The 7 steps of machine learning
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- เผยแพร่เมื่อ 1 มิ.ย. 2024
- How can we tell if a drink is beer or wine? Machine learning, of course! In this episode of Cloud AI Adventures, Yufeng walks through the 7 steps involved in applied machine learning.
The 7 Steps of Machine Learning article: goo.gl/XEo6i2
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is this a scam ?
Is Google a scam? No!
@@dazail5474 No, but it's misleading. You don't get $300. You get a free trial of some features.
I like indices!!!
1:49 | Gathering Data
2:21 | Preparing Data
4:03 | Model Selection
4:30 | Training
6:46 | Evaluation
7:24 | Parameter Tuning
8:55 | Prediction
you have skill man
Thank you
I’d love to hang out some time
awesome
9:49
Bah I never even finished the first step when I tried to replicate this. I got back from the store with the beer and wine and everything just went downhill from there.
I managed to gather up an impressive amount of data before I threw up all over it.
Data ingestion one'd say
you are a master!😂
Creating quality data is tough! New Software like Diffgram can help! diffgram.readme.io/docs/video-introduction
Or uphill 😏
I'm amazed how a complex topic could be explained seamlessly!. Great video.
its not complex, checkout Andrew Ng - AI For Everyone course
I love how he explained the steps of Machine Learning in simplified plain english. thank you very much!!
What an amazing depiction of ML steps. Very very nicely put! Thank you so much Yufeng !!
1. DATA COLLECTION/GATHERING:
+Collect features. e.g.:
1. Alcohol concentration.=>Hydrometer.
2. Color.=>Spectrometer.
+High quantity & quality of data needed.
2. DATA PREPARATION:
+Randomization.
+Visualizations.
+Data split: training+testing/evaluation.
3. CHOOSING A MODEL:
+Among many in the community today. e.g. tensor flow.
4. TRAINING MODEL:
+Example: y=m*x+b.
The only values I can adjust/train are: m & b.
+In machine learning, there many m's since there are many features. +These m's are denoted using a matrix referred 2 as w(weights).
+The b's are organized into another couple matrix referred 2 as b(biases).
+After training once & getting a prediction, adjust the weights, w & biases, b.
5. EVALUATION:
+Test model against data that has never been used 4 training.
+Representative of how the model would perform in real world.
+Great split ratio example: 80% training & 20% evaluation.
6. PARAMETER TUNING:
+Example of such a params:
1. The no. of epochs; the number of passes of the entire training dataset the machine learning algorithm has completed .
2. Learning rate; how far we shift the line of y=m*x+y in each step.
+The parameters are referred 2 as the HYPERPARAMETERS.
+Tuning is more of an art than a science. i.e. it's an experimental process depending on the specifics of:
1. My dataset.
2. Model.
3. Training process.
7. PREDICTION:
+Doing sth useful, for example, in this case answering the question on whether it's bear or wine.
Unlike 99% of youtubers and online lecturers this guy did not cut the video at all. One shot 10 min video
Thank you, I am new to the IT industry and I found your explanation very easy to digest especially from a lay person's pov
Finally!! I found a place to start my ML journey!! Looking forward to the future videos👍🏻
Having just studied machine learning and coding for it, this is a great, simple and logical explanation in common conversation. Well done!
Hii., Can you please guide me for ML learning... I am a core engineer and changed my career in Data analytics recently and wanna learn this
@@guruprasath765 checkout Andrew Ng
@@RR-et6zp Thanks.. I will check it out 👍🏽
I have watched many videos now, and this was the best for AI beginners IMO. Thank you!
This is the most clearly entry point for Learning Machine I ever seen..
Great stuff! Really looking forward to more of your videos.
Excellent presentation...he got me when he said, "...don't worry, you can't break the site." Game on!!
*quick summary:*
- machine learning is all about seeing some examples of input-output pairs and then being able to predict the output for new inputs
- basically, you feed a bunch of examples to a machine, and the machine will start to learn about the defining characteristics of your examples
- therefore, it is extremely import that you feed it good examples! Generally, the more examples the better, but you also want your examples to have the distinguishing features in them.
- once you gather some good examples (with distinguishing features), you generally clean it up, plot it, do some statistical analysis, etc
- then you choose one of the many different machine learning models (e.g. linear, neural network, etc). Each has its pros/cons. Depending on your examples, and your time constraints, you will pick one of these models
- you will then tune some parameters of the model (again how you do this depends on your examples and time constraints)
Hope that was helpful!
Thanks for the awesome video :)
All.My.phones.need.to.be.fix
this video is so simple yet so informative. good job Yufeng and google!
You've done a great job here with the explanation of the processes of building a ML model. So clear, easy to understand and quite helpful to even someone without prior knowledge of ML. 👏
It is great lecture and explains the topic very clearly and simply.i will follow all the videos because comparing to other programs and books this the most clear videos I’ve seen so far
this is good start... thankyou very much... m new to ML... its actually gonna help me in my project
Great overview, Yufeng!
Great vid! Love this series.
Very informative video. Thanks for explaining a fairly complicated subject in a simple explanation that makes sense
By far the best starter video I've seen - and I've seen quite a few!
Awsm explanation YufengG...thanks for this video..
The quality and quantity of data you collect shows how good your model can be. 👌
Great way of explaining such a sophisticated topic! Good job!
Simple and clear explanation on machine learning process, thanks yufeng!
This is the best video that ever explain to me how and why there are training and testing datasets. Great Great Job!!!
for people who are looking to get into deep-learning. take a look a tensorflow, it is a library for python and it makes designing and training a neural net very easy. i am 13 and even i have made a speech-recognition algorithm for my AI-assistant (much like google home)
Death
Bike Vids say all you want. At the end of the day, i make a good wage with it. Your hate isnt going to do anything :) p.s. python isnt the only language I know, i also know C#, Javascript and Ruby
Bike Vids python s future
Don't forget that 13yr-olds are more intelligent than people growing up in the not so rich computationally earlier world.
i'm 10 years old and im designing skynet .
