Here Is All That You Need To Know For A Career as A Machine Learning Engineer

In the era of Artificial Intelligence Machine Learning is a great job title to pursue. The use of Machine Learning is almost in every field right now. The demand for Machine Learning Engineer has risen exponentially to deal with all the complex data to make a computer of machine come to a decision based on the information. The career path of a Machine Learning Engineer brings lots of opportunities to work on.

Brad Miro Machine Learning Engineer

Definition Of Machine Learning

Machine Learning is the computers' ability to learn from data. In traditional programming methods, if one would want to identify the type of data, i.e. an integer or a string, it is very easy to do in almost all available programming language today. 

However the question is if you try to identify something like an apple, how would you program into a computer to make it understand that it is an apple? Here comes the concept of machine learning where a Machine Learning Engineer would show the computer a picture of an object and say what it is. It will allow the computers to learn all these patterns from the data; here the pixels values of the image will be the data. 

 

Main Areas Or Services Where Machine Learning Is Used

In 2019, Machine Learning is everywhere. 

  • Finance: Machine Learning is showing up in finance to deal with stock. Lots of high-frequency traders give power to Machine Learning for making decisions.
  • Advertising: Machine Learning is also part of the advertising market. In this spectrum, the decisions are being made in terms of what the advert is. Targeted advertising specifically uses lots of Machine Learning.
  • Computer Vision: Self-driving cars are the subfield of computer vision, and Machine Learning powers the cars.
  • Fraud Detection
  • Weather Prediction 
  • Medicine 
  • Video Games: Nowadays a lot of video games are powered by Machine Learning. 
  • Speech to Text: The audio is taken to translate it into a text and also the vice versa. 
  • Translation: The ability to translate one language to another language is easier through Machine Learning with efficiency and accuracy. 

 

Must Have Skills On Your Machine Learning Job Resume

It is crucial for a Machine Learning Engineer to have basic skills of what a Software Engineer. If you are an Engineer, you have to know data structures, algorithms and “how to build software”.

  1. Learn Languages: In Machine Learning, all will depend on what type of role you are interested in. You can learn
    • Convolutional Neural Networks
    • Recurrent Neural Networks
    • Tensorflow or Pytorch
    • Be proficient in one of these two software.
    • Learn Python
  2. Build Dummy Projects
  3. It doesn't have to be huge. Just copy some real models. Give little information about those toy projects in the resume.

 

Career Path Of A Machine Learning Engineer

There are lots of different career paths that a Machine Learning Engineer can pursue. 

  • Senior Machine Learning Engineer
  • Data Scientist
  • Machine Learning Researcher
  • Project Management Role

The Machine Learning role will open up lots of opportunities for you. 

 

Deep Learning Vs Machine Learning

  • Deep Learning is the subset of Machine Learning.
  • Deep Learning can create powerful Models. 
  • It is very expensive and requires a lot of resources to maintain which may notbe always be necessary. 

 

Machine Learning Can Be A Part Of Artificial Intelligence

Machine Learning is used heavily for Artificial Intelligence. Machine Learning algorithms power many AI tools. Machine Learning is not necessarily a subset of Artificial Intelligence. 

 

Frequently Asked Question In Machine Learning Interview

  • Difference between supervised learning and unsupervised learning. Explain when you might use a supervised learning algorithm versus an unsupervised learning algorithm.
  • Suggest some algorithms to me for building something that can detect the difference between two objects. What are the trade-offs of doing that algorithm versus another algorithm?
  • Is model accuracy the best metric for testing model performance?
  • What is the bias-variance tradeoff? What does it mean to have an over-fit model versus under-fit model?
  • Two machine learning algorithms are out there, K nearest neighbors and K-means clustering. Difference between them.
  • Derive a simple algorithm from scratch and explain the mathematics behind it. 

 

Best Resources To Prepare For Machine Learning Interview

  • Go online
  • Cracking the Coding Interview- Book 
  • Read Book on Statistics 
  • Linear Algebra
  • Take Course on Machine Learning
  • Get Mock Interview from Experienced People in The Same Field

 

Challenges Associated With Machine Learning Job

  • Data is the Biggest Challenge: Machine Learning Engineer has to deal with the privacy of data, data fairness and also has to make sure that the data is good and unbiased.
  • Model Explaining Ability
  • Model Hosting

 

Most Interesting Project Undertaken By Brad Miro

Summarize large bodies of text into smaller bodies of text. Being able to parse the document and various amounts of strings available and providing more concise summarization.

 

Education Or Certification Required For Machine Learning Field

  • Get a Masters' Degree
  • Do PhD
  • Online Classes
  • Build Some Models
  • Read Research Papers

 

Best Companies To Work For As Machine Learning Engineer

  • OpenAI
  • Google
  • Facebook
  • Choose a Company Based on What Type of Work You Want to Do

 

Job Search Strategy For Aspiring Machine Learning Engineers

  • Don’t Cold Apply to a Whole of Bunch of Companies
  • Referrals are Great
  • Reach Out to People 
  • Have Online Presence

 

Advice For Applicant Seeking Machine Learning Role And Suceed In It

  • Strong Network is Important: Good networking may land you a great job in your field.
  • Continue Learning: Know about new technologies, new packages and new updates to stay up-to-date.