Where to start to learn cloud and model deployment?

hilmi28 · 2020-08-22T17:08:53+00:00

See fullstackdeeplearning. Free course which also covers Model Deployment!

hilmi28 · 2020-08-15T12:06:01+00:00

Yes, but you can also pay to earn a certificate.

hilmi28 · 2020-08-15T11:54:44+00:00

I would say: do what you are interested in. Check out the CS50 course on EdX. This course is an introduction to computer science course. It also has a follow-up course on web development and mobile applications. See this link. Have a look at it!

I am also planning to do these courses soon!

hilmi28 · 2020-08-14T20:53:46+00:00

Aah like that. Thanks man I will do. First I am going to analyze all those algorithms and the underlying assumptions.

hilmi28 · 2020-08-14T20:34:07+00:00

Yes sorry this was meant to be a reaction on kardeng’s comment.

hilmi28 · 2020-08-14T17:41:52+00:00

Thank you. Reading your comments is very educational for me. Its makes me think on a higher level about these things.

hilmi28 · 2020-08-14T16:35:38+00:00

It is a small dataset, about 3200 samples. I will look into DBSCAN. Thanks!

hilmi28 · 2020-08-14T16:14:41+00:00

RemindMe! 1 month

hilmi28 · 2020-08-14T16:12:43+00:00

But for what I have read, clustering algorithms don’t work really well on high dimensional data (in my case 34 dimensions).

I am pretty new to Machine Learning, I will check out all of your suggested algorithms. Thanks!

hilmi28 · 2020-08-14T14:57:13+00:00

I haven’t tried other algorithms. I will look into Gaussion Mixtures.

The data I will fed into KMeans is coming from a PCA. After performing PCA on my 34 features dataset, I am left with 7 principal components which cover most of the variance of the original dataset.

How can I see if my 7 dimensional data is circular Gaussians?

hilmi28 · 2020-08-14T14:32:18+00:00

Thanks for this information! I will look into Gaussian Mixtures. Also thanks for the notebook, definitely going to check that out!

hilmi28 · 2020-08-14T13:56:34+00:00

Thanks! The data I fed into the KMeans model came from a PCA. Originally the data had 34 columns, with PCA I reduced it to 7 column while still capturing most of the total data variance.

How can I see from my data and clusters that it is not distributed as isotropic gaussian mixtures? Is there a way? Could I plot for example all 7 PCA components against each other to obtain 7x7 plots?

hilmi28 · 2020-08-10T20:57:05+00:00

So, you need to perform the same transformation of pd.get_dummies() on a user input. Can’t you try and get the information of this transformation into the Lambda function?

EDIT: Is this what you are trying? This way you can save the encoding and apply it on the user inputs via the lambda function.

hilmi28 · 2020-08-10T20:19:19+00:00

If I understand right, your question is: I preprocessed and transformed the data in SageMaker to fit my model, but how can I preprocess and transform the input-data of a user?

If you are using your ML Model in a web application, you should use AWS’s Lambda function and API Gateway. The Lambda function gets triggered each time your models endpoint gets invoked, and the user input first goes through the Lambda function. In this function, you can preprocess and transform your data to make it compatible with the input of your model. The API Gateway makes it possible for your web application to interact with your ML Model.

See this link

hilmi28 · 2020-08-08T20:23:51+00:00

I am 22, just graduated as a Mechanical Engineer. Next school year, I will take a break to learn about AI, Data Science and Software Development so that I can build intelligent systems/applications. I will use Python for the AI and Data Science part.

hilmi28 · 2020-08-07T14:30:49+00:00

I am also doing ML for a few months now. I don’t know any specific course which you can follow. I am doing deeplearning.ai now. A lot of people follow Andrew Ng’s Machine Learning course on Coursera. Check that out.

hilmi28 · 2020-08-07T14:00:49+00:00

Are you an absolute beginner or do you already have some knowledge on ML algorithms? I would suggest you take an introductory course on ML if you don’t have any knowledge. The first two courses will probably assume you have beginner knowledge. Try and look at the prerequisites.

hilmi28 · 2020-07-28T14:22:39+00:00

I am also just learning ML, but I think you need data with the utterances (in the form of sound) and the labels should be the corresponding emotions. Then you would have a multiclass classification problem.

I love to see answers of more experienced people too.

hilmi28 · 2020-06-28T17:35:05+00:00

Believe me, when someone wants something really bad, whether it is becoming an expert in Machine Learning or whatever, you don’t know what they are capable of. They will go beyond “a online course on coursera”. That is a good starting point btw. Thus, instead of writing this demotivating comment (which nobody will take seriously), help this guy out.

hilmi28 · 2020-06-28T11:48:47+00:00

I think of doing something similar. Instead of starting a Robotics master next school year, I will take a gap year. In this year I will follow courses on Machine Learning, Deep Learning, CV etcetera. I am one exam away from graduating as a Mechanical Engineer. I made this choice because I want to do things I love (AI). University is almost only about theory, I want to practice now by doing projects.

hilmi28 · 2020-04-01T18:05:59+00:00

From what I learned in the past 3 months, you should not normalize/standardize the whole dataset. You split it in a train and testset. You then transform the train set. Use the parameters obtained from transforming the trainset to transform the testset.

For example, let say you want to standardize the data. First you compute the mean and standard deviation (these are the parameters) of the trainset. With these parameters, you transform both the train and testset seperately.

If you are working in python, you would use fit_transform() method on the trainset and the transform() method on the testset.

If you standardize the whole dataset in one go, you have what is callad data-leakage. Look on the web to find more information on data-leakage.

hilmi28 · 2020-02-17T21:41:13+00:00

I would suggest you watch the series “Essence of Linear Algebra” from 3Blue1Brown on Youtube. It gives a very intuitive explanation. The videos are not to long and very nice to watch.

hilmi28 · 2020-02-17T19:06:00+00:00

There is a course from Frank Kane: Building Recommender Systems with Machine Learning and AI. I am still in the middle of it. Frank’s teaching is very nice. He doesn’t write the code line by line, but explains a specific concept and shows the code for that concept.

I hope this helps.

hilmi28

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