Very clearly explained! Thank you so much Mr. Guo!
Very good explanation and elaboration. I like this kind of demo where there is a direct elaboration of the topics unlike other video tutorial difficult to understand beside of the accent of language.
This video is so fantastic!!! I love the simplicity of the explaination to the complex content. Great!
Thaaank you for the clear explanation. The only video series that i can follow as a beginner
Extremely brilliant. Thank you google and yu Feng for the awesome stuff
whoa great explanation, i want a full course from you !!!
dude ?..
good luck in your journey mate, ignore people like SUNDAR B, you can see his profile pic, he would prefer gobar over AI
The y axis in the red orange plot shows the percentage of accuracy of the prediciton for variable y.
Yufeng, thanks so much - interesting, fun, and funny :)
you are an amazing teacher yufeng!! thank you vey much!! very clear! it was a real pleasure to listen to you!!
You are a truly subject’s expert and teacher. Born to transfer knowledge and to explain. Chapeau 👏🎓
This is great, so clear and precise! Keep uploading please
Great way to explain the important steps for ML.
Another great video. Clear and effective communication.
Really cool all the explanation! Really love this series! XD
For this use case Chemometrics approach is best I think. Would be nice to relate images, spectral signatures and have that for training, test and validation dataset. This would mean of course working not just tabulated data but the fusion of images, spectral data and lab measurement data
Great explanation!
Perfectly explained. Thank you very much
I am using this same learning technique to learn about machine learning, by watching many videos about it then seeing what I can understand about similar ideas talked about by different people
great explanation . hoping for a full course from you
th-cam.com/video/0J_mA-P7Zjg/w-d-xo.html
This is a very underrated video. keep up the good work!!
As ever from you guys - wonderful. Thank you.
Thank you so much ! you really helped me a lot understand the whole process
Yup.. will be really easy for us to buy alcohol, right? I have been selecting my wines and beers through this process.. really helps!
@@chilarmah lmao
:O good video ! It has been a great summary for only ten minutes.
Thanks, I will share it with those friends that ask for how neural networks works without technical details
Great presentation , can we get a presentation about neural network in future ,Many Thanks.
Amazing and well explained the complex subject in a simple way for those who are new Beebe.
Thank you for sharing
Seven steps of machine learning:
1. Gathering Data;
2. Preparing that Data;
3. Choosing a Model;
4. Training;
5. Evaluation;
6. Hyperparameter Tuning;
7. Prediction.
In my previous jobs, normally the data are gathered already. I need to clear data, link tables, and choose a model ...
Thank you, Yufeng Guo!
Great video which someone like me who has no machine learning background can understand very clearly. Hats off!
Awesome presentation! Clean, short and sweet.
Thank u for such a nice video, it clear my basic concept.
Great info, thank you for sharing!
This is simple and straightforward. Thanks
Very nice presentation
Very well done video, well ordered and well explained. Thank you!
I find this video very appealing. Explaining concept with examples is really good.
AWESOMELY well explained !!!
Great presentation, loved it.
Giving me, a maching learning beginer, a great simple start. Thanks.
Hey how good are you now?
Great content, easy to follow. Many Thanks
Thank you for this amazing video really appreciated
First watching the video I couldn't stop watching his gestures. After 20 minutes I got it
Very interesting. How would you handle situations where datapoints from two different categories overlap? A white wine that is close in colour and alcohol content to a white ale? Also, the model you describe is a linear split between the categories. But is that always the case?
This is great! Keeps the videos coming please
Excellent overview and great example.
Well defined and in a nutshell
7 ingredients to ML. Thanks.
Wow input model and output . If output is acceptable then fine if not feedback to obtain right answer. Explained nicely...great to visit this channel .
Great video! I my opinion data preparation is the one of the most important thing in Machine Learning.
So fluent ! Thank you :)
Awesome dude
Great video, thanks!
wow!!!great explaination....
Very nicely described. Thanks.
Nice video Yufeng,
you could use a polarizer filter to reduce reflection from your glasses
Good teacher and good illustration with pictorials-especially with the equation.
Glad it was helpful!
well done! cheers
That was very clear thank you
Very insightful, thanks
‘Don’t worry, you can’t break the site’! How sweet is that?! 😅
Very informative, thank you 😊
Simply, the best !
A very good video, i understood easily .. thank you brother
thanks for the nice video
as you said , there are a lot of available models , where to get those models for numerical and linear problems for free or paid ?
Brilliantly explained
Good work... willing to learn more
I've got a question: How does the evaluation step work? Do I have to tell the machine whether the prediction was right or wrong and then adjust the parameters till I get a better prediction? But in how far would this be machine learning since I'm the one who adjusts the parameters to get better results?
Does the process remain the same when the data is already available? For example, where does data exploration and feature creation comes in to play ? Will it be part of data preparation stage to ensure that we have the right features before we start the training
So what I struggled with in practice was getting evaluation data. I initially split my data set, but after 2 or 3 training cycles I had no evaluation data left and still thought my parameters could use some better tuning.
What do I do in such a case? I feel like I just done goofed, have to wait for more data to come in (time constraints= not an option) or take what I got for my predictions. As soon as I'm love with predictions I also don't get more data,that I could later use to further tune.
Great explain with the good examples
th-cam.com/video/0J_mA-P7Zjg/w-d-xo.html
Very good video. Gave me good introduction and understanding of Machine Learning.
Excellent presentation and pronunciation!
Thank you great video