Machine learning as a career (in India or abroad)

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SUMMARY

Machine learning careers require a solid foundation in mathematics and programming. Devanand T, with a background in electronics engineering, inquired about the necessity of rigorous math and programming skills for pursuing a PhD in machine learning. It was concluded that a decent statistics background is essential, particularly for understanding models like naive Bayes and neural networks. Additionally, proficiency in programming languages such as C and C++ is crucial for modifying and applying machine learning tools effectively.

PREREQUISITES
  • Statistics knowledge for understanding machine learning models
  • Proficiency in programming languages, particularly C and C++
  • Familiarity with applied probability models, such as Markov and hidden Markov models
  • Basic understanding of neural networks and classification techniques
NEXT STEPS
  • Learn advanced statistics for machine learning applications
  • Study C and C++ programming for algorithm implementation
  • Explore Markov models and their applications in machine learning
  • Investigate neural network architectures and their practical uses
USEFUL FOR

Individuals interested in pursuing a career in machine learning, particularly those with a background in engineering or related fields, as well as aspiring PhD candidates looking to strengthen their mathematical and programming skills.

dexterdev
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Hi all,
I recently got very interested in machine learning and neural networks etc. Although I am having a taste for mathematics , I haven't rigorously learned any mathematics course because my background is electronics engineering (masters level). And also I am not a good C or Java programmer. My basic doubts are :

1. Is heavy math prerequisites needed for me to do a Phd in machine learning? (Or can the required math can be learn from phd course work itself)

2. Should I require great programming skills in C / C++ / Java / Python etc for machine learning? (What I can handle at the moment is Matlab or Octave etc)

My some of the research dreams is to work on automatic music composition, classification etc.
Please guide me in this area.

-Devanand T
 
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Hey dexterdev.

With regard to automatic music composition, you should take a look at applied probability models like markov and hidden markov models.

As for machine learning, you should (in my opinion) get a decent statistics background so that you understand what is going on with things like naive Bayes, neural networks, and other classifications based on statistical models.

You will also need to read code at some point and take other peoples code to compile. If you don't know how to program then things are going to be difficult.

If there is a bug in the code or you have to start changing variables or code definitions to get the kind of behavior that you need, you are going to be stuck.

I would recommend you learn C and C++ for this field.

The thing that you need to answers is whether you are justing going to use the tools as is, or whether you are going to modify, create, and apply the tools for a variety of situations in different ways.
 
Thanks for the reply Sir.
 

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