Machine Learning and Deep Learning. Looking for advice and peers | Coursera Community
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Machine Learning and Deep Learning. Looking for advice and peers


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Hi everybody,

I am look for advice and peer for the jurney I am starting to take. Within 3/4 years I would like to become proficient in Machine Learning and Deep Learning, therefore surfing the cousera site, I have found interesting opportunity to let me move along this path.

Here's the list:
  1. Python for Everybody Specialization
  2. Applied Data Science with Python Specialization
  3. Mathematics for Machine Learning Specialization
  4. Machine Learning Specialization
  5. Deep Learning Specialization
The five above I think are compulsory for the jurney I want to take.

The following could be considered optional:

  1. Data Science Specialization - Johns Hopkins University
What do you think?

Is there someone interested in this journey? We can get in touch and help each other.

Thanks for your time.

6 replies

Userlevel 3
Badge +1
Hi @AlexIca82

Firstly congrats to you for choosing a great platform.

Let me quickly share my opinion on your post. In order to be proficient in Ml & DL the first and foremost thing is we need to be perfect in basics. Basics include Statistics, little bit Maths then followed by programming like Python and R.

Once you are familiar with the above subjects then you are good to move to learn ML later DL. this would be the perfect curriculum in order to be your journey successful.

I believe the below list would be the right path:
1: Basic Statistics/Probability and Statistics
2: Introduction to Data Science
3: Applied Data Science with Python
4: R Programming (Only if you are interested. Otherwise🙂)
5: Machine Learning(Stanford University)
6: Deep Learning.ai


Currently i am doing my ML course from Stanford. Let me know if you required any help/ info on the same.

Have a great week!
Cheers.
Userlevel 3
Badge +4
I started Machine Learning with Machine learning course from Stanford University and then completed Deeplearning.ai specialization. I felt this as a good way to start. You will get strong foundations in Stanford's Machine Learning course. Deeplearning.ai will give you a better idea of deeplearning and you will learn to implement the deeplearning models. For learning python the best way is to learn by creating the deeplearning models. Just you need to know the basics of python to start creating models. Explore documentations and examples of tesorflow or the specific library you want to use in your model. The tensorflow specialization is recently launched by deeplearning.ai. It can help you improve implementations of machine learning models. I am now doing Advanced machine learning specialization to learn creating production ready models.
Userlevel 7
Badge +12
Hi @AlexIca82. Great post! To add to the helpful responses here, I wanted to recommend this recent post: Programming for Everybody. You might like to read what learners have said about the first course in the Python for Everybody Specialization. In particular, @Mohamed_ahmed has offered to answer any questions about the Specialization!
Userlevel 1
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i have taken the python for every body specialization(if you have question i can help)
there is also recommended path by coursera
https://careers.coursera.org/machine-learning-engineer/
there also a one for data scientist, you can find it in this in this blog
https://blog.coursera.org/new-coursera-start-finish-learning-paths-starting-new-career/
related to machine learning have a look at kaggle
Badge +1
Hi Mohamed,

thank you for adding the useful url where I can find a path to become a proficient learners in Machine Learning and Deep Learning Filed.

I appreciate a lot.

Laura,

thanks a lot for your suggestion. I will finish the Python for everybody specialization and I will dive in the Machine Learning Engineer path available through coursera.

Thanks again to all of you for your support and help.
Userlevel 4
Badge +3
https://www.coursera.org/specializations/scala

So this is a bit of a curveball (and I my experience may also be skewed by the london job market).

So I've been doing a few interviews of late for ML engineering roles and I've been asked a lot about topics I was unprepared for. Such as building "Scalable" applications, cloud computing/parallelism, etc.

In hindsight it makes sense for them to ask such questions (e.g if databases are gigabytes of data and need to respond in real-time (e.g fraud detection, customer recommendations) then you need to know some of the engineering principles behind scaling applications.

Don't get me wrong, I think your current path is a good one. BUT, my advice would be that once you have the core ML/deeplearning skills you should try to broaden your skillset as opposed to deepening it!

Learn SQL, Databases, Big Data Techniques, Algorithms, Parallelism/cloud computing, etc, etc.

The specialisation I've noted above has some of those things. Plus, learning a functional language will probably make you a better engineer (even if you never use scala after the course).

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