holy god, Ive been looking whole KZbin and torrent to find a best package for learning machine learning and I watched many videos, but believe me I am totally in love with your course, this is awesome, you explain it so simple it is like you teach me in my mother tongue. i wish you best like my man.
@medupidups47415 жыл бұрын
Me too this is clear and straight to the point
@EmettSpeer8 жыл бұрын
I love your series and wish you would put out videos more often.
@JBGordon8 жыл бұрын
+Emett Speer Thanks so much! Episode #4 is live: kzbin.info/www/bejne/bmXKooaYgch1fKc
@ThereIsNoSpoon6784 жыл бұрын
Everyone complaining and wanting more episodes. Here I am in 2020 enjoying all 10 of them so far.
@sephirothjc8 жыл бұрын
I have limited knowledge of programming and computer science, yet I find this series very approachable and fun.
@giantneuralnetwork8 жыл бұрын
Loved the stacked histogram, nice way to visualize the different means of the distributions!
@mitchese18 жыл бұрын
This is a really great series, please publish more often/sooner!
@Technologynorth8 жыл бұрын
Really enjoying this series. The examples are quality, and you can tell you have put a lot of thought into them. Thanks for making the subject clear and interesting in each episode.
@Abdullah-mg5zl6 жыл бұрын
*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!
@alxleiva8 жыл бұрын
WE WANT MORE EPISODES!!!!
@ErnestGWilsonII7 жыл бұрын
Agreed
@net8 жыл бұрын
Patiently awaiting the next episode in two weeks!
@sanjayakumarsahoo22938 жыл бұрын
Awesome series, prevents myth that machine learning is difficult
@philipsalvo8 жыл бұрын
Josh, Thank you so much to you and your team for building this series! In particular, I really like your 'tl;dr' approach and keeping things grounded in accessible, real-world examples -- I can't wait to see what comes next!
@arkrou8 жыл бұрын
For feature selection: is having no individual prediction power (in your example, eyes) enough to tell whether those features have no value at all? Could they not have some non-linear joint predictive power with some other features?
@codingwithjoyk8 жыл бұрын
Very cool series & I appreciate the links to the examples and especially the "article that inspired". Extra links like that really help! Thank you!
@gbhall8 жыл бұрын
I could honestly watch Josh all day. He presents really well. Keep up the good quality content Josh! :)
@jamiequigley59066 жыл бұрын
Creepy smiles at the end of each sentence, "Smile MORE Josh" Marketing bellows!
@michaelwhitlock73984 жыл бұрын
Now I can't unsee it
@andreasparasian72254 жыл бұрын
Ik right, I mean yea this is very informational but he doesnt have to act like he is inhuman or sth...
@MattSiegel8 жыл бұрын
terrific episode! those heuristics for feature selection are invaluable. also, lol @ whoever produces the graphics: the frontmost dog *head tilt* XD
@EddieImada308 жыл бұрын
Very good explanation! Looking forward for new episodes!
@luisleal41698 жыл бұрын
How do you use categorycal features, for example, we are trying to train a classifier and one of the features is "State"(or maybe "city"), do you create a mapping table where every state(or city) gets a numerical representation? Or would you solve this at programming level: looping through the states(or cities) and finding a classifier for every state(or city) ?
@HubertRozmarynowski5 жыл бұрын
How to get that pretty data visualisation from matplotlib like in 2:05 ?
@diegolima20988 жыл бұрын
I just want you to know that I loved the article reference in this video. Please refer to more nice articles like this.
@nandishajani6 жыл бұрын
I tried the same code but the graph is looking very ugly. There are no spaces between the bars. Can anyone please help?
@mrtnsnp8 жыл бұрын
I enjoy the series. Is it possible to provide the source code (and possibly data if not included in sklearn) so we can follow along and experiment while watching?
@JBGordon8 жыл бұрын
+Maarten Sneep Good idea! I've been meaning to, but haven't found cycles yet. In the mean time, I'm trying to keep the code nice and short (~20 lines or less).
@alsonyap8 жыл бұрын
Great job on the video! I can tell that you have taken feedback from previous videos and made adjustments. Thanks for the effort! Will be waiting for the next episode :)
@DerekEskens8 жыл бұрын
Great series so far. Considering independent features, for something like a dog, would capturing both height and weight be counter-productive since they are most likely interrelated?
@amomasi99095 жыл бұрын
This was incredibly useful, Josh. Thanks.
@ameynaik27436 жыл бұрын
Wasn't this episode a bit inconclusive? Does anyone know the next episode in this series which discusses about the features?
