This content originally appeared on DEV Community and was authored by Ankit Gupta
The world is unquestionable, altering in rapid and melodramatic ways, and the need for Machine Learning engineers is increasing day by day.
If you dream to become a Machine Learning Engineer, you need to keep-up with the following points -
- The fundamentals of programming and algorithms are something that you cannot miss out on.
- Sorting, searching, optimization, memory, cache, deadlocks, and bandwidth are concepts that you may need to master to excel in your Machine Learning profession.
- Mathematics & Statistics
- Mathematical play an important role in understanding the working of Machine Learning, and the use of different algorithms for business problems.
3 of the most important concepts you need to understand are –
- Probability — Bayes Theorem, Probability Distribution, Sampling, Hypothesis Testing.
- Linear Algebra — Matrices (To Perform Simple Operations) & Vectors (To Analyse Customer Behaviour)
- Calculus — Differential Calculus and Integral Calculus. These helps in determining the probability of events.
Develop Good Programming Skills
- Knowledge of R, Python, Java, C, C++, and other languages is essential.
- Python & R are the best languages for coding a Machine Learning algorithm.
- Python is Object-Oriented and R is a Functional-Oriented language.
Data Engineering Skills
- These skills help you process and analyze data. A Machine Learning engineer needs to work on the available data set and create patterns that will be useful for the business in question. Knowing integral aspects of data modeling and evaluation will certainly help a Machine Learning Engineer in evaluating various models.
- For a Machine Learning professional, it is a bonus to know about basic algorithms, memory management, and the linking process.
- It is also recommended to have been familiar with Libraries like Tensorflow, PyTorch, Scikit-learn, and Jupyter.
- Tensorflow — An open-source, free software library used for differentiable programming, and data flow, across a wide range of tasks. It is used for Machine Learning applications, like neural networks.
- PyTorch — An open-source Machine Learning library that is based on the Torch library. It is used in several applications, like natural language processing, and computer vision.
- Jupyter — It is a project that helps support execution environments, in several programming languages.
Now, lets talk about Machine Learning job opportunities.
Machine Learning engineering job graph is increasing at an exponential rate, well above Big Data and Cloud Computing opportunities. Machine Learning jobs are increasing day by day and Machine Learningengineers are financially well off.
Day-to-day innovations are highly dependent on Machine Learning.
Internet giants like Google, Facebook are embedded around Machine learning and its applications. The scope is tremendous and advancements in Machine Learning will make a big difference in every field.
You can bag a job as a –
- Machine Learning Engineer
- Computational Linguist
- Data Scientist
- Designer in Human-Centred Machine Learning
- NLP Scientist
- Business Intelligence Developer
- Software Developer/Engineer (AI/ML)
- Data Analyst
- Data Architect
- Cloud Architect
Thanks for reading till the end! Hope it helps…
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Ankit Gupta
This content originally appeared on DEV Community and was authored by Ankit Gupta
Ankit Gupta | Sciencx (2022-03-20T15:51:50+00:00) How to become a Machine Learning Engineer?. Retrieved from https://www.scien.cx/2022/03/20/how-to-become-a-machine-learning-engineer/
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