Udacity Machine Learning Udacity Connect Lesson 01 Syllabus

In-person Udacity Connect Machine Learning Nanodegree syllabus

• Practice running python from within a Jupyter Notebook (FKA IPython Notebook).

• Become familiar with importing useful modules and packages, e.g. pandas, numpy, matplotlib.pyplot.

• Learn about the pandas data structures, including the Series and DataFrame objects.

• Create a DataFrame object from data in a comma-separated variable (csv) file using pandas.read_csv

• Index and select data from Series and DataFrame objects using loc and iloc

• Compute descriptive statistics on a Series or DataFrame, including the mean, the median, and the min & max

• Explore a public data set found on Kaggle

• Conduct some exploratory data analysis, and visualize trends in data using matplotlib

Pre Lesson Activities

• Student Handbook
• Class schedule and holidays
• It's an aggressive schedule
• Logistics
• Github repo

Lesson 1 in-person activity

• Meet classmates
• Meet and greet
• First lunch is provided. No free lunches in future sessions

Seven Extraordinary Startup Founder Stories in Tweetable Sizes

Seven Power Hustle Stories about Early Startups

Andrew Chen wrote in his recent essay the key to the future of growth is to execute (growth tactics) thoughtfully and iteratively. Ingenious moves of growth hacking could be luck, a sparkle, a clever thought or simply persistence. You can call it hustling, bootstrapping, hacking. There are some crazy stories. Paul Graham calls it do things that do not scale. Here are 11 founders who did the unthinkable at the dawn of their startups in byte-size stories:

1. Ben S. #founder of @pinterest used to sneak into Palo Alto Apple Store & change safari homepages to Pinterest until he’s kicked out. #hustle
2. Adora C. brushed teeth at McDonald’s to save $. Learned how to clean like a maid to found billion dollar HomeJoy on-demand cleaning service.
3. @stripe #founder brothers would personally implement Stripe on #YC batchmate’s computers to get other developers to try the product
4. @Codecademy never stopped pivoting at #YC its interactive #javascript tutorial wasn’t live until the night before #startup #demoday
5. #Reddit #founder once created fake accounts to boost user number in the early days of reddit
6. Founder of Muse #millennial #career site was flagged as a spammer on Gmail after sending out many cold emails in an effort to up user count
7. @Airbnb founders turned their loft apartment into 1st Airbnb listing to fund rent & the #startup. Ditched Craigslist for being “impersonal”

Comment and favorite if you want to read more byte-size hustle stories like these. Pss all the stories are perfectly tweetable.

Originally published on Medium

Machine Learning K Nearest Neighbors KNN Algorithm

On a high level, KNN tries to look for the most similar existing instances (defined by a chosen distance function), then retrieve the corresponding labels. Then using a voting method to decide the final label based on k votes. Voting mechanism could be a simple majority. May weigh instances unevenly. Commonly used distance function is euclidean e.g. as in sklearn. FYI: Euclidean is a special case of Minkowski with p=2

Can modify the weight parameter: uniform treat all neighbors equally, distance as measured in a specified distance function. Can customize func.

KNN	k nearest neigbhors to a given point
k	the number of neighbors
n	the number of data points, data is sorted to speed up the algorithm
distance	Euclidean distance shortest line connecting the points	OR a custom function that defines the distance
visualize	scatter plot, each point is a circle with a radius that include certain number of neighbors
KNN	a query intensive algorithm, not learning intensive
performance	big o notation, log(n) binary search, 1 is constant, n is linear
intuition	KNN stores all the data, then performs a binary search on the data when querying. Linear regression only stores the model y = mx+b. Key concepts: LEARN vs QUERY
		Running Time	Space
1 NN 1-nearest neigbhor, 1 dimensional list e.g. [1 2 4 7 8 9]	learning	1	n	KNN all data to storage without learning, so running time is 1 which means constant in Big O notation, and storage space is n for the number of data points
	query	log(n) binary search to find one point	1
K NN	learning	1	n
	query	log(n) + k binary search log(n) to find one point and the k items next to it in a sorted list	1
linear regression	learning	n	1
	query	1	1