@tkmallik868 жыл бұрын
Awesome videos, probably the best ML course by far
@dipalimalviya69217 жыл бұрын
hey Josh! I wanted to learn concepts and standard algorithms of machine learning ,please suggest me how i can do this?It will be so helpful. I also want more tutorial of this series.Thanks for this 9 episodes series
@sukantakr12696 жыл бұрын
Dipali Malviya you can watch prof Andrew's ml tutorial! very very helpful to learn algos.. I have also been some ppts made by our one ml research and developer prof!
@KelvinKagia8 жыл бұрын
so awesome i think machine learning is the easiest topic i have ever come across having a good background in programming,probabbility and statistics
@asiddiqi1238 жыл бұрын
Is it possible that we don't know about what features lie in Data and we do some processing and find features needed?
@deevioo8 жыл бұрын
What about ratio between height of a dog and the width of its head?
@newcoolvid277 жыл бұрын
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.
@brahmlife6 жыл бұрын
can you have sub features of features in your decision tree algorithm?
@dzxxbj8 жыл бұрын
Are there viedos like this tutorial's type?
@rjankie8 жыл бұрын
like the way you are (story)telling about this subject. Makes it accessible for many People. looking forward to "future episodes". ;-)
@abdulrahmanalotaibiq88 жыл бұрын
keep up the good work. Would you talk about image classification but not the simple one that's in the tutorial, please?
@JBGordon8 жыл бұрын
+Abdulrahman Alotaibi Definitely planning on that down the road when we get to TensorFlow. Their tutorials are great btw! www.tensorflow.org/versions/r0.8/tutorials/index.html
@abdulrahmanalotaibiq88 жыл бұрын
Awesome and thanks for the reply. I really wanted to know how to preprocess the images before feeding them to the classifier. Every tutorial out there uses the classic MINST and I wish that use guys use something different. Thanks again and you are doing a great job.
@giantneuralnetwork8 жыл бұрын
+Abdulrahman Alotaibi I'd checkout open-cv for image preprocessing. Great idea to distort/translate/skew/scale the training images to get a better general predictor!
@diwashshrestha49967 жыл бұрын
It great easy to understand series .we want more.
@dannyhunn60257 жыл бұрын
Couldn't you use Latitude and Longitude to find Euclidean distance?
@xsyed8 жыл бұрын
Waiting for more of this kind of stuff!!
@TTarfreak8 жыл бұрын
This is explained in such a simple and practical way.Loving this series! :)
@MrHuno928 жыл бұрын
Is there a good read available to elaborate on the topic or follow the tutorial?
@JBGordon8 жыл бұрын
+60SecondsTech Good question. I haven't done a write up (maybe will do in the future, that's a good idea). For an in depth treatment, Prof Andrew Ng’s course is an incredible resource: kzbin.info/www/bejne/i6vbip-YgJeWbac
@AviPars6 жыл бұрын
mine doesn't overlap , is that normal?
@rajnishrajput61217 жыл бұрын
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.
@bhavykhatri26696 жыл бұрын
His smile is so motivating.
@mycount647 жыл бұрын
simplified classifiers OK. what if feature ratio's are significant or their are sorting stations for the letters with varied release times for the letters... eh just confusing things. will the ML program figure out relationships between features if it is supervised or is it our job to figure out the relationships. Actually isn't the problem that if we have a system with 2500 features and do not know how they are significant then ml will figure it out?
@sadiqsariq80375 жыл бұрын
what is the difference between np.random.random() and np.random.randn() ?
@pitbbe8 жыл бұрын
Awesome Series! please make more videos!
@Hieuiph8 жыл бұрын
thanks !!!! I hope you will explain in the future the "feature engineer" technique.
@dzxxbj8 жыл бұрын
These videos are very helpful. Through them I know how to code and understand happily. How can I pass the gap between Little Code and real world problem?Hope for topics about this:)
@fatihturgel8 жыл бұрын
Great work! Really love your series! :)
@theburntcrumpet83717 жыл бұрын
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.
@DragoonGalaxy78 жыл бұрын
what's the program you use to write the python code?
@titas34197 жыл бұрын
Sublime Text
@RM-lw2qi6 жыл бұрын
What does the greyhound = 500 line actually do?
@shoun41486 жыл бұрын
Juts makes the variable greyhound equals to a value of integer 500
@husainzafar16488 жыл бұрын
I think np.random.randn is not restricted in range (-1,1). So the error wouldn't be just +/- 4%. Btw excellent videos and loving your style of explaining!!
