What are ways to become a good machine learning engineer and work for MAANG ?

Here are some steps to become a good machine learning engineer and work for MAANG or any other organization:

Get a solid education: A bachelor’s or master’s degree in computer science, mathematics, statistics or a related field will be helpful in understanding the fundamental concepts of machine learning.

Gain hands-on experience: Participate in online competitions, build your own projects and contribute to open-source projects to gain hands-on experience.

Learn from experts: Read books, attend workshops and webinars, and connect with experts in the field to stay up-to-date with the latest advancements in machine learning.

Focus on a particular area: Specialize in a specific area of machine learning, such as computer vision, natural language processing, or reinforcement learning.

Familiarize yourself with programming languages and libraries: Python, R, and TensorFlow are commonly used programming languages and libraries in the field of machine learning.

Practice, practice, practice: Keep practicing, experimenting, and iterating to improve your skills.

Network: Attend meetups and conferences, participate in online forums, and network with other machine learning engineers to expand your knowledge and find job opportunities.

Build a portfolio: Showcase your projects, code samples, and skills on a personal website or online platform to demonstrate your expertise and attract potential employers.

Remember that becoming a good machine learning engineer requires ongoing learning and dedication, so it is important to stay focused and continuously improve your skills and knowledge.

Please note that the hiring requirements and selection process of MAANG or any other organization may vary, and the above steps should be seen as general guidelines to help you develop the skills and knowledge necessary to pursue a career in machine learning.


Here are some resources you can use to learn the steps to become a good machine learning engineer:


Coursera (https://www.coursera.org/courses?query=machine%20learning)
Udemy (https://www.udemy.com/topic/machine-learning/)
edX (https://www.edx.org/learn/machine-learning)
Stanford University’s Machine Learning Course (https://ai.stanford.edu/courses/index.html)
Hands-on experience:

Kaggle (https://www.kaggle.com/competitions)
GitHub (https://github.com/search?q=machine+learning)
Learn from experts:

Fast.ai (https://course.fast.ai)
Google Machine Learning Crash Course (https://developers.google.com/machine-learning/crash-course)
YouTube channels such as Sentdex, Siraj Raval, and two minute papers


“An Introduction to Statistical Learning” by Gareth James et al.
“Pattern Recognition and Machine Learning” by Christopher Bishop
“Deep Learning” by Ian Goodfellow et al.
Programming languages and libraries:

Python (https://docs.python.org/3/)
R (https://www.r-project.org/)
TensorFlow (https://www.tensorflow.org/)

Kaggle (https://www.kaggle.com/competitions)
HackerRank (https://www.hackerrank.com/domains/tutorials/10-days-of-statistics)
LeetCode (https://leetcode.com/problemset/machine-learning/)

Meetups (https://www.meetup.com/topics/machine-learning/)
Conferences such as NeurIPS, ICML, and CVPR
Online forums such as Stack Overflow and Quora
These are just a few of the many resources available for learning machine learning. The key is to find what works best for you and to be persistent in your learning and practice. Good luck!

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