Become a AI ML Engineer

0 votes
What is the flow that should we follow to become a AI/ML engineer?

i'm asking about what should i learn and what are the skills that should i have?
May 5, 2020 in Career Counselling by anonymous
• 250 points
147 views

2 answers to this question.

+1 vote

Hi,

If you are a beginner then you should start with python concept like numpy and pandas concept. After getting some knowledge of this topic you can start your ML/AI journey.

First thing you can start with computer vision topic where all the basics of array and dataframe will clear. Also you will know about some small small operations that plays a big role in ML/AI while going through the computer vision topic.

After that you can start with Machine Learning concepts. But before that you should go through feature engineering concepts. To create a ML model is not a big deal, but the model should work properly. So you should know about your dataset, about your input data and predict data. That is why feature engineering is more important then creating a ML model.

From there you can start to create your ML model using LinearRegerssion and it will make comfortable to you in ML world. After that you can start with Neural Network, ANN, Classification problem, CNN etc.

You can also go through the below given links, it will give you some idea.

https://www.youtube.com/watch?v=WGJJIrtnfpk

https://www.youtube.com/watch?v=-ZrDjwXZGxI&t=1s

https://www.youtube.com/watch?v=GwIo3gDZCVQ

Hope this will give you some basic idea.

answered May 5, 2020 by MD
• 95,140 points
+1 vote

Hey, @Haritha,

There are some points which you should know before you start with AI ML:

  • Computer Science Fundamentals and Programming: Computer science fundamentals important for Machine Learning engineers include data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.)
  •  Probability and Statistics: Many Machine Learning algorithms are essentially extensions of statistical modeling procedures, that are necessary for building and validating models from observed data.

  • Data Modeling and Evaluation: Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns (correlations, clusters, eigenvectors, etc.) 

  • Software Engineering and System Design: You need to understand how these different pieces work together, communicate with them (using library calls, REST APIs, database queries, etc.), and build appropriate interfaces for your component that others will depend on. Careful system design may be necessary to avoid bottlenecks and let your algorithms scale well with increasing volumes of data. Software engineering best practices (including requirements analysis, system design, modularity, version control, testing, documentation, etc.) are invaluable for productivity, collaboration, quality, and maintainability.

I hope this will be helpful.

answered May 5, 2020 by Gitika
• 65,870 points
Thanks a lot for your support
Hi @Gltlka,

 should we have deep knowledge about statistics and probability to start machine learning??
No it is not required. If you have these knowledge then it is OK. At the time of learning ML you can easily get these knowledge.

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