@bimDe20247 жыл бұрын
Complex in easiest words, thanks a lot
@amomasi99095 жыл бұрын
#Hi guys. Code that works now so you can follow: import numpy as np import matplotlib.pyplot as plt greyhounds = 500 labs = 500 grey_height = 28 + 4 * np.random.randn(greyhounds) lab_height = 24 + 4 * np.random.randn(labs) plt.hist([grey_height, lab_height], stacked=True, color=['r', 'b']) plt.show
@Warri0r19928 жыл бұрын
Hey Josh! I'm studying machine learning in my University since a couple of years, and i've found your tutorials very useful, altough i already know those things! The concepts are very clearly teached! Python looks really easy to understand, instead of R, for istance. Also, i'm looking for blogs or web sites to keep myself always updated on ML topics....do you have some advices? Keep it up, i can't wait for another video!
@JBGordon8 жыл бұрын
+Warri0r1992 Thanks! My favorite blog is colah.github.io - it's a pretty incredible explanation of how neural networks work, and some assorted other topics.
@TheIsrraaa8 жыл бұрын
Analytics Vydhia at fb or web
@MattieCooper100007 жыл бұрын
Exactly what I needed! Thank you so Much! Love your presentation!
@stickmouse50026 жыл бұрын
how does training work, my program marks an apple as an orange
@akansha.da1iiitmk.ac.inaka2907 жыл бұрын
Doing it on Python 3? Don't want to pause the video and write? Find the code here: github.com/akanshajainn/Machine-Learning---Google-Developers
@tekki.dev.5 жыл бұрын
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...
@andrescolon8 жыл бұрын
Good job on these videos!
@user-ny8xt1ui3h4 жыл бұрын
Wonderful Series of ML! Someone recommends me another one? please
@MuammarElKhatib8 жыл бұрын
Very clear explanation.
@vivasjimmy8 жыл бұрын
we want more of these videos
@gaurav98395 жыл бұрын
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 ...
@HighNorthStudios8 жыл бұрын
These vids are great! Thank you and keep it up!
@SirajRaval8 жыл бұрын
Hell yeah! If you guys like machine learning check out my new ML series on my channel.
@Cupofshake6 жыл бұрын
The graph doesn't work, only gets this message.
@ErnestGWilsonII7 жыл бұрын
# 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()
@dolomikal8 жыл бұрын
A bit light on the info here I think. Compared to the last episode.
@Unhacker5 жыл бұрын
I loved it, but my dog would prefer a squirrel detection algorithm.
@harendrasingh_226 жыл бұрын
This is an awesome series ! The best thing ever in ML :P (Well not "the best" , but yeah ! ) !
@JeffWeakley8 жыл бұрын
Big Fan. thanks Josh. Much appreciated.
@JeromeEtienne8 жыл бұрын
So clear! Thanks
@TheTruthFadeswithTime286 жыл бұрын
why does my bar graph look way worse aesthetically than yours?
@andrecastro11796 жыл бұрын
mine too, probably because of his magic Mac.
@avinashravi46066 жыл бұрын
where i can Learn deeper about ML algorithm with statistics
@fpspsyduck8 жыл бұрын
great video!
@vladvlog82618 жыл бұрын
Thanks for the video. Really appreciate it!
@jinxblaze7 жыл бұрын
Wouldn't Latitude and Longitude give you accurate distances by some very simple calculations ?
@riverland00727 жыл бұрын
yes, but who has time for simple calculations when we have simpler distances...now you just made me try to remember log and lat maths from high school..it was not that simple
@egor.okhterov8 жыл бұрын
Amazing. I understand everything perfectly :)
@twmicrosheep8 жыл бұрын
Keep up the great work!
@MamunSuper8 жыл бұрын
awesome lectures...)
@JackalovichMokov7 жыл бұрын
has anyone else noticed that he never blinks
@editmergedx65416 жыл бұрын
5:00
@sapnaroy63736 жыл бұрын
At 2:16
@Dynamite_mohit4 жыл бұрын
Thankyou
@ElVerdaderoAbejorro7 жыл бұрын
I have created a github repo with all of the code for all of the recipes of this series. I've used Python3 for all recipes. I've also updated all of the libraries and have added some things to the code here and there. Check it out: github.com/TheCoinTosser/MachineLearningGoogleSeries
@dude22607 жыл бұрын
when the max height is 28 + 4 where does 35 comes from
@jeremyheminger68827 жыл бұрын
Yeah, I agree, its sketchy. I fed it print(10 + 4 * np.random.randn(10) + 4) to generate numbers only above 10 and less 18 but I occasionally am getting numbers like 9.6... and 18.5... [ 9.65793498 13.18266908 8.06937322 10.59406851 11.29406986 11.14891777 11.93107746 13.25606161 18.58717831 8.54857054] How does this function work?
@akarshrastogi36827 жыл бұрын
The decision tree that he follows to decide when and how often he SMILES creeps me out..! 1:06