11 Things You Can Do at the Crunchies 2017

Hot off the press, use your super powers and unleash your inner extrovert at the Crunchies award ceremony today in San Francisco! adapted from Medium

1. Meet famous founders, tech investors and journalists such as Mark Zuckerberg, Dave McClure and Ron Conway. Kungfu hustle for your budding tech product in after party open bar
2. Take fabulous runway pictures at this tech oscars on the green carpet
3. Get advice and mentorship from fellow founders, YCombinator 500 Startup alumni and tech workers
4. Find inspiration and actionables for your startup
5. Trend spotting and policy crunching.
6. Indulge in the sparkles and glamours of tech giants like Kevin of Instagram, Pinterest, Mark Zuckerberg of Facebook, and famous investors like Ron Conway. It’s hard to resist a selfie even if it is mildly impressing.
7. Take a selfie. Take lots of selfies.
8. Chuckle at occasional tech nerd humor squeezed into the ceremony by guest artists, hosts, comedians and even opera singers. Past guest artists include the Daily Show actors.
9. Drink and hustle. There is a bar with flowing champagnes and cocktails that can give you a boost and bring out your networking inner extrovert. Prep dinero, it ain’t no free ride.
10. This year’s nominee’s include SpaceX and Pokemon GO! Expect to see Elon Musk and the Niantic studio in the crowd?
11. Network with reporters from VentureBeat, Mashable, TechCrunch … for your baby startup?

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What is the Crunchies award ceremony?

Here’s a whimsical opera intro to Crunchies! Enjoy



In short it is the oscars for the tech community where all the startup and tech giants and inner circles meet. Like HBO’s Silicon Valley show? You will likely really enjoy this award ceremony. But if you are not huslting in tech, this event may be nothing more than a selfie opportunity.

Crunchies’ Past

In the past Crunchies, people arrived at the events in style in their fabulous outfits and with their tech gadgets. Travis of Uber had his pampered lap dog with him. People showed off their Google Glasses and Tesla in the past. This year, expect Snap to make a splash with their unique personality and Snap Spectacle glasses before their big IPO.

Past guest speakers included Mayor Ed Lee and Ron Conway “the Godfather of Silicon Valley”. They provide great insights for the future of the Silicon Valley.

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My Crunchies’ Past

Fun fact, I shared the stage with Mark Zuckerberg, Marissa Meyers and Kevin Systrom of Instagram at the 2012 Crunchies award ceremony. I was at 2 YC startups. Dave McClure 500 startup and I follow each other on Twitter :P

Growth Hacker Tool - Google Mobile Friendliness Online Tester

Building a website? Launching a startup? Make sure Google can find you and wants to find you. One key search metric is mobile responsiveness and friendliness. Use this official Google tool to test your website.
https://search.google.com/search-console/mobile-friendly

Machine Learning 101 Resources, Lessons and Tips

Thinking about getting started with Machine Learning? Silicon Vanity is your go-to resource on the Learn-to-Code movement #learntocode, tutorials and the hottest job trends in the Silicon Valley.  Educational tech is dear to my heart. Here are some resources to get you started.

• Udacity Machine Learning Nanodegree.
• Udacity Machine Learning Nanodegree Udacity Connect Intensive. An in-person, intensive bootcamp version fo the full Nanodegree. Github workbooks.
• Udacity Deep Learning Nanodegree. Created in collaboration with YCombinator startup founder / member Siraj, who has his own Machine Learning Youtube show. 
• Coursera Machine Learning
• Stanford Machine Learning course on Youtube
• Khan Academy Machine Learning
• Machine Learning competition on Kaggle. Kaggle is a great place fo datasets and competitions. Talking about competitions, Alibaba sponsored a customer flow competition on its Koubei product.
• Udacity Machine Learning notes, slides, forum, online one-on-one mentoring 
• Books Machine Learning for Dummies

The above resources are more academic than practical. Udacity has tried to marry practicality, industry requirement with academic coursework. The course is still in its early stage of becoming beginner friendly.

Is there a resource that you would recommend? Please share with me.