite feedback to the ultimate goal of having oodles of cash as academic pay has a natural limit.

And the main task though is to actually be expert in this area, rather than just appearing as one! Feb will see the DataCamp specialism completed, and a fair bit of work on datasets to build up core pandas knowledge (the building blocks of data science). Then March onwards is all about building up proper specialism in machine learning: the ultimate goal. The book chapter and article will be great in this regard as chance to understand where the area is and think about where it's going.

[Mellor]

I've nothing to add except I like that title
I'm not even sure what python is for

[Hitchhiker's Guide To...]

The new Wes McKinney 'Python for Data Analysis' is out now for all the pandas insights needed. Seems like a very good book. Going to work through it in February as well, as a lot will be known.

[Murdrum]

I started the Applied Data Science with Python from Michigan this week and I hope to have the VBA done by next week.
It's been suggested to make life easier to use them for 2 of the classes but you could struggle along with excel for both.
The only disappointing part is that neither will be examinable but it'll simply make the project work easier.
Semester 2 feels incredibly short, last week of classes finishes the 23rd of March

[Hitchhiker's Guide To...]

I started the Applied Data Science with Python from Michigan this week and I hope to have the VBA done by next week.
It's been suggested to make life easier to use them for 2 of the classes but you could struggle along with excel for both.
The only disappointing part is that neither will be examinable but it'll simply make the project work easier.
Semester 2 feels incredibly short, last week of classes finishes the 23rd of March

That's mad quick. What are you doing the dissertation on?

[Murdrum]

That's mad quick. What are you doing the dissertation on?

I'm hoping something in the area of prospect theory and physiological response.
They seem to think it's a good idea although it's a bit off topic so I'll know once the first draft of the research proposal is submitted.

I figure I'm not going to make any contribution in the area of finance by determining historical correlation of x stock etc plus it's not something that particularly interests me.
I don't expect to make any particular contribution either way but I do think this topic has far more scope for (dare I say it) a PhD.

Everybody is going to try cryptos I'm sure or work with Brian on his IPO stuff (which probably makes the most sense tbf).

I'll send you a copy of the research proposal when it's completed and get your thoughts, I know behavioural is your area.

[DeadParrot]

Hitch,

just a quick thing. What I have noticed in here is the python guys struggle with static offline data sources as python does the heavy lifting opening csvs etc.
Use a proper DB engine as a data source and performance is markedly improved.
I'm still level 0.1 in python but hoping to improve

[Hitchhiker's Guide To...]

I'm hoping something in the area of prospect theory and physiological response.
They seem to think it's a good idea although it's a bit off topic so I'll know once the first draft of the research proposal is submitted.

I figure I'm not going to make any contribution in the area of finance by determining historical correlation of x stock etc plus it's not something that particularly interests me.
I don't expect to make any particular contribution either way but I do think this topic has far more scope for (dare I say it) a PhD.

Everybody is going to try cryptos I'm sure or work with Brian on his IPO stuff (which probably makes the most sense tbf).

I'll send you a copy of the research proposal when it's completed and get your thoughts, I know behavioural is your area.

Yeah do that. I'm working on a prospect theory paper at the moment, nearly finished. I presume its the Indian guy who will be your supervisor so. Read the prospect theory paper he did with Brian.

Hitch,

just a quick thing. What I have noticed in here is the python guys struggle with static offline data sources as python does the heavy lifting opening csvs etc.
Use a proper DB engine as a data source and performance is markedly improved.
I'm still level 0.1 in python but hoping to improve

That very much matches with some things I've read, but then keep forgetting! Are you talking sql or something else like that, or something else altogether?

[DeadParrot]

Any proper DB engine MSSQL for me anyway

[Hitchhiker's Guide To...]

Cheers. Must look into that. Today's course is some basics around that idea - using sqlalchemy in python to read, extract, write to a range of sql dbs. But mainly sqlite.

[Elshambles]

Thinking of making python part of my time doing courses

What would you advise to someone coming at it from a fado fado coding viewpoint not a financial one?

Ta

[Hitchhiker's Guide To...]

Thinking of making python part of my time doing courses

What would you advise to someone coming at it from a fado fado coding viewpoint not a financial one?

Ta

Step 1.1: Have finished this coursera course: Learn to Program: The Fundamentals.

