This is easier than it sounds

Build your own face recognition system in 10 minutes. No prior knowledge required, just code along and in 10 minutes you will have a great project to showcase and develop.
Study materials:
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If you are comfortable with python and have an understanding of Neural Networks, then you can just go over this course:
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Course 4 (CNN): imp.i384100.net/NeuralNetworks4
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If you want to become a Deep Learning Wizard, then I advice to go
over the whole specialisation in the following sequence:
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Course 1 (NN&DL): imp.i384100.net/NeuralNetworks1
Course 2 (NN hyperparameters): imp.i384100.net/NeuralNetworks2
Course 3 (StructuingMLProjects): imp.i384100.net/NeuralNetworks3
Course 4 (CNN): imp.i384100.net/NeuralNetworks4
Course 5 (Sequence Models): imp.i384100.net/NeuralNetworks5
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If you are completely new to Data Science & Python and want a clear understanding of how to get into this profession, check out my tutorial:
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• Learn Data Science: I ...
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Repository with the code supporting this video
github.com/DanilZherebtsov/fa...
#facerecognition #facerecognitionsystem #computervision #deeplearning #datascience #python

[@rajsala3026]

Sir can you suggest or give road map, how to become a self driving cars engineer

[@lifecrunch]

Well, what is your current level of software engineering/data science/IT skills?

[@rajsala3026]

@@lifecrunchNow I am Masters in Ai, I have average computer vision skills and I completed so many nano degrees and but in my mind this is not enough, go to something else found it. Can you suggest anything. I learn and apply it. I see your data science courses video that is incredible. And also how to create python libraries. I think your different person. Thanks for reply me

[@lifecrunch]

@@rajsala3026 In this regard you should be focusing on deep learning for computer vision and signal processing (data from sensors).
Your first choice should be the programming language - for R&D in this domain there is nothing better than python. If you are not up to speed with this language yet - I suggest to start with the first two courses that I outlined in my ‘Learn Data Science…’ video. Depending on future application of trained models in production you might be required to learn some low level language like C or Go so that your models could be inferenced faster. But that depends on the application.
Get to know python - tinyurl.com/5yhhw6e8
Some more python (OOP) - tinyurl.com/58wkzhn7
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The next step should be the Deep Learning Specialisation - the 5 courses that you need are listed under this video, here they are:
BEST NEURAL NETWORKS EDUCATION PROGRAM (in sequence)
Course 1 (NN&DL): imp.i384100.net/NeuralNetworks1
Course 2 (NN hyperparameters): imp.i384100.net/NeuralNetworks2
Course 3 (StructuingMLProjects): imp.i384100.net/NeuralNetworks3
Course 4 (CNN): imp.i384100.net/NeuralNetworks4
Course 5 (Sequence Models): imp.i384100.net/NeuralNetworks5
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After that a good idea would be the ‘Machine Learning in Production’ Specialisation which is also referenced in my ‘Learn Data Science’ video description under the following title:
MACHINE LEARNING DEPLOYMENT INTO PRODUCTION ENVIRONMENT
Course 1 (Intro in ML in prod): imp.i384100.net/MLProduction1
Course 2 (ML&Data Lifecycle in prod): imp.i384100.net/MLProduction2
Course 3 (ML Modeling Pipelines in prod): imp.i384100.net/MLProduction3
Course 4 (Deploying ML in prod): imp.i384100.net/MLProduction4
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While going over those courses you will execute numerous study projects. Choose the one that is interesting to you and develop it beyond what is required by the study assignment. Track your development process on GitHub and write about it on medium. This project should be relevant to the field you want to get into. When applying for a job in the field - reference your GitHub repo link in your application and CV. So when the hiring manager will consider your application - they will already see that you are working in this field and this will automatically qualify you above most of the competing candidates.