What Makes a Good Feature? - Machine Learning Recipes #3

# Import Numpy
import numpy as np

# Import matplotlib
import matplotlib.pyplot as plt

# 500 of each dog
greyhounds = 500
labs = 500

# Set the dog heights +/- 4" randomly
grey_height = 28 + 4 * np.random.randn(greyhounds)
lab_height = 24 + 4 * np.random.randn(labs)

# Plot
plt.hist([grey_height, lab_height], stacked=True, color=['r', 'b'])

# Launch the results in a window
plt.show()

1waawww

If you're using spyder and want a new window to show the plot, [Tools > Preferences > IPython console > Graphics > Graphics backend > Backend: Automatic] then restart Spyder.

has anyone else noticed that he never blinks

So if i want to make a program that identify all the dogs in the world,
I have to store all the data of all dogs in the world?
like height, weight, speed hair etc.

Exactly what I needed! Thank you so Much! Love your presentation!

Couldn't you use Latitude and Longitude to find Euclidean distance?

Complex in easiest words, thanks a lot

I'm a bit late to the party here but I'd just like to say thanks to the Google Developer's channel for putting these videos out there.

This guy is definitely a robot

this episodes are making me only dumber

His smile is so motivating.

Creepy smiles at the end of each sentence, "Smile MORE Josh" Marketing bellows!

seriously google...you hired this guy to explain your video...he can hardly keep from smiling lolololololol google you are some kind of pranksters...better to use FEET who are you kidding??????

What does the greyhound = 500 line actually do?

Thank you

can you have sub features of features in your decision tree algorithm?

why does my bar graph look way worse aesthetically than yours?

mine doesn't overlap , is that normal?

better remove inches

This is an awesome series ! The best thing ever in ML :P (Well not "the best" , but yeah ! ) !

I wish i could look so happy ^^

I tried the same code but the graph is looking very ugly. There are no spaces between the bars. Can anyone please help?

Wasn't this episode a bit inconclusive? Does anyone know the next episode in this series which discusses about the features?

quick summary of the video:

- let's say that your goal is develop a program that can distinguish between two breeds of dogs

- what features do you want your example data to have?
- you want the features to be the "distinguishing" features between the breeds, i.e. features that are very different between the two dog breeds
- for example, if the two dog breeds tend to have very different heights, you want to use height as a feature in your training data
- if on the other hand, the two dogs have about the same distribution of eye colors, you don't want to use eye color as a feature
- you also don't want to use features that are highly correlated (i.e. that don't bring in new information)
- you want to use simple features, as simple features will require less examples to get a decent classifier

- you wanna be careful about adding too many features, especially if the features are not "distinguishing" features, they may just by chance be distinguishing in your example data, thus your classifier will start basing its predictions based on these faulty features

key thing to take away from the video:
Selecting features is extremely important. Select the simple, distinguishing features, that bring in new information (i.e. that aren't highly correlated).

Thanks so much for these videos!

where i can Learn deeper about ML algorithm with statistics

how does training work, my program marks an apple as an orange

The graph doesn't work, only gets <Figure size 640x480 with 1 Axes> this message.

Dem doggos.

。。

what is the difference between np.random.random() and np.random.randn() ?

I loved it, but my dog would prefer a squirrel detection algorithm.

im trying to learn two things at once here, python and machine learning, but i guess its not too hard as i already know c#,php etc...
ML is also not very hard at first but gets little complicated as you go deep...

This was incredibly useful, Josh. Thanks.

What if I don't what is dog and I need to identity it ... My program is just a toddler and it is learning from Internet ...

Everyone complaining and wanting more episodes. Here I am in 2020 enjoying all 10 of them so far.

Wonderful Series of ML! Someone recommends me another one? please

Thankyou

Awesome👏✊👍

Anyone else bored at home?

Thanks for good explication💖👆💖🎓🎓🎓👍👍👍⛅⛅⛅🌴🌴🌴🎆🎆🎆