This was a brill course. Really well delivered. Went for the certified version (€46) as have seen on CVs a lot of people now including Coursera certified courses and those courses being accepted.

You start the course with nothing and end with great basic knowledge of how python works.

Definitely the above one, its an excellent introductory course thats taught very well.

[Elshambles]

Cheers!

[Hitchhiker's Guide To...]

Doing a course today on deep learning (where you create hidden layers after your main data to account for unknown interactions between variables). Have covered all the basics of deep learning before but it's genuinely mind-blowing the scope of the expansions in this area. It's almost like being given magic keys to understand things that no-one else knows.

Big plan for next month is to develop a Eurovision gambling model using a variety of machine learning techniques. Have wife involved too as she has tonnes of Python skills from her job and was surprisingly of the 'lets gamble everything' view. Will post all the key betting takeaways here and we'll get rich together.

Just about finished the 20-course Python specialism on DataCamp so a formal progress update to follow in a day or so.

[Hitchhiker's Guide To...]

So I finished the 20-course monster on data science on DataCamp today. Data Scientist with Python. It was excellent really, although I think some of those coursera academic courses are a good idea to do beforehand as most of it is just explaining how to do things, rather than the deeper meaning behind why you do things. (the latter being a natural focus of academics vs practitioners, and I think a better way of engraining real learning).

At this point its hard to see much more in core non-machine-learning data science that needs to be learned, and thats a big thing. There's a lot more in actual machine learning as thats an incredibly complex area.

The plan therefore:

March: spend 80 hours working on three projects to build good practical knowledge:

1. Have a project ongoing involving a survey that needs all UK professor email addresses. So am going to do something simple enough to automate the collection of these through a web scraping tool in Python. I'd imagine this will work out as quite a painful data gathering task, but good learning for future data gathering.

2. Have a work project involving standard finance data that I'd normally test in Stata (the go-to academic statistical testing software), but because of a few data manipulation difficulties am going to run everything through Python and build up a bit of parallel knowledge about how to do what I normally do in Stata in Python, plus benefit from actually being able to put the data in a more usable way.

3. The third project was mentioned yesterday and is a bit more fun. Going to use a range of machine learning techniques to develop a betting model for Eurovision. The initial plan is to wager around 2k on whatever is developed as long as the model looks reasonably competent. This is going to be a multi-stage ML model - voting hierarchical clustering to model voting tendancies, probably something like lasso regressions or deep learning to model songs, and maybe a bit of natural language processing of news coverage depending on availability. Time to get a bit of skin in the game!


April will then return to learning. First up is a few more coursera courses on the breadth of machine learning, going quite in-depth, then May will see a descent into the maths behind the modelling. This could easily take June too (and is probably an endless task). The maths seems vital as the actual testing of these models is becoming easier and easier, but very few of the newer people will have the slightest clue what the models they are applying actually mean. Will finish focused learning in July/August with some advanced machine learning courses. Any courses after that will be shortish courses to catch up on smaller additional learning needs.

That's 28 courses done so far, of varying sizes. Maybe another up-to-12 to round out the learning and its then time to consider things done really.

[Hitchhiker's Guide To...]

Google have made their machine learning course that most starting Googlers have to take available for free. I'd imagine it's quite good.

[Denny Crane]

What path would you recommend?

Fundamentals, crafting quality code, datacamp, and then Michigan?

[Hitchhiker's Guide To...]

You might find the fundamentals course a bit too basic, but I thought it set up the whole learning very nice. Crafting quality code isn't a great course, but it goes together with the first course.

Yep, DataCamp would be a decent next step then, as its short sharp courses that are very practical.

Not sure about the third step. Maybe Michigan, which are very good applied courses, but I'd say there might be a better third step that just am not aware of yet!

[Denny Crane]

Went to an into lecture for a data analytics course

*Tools will be pen paper calculator, Excel, SPSS and R(main lecturer doesn't know much Python)
*Highest marked project last year was a dashboard built in Excel
*There were a bunch of annoying older students asking a barrage of pointless questions


Strongly reconsidering my plan to give up several evenings a week to do it

[DeadParrot]

Google have made their machine learning course that most starting Googlers have to take available for free. I'd imagine it's quite good.

ugh...I was busy today as well...