Machine Learning Career Path

This thread contains 5 replies, has 3 voices, and was last updated by Chirag Sawhney 5 years, 2 months ago.

  • Author Replies
    • #52970

      Some basic skills you need to become a machine learning engineer.

    • #52971

      So here are the basic skills:
      1. Computer Science Fundamentals
      – It includes data structures of the stack, queue, multi-dimensional arrays, trees, graphs and etc.
      -It also combines algorithms like searching, sorting, optimization and so on.
      2. Probability and Statistics
      3. Data Modeling and Evaluation
      -Data modeling is the process of determining the underlying structure of a given dataset. A key part of this estimation process is continuously evaluating how good a given model is.
      4. System Design
      -The software is a base component that fits into a larger ecosystem of products and services. You need to learn how these different parts work together.

    • #55652

      Which field has a better career in the future, Data Science or Machine Learning?

    • #55653

      Data science, machine learning and artificial intelligence are growing at a rapid speed. Machine learning is the subpart of AI, while data science neither fully cover AI nor it’s AI, it is the part of AI. AI and ML concepts have been around for many times, but it is only recent because as technology increases a huge volume of data is gathered from various resources. Both the fields which you mentioned have a better career. Data science is a field which deals with the data and extracts knowledge from it to gain useful insight into the business. While Machine Learning is the science of getting the computer to interact without explicitly programmed. The thing is, got an understanding of both the subject, find your interested topic and go further. ML and Data science both are huge fields. There are no. of topic for which you can do a study, research, Ph.D.

    • #56589

      Why machine learning matters a lot in today’s trend?

    • #56591

      Because of new computing technologies, machine learning today is not like machine learning of the past.
      -The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.
      -Through machine learning, it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
      -Financial services used it especially to identify important insights in data and to prevent fraud.

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