If you’ve come up with a decision to master Machine Learning, congratulations!
You are on your way to gaining one of the most popular and profitable professions of the 21st century, which, according to Indeed, reaches to $149,801 average base salary in 2021.
As an ML specialist, you will deal with Artificial Intelligence and tons of data to extract insights that might not be accessible otherwise.
If you’ve already decided for sure to step into the Machine Learning galaxy, but are unsure of where and how to start, don’t worry.
This blog post contains everything you need:
- What is Machine Learning?
- What do Machine Learning engineers do?
- What do you need to become a Machine Learning Engineer?
Who is a machine learning engineer?
First, you need to know – a machine learning engineer is not a data scientist, but those two specializations are tightly related.
Data scientists are mainly focused on storing and analyzing data. Later, they use data analysis results to make statistics and build models that can make predictions.
Machine learning engineers connect data scientists and the construction of machine learning and AI systems. Particularly, using the data analysis results, ML engineers build independently working software that automates making predictions.
Those created systems or software use Artificial Intelligence and leverage extensive data until they can make accurate predictions.
Later, the system “learns” from its experience. Each time a system performs a specific action, the results of that experience are collected and considered the next time machine performs similar action.
If you feel the ML picture is still vague for you, let’s discuss the responsibilities of the machine learning engineer in more detail.
What are the primary responsibilities of the machine learning engineer job?
- Communicate with data scientists, business analysts, and other specialists from the IT department to collect the data analysis results.
- Based on the received data, design machine learning systems that can automatically predict future trends for the business.
- Select appropriate data sets and exclude not purposeful data so that ML systems’ performance is not affected.
- Continually run machine learning tests and use the results to improve the overall performance of the system.
- Communicate the results to the main product stakeholders, even if they are not into ML and do not understand professional terminology.
Which skills do you need to become a machine learning engineer?
Even if you haven’t explored this profession deeply, you can guess it is a complicated IT direction, which requires a specific set of skills and a particular mindset.
Being a tech junkie who has a general understanding of AI and how it works is one of the must-haves to enter the profession. However, if you are a hard-working individual with a strong passion for ML, you can still learn it from scratch.
Here is what you should know to start working in the ML field.
- You should learn computer science fundamentals to imagine a general picture of data structures, algorithms, and computer architecture.
- Having mathematical skills is a must. Many things in ML are tightly connected to calculations and mathematical mindset.
- To bridge Data Science and AI systems construction, you need to have both data scientist and software engineer skills.
- Practice your analytical and problem-solving skills, as you will need them much when working with algorithms.
Which programming languages do I need to become a machine learning engineer?
If you are interested in becoming a machine learning engineer, you should first focus on the required hard skills – coding languages.
The more popular programming languages used in machine learning include Python, JavaScript, etc.
Let’s figure out below how you can benefit from these languages in machine learning and where you can learn them.
Python in machine learning
Python has become the synonym of data analysis, data science, AI, and machine learning. Python deals with excessive volumes of data and performs complex results, but it’s easy to work with because of libraries’ user-friendly coding and organization.
The simplicity of Python is one of the key reasons why it’s so popular for ML-based projects. The code in Python is very user-friendly, and you can run complex AI systems with the code understandable both for developers and project stakeholders who are not programmers.
Interested in learning Python for Machine Learning? Browse our Python for Data Science interactive course, which introduces the general concept of Python, with the main emphasis on the libraries and functions which can help you conduct data analysis projects.
JavaScript in machine learning
While Python is the absolute leader language for data science and machine learning, there are other languages you can use to conduct ML-based projects, one of which is JavaScript.
In many cases, you might be technically limited in conducting AI projects with JavaScript. Still, it has a significant advantage. As JavaScript is supported by the majority of the web and mobile devices, you can be sure that your ML product created with JavaScript will successfully load for all your stakeholders.
JavaScript already has plenty of Machine Learning libraries, such as TensorFlow.js, ML5.js, Synaptic, and Brain.js.
Interested in learning JavaScript for Machine Learning? Browse our JavaScript course to learn all the main variables and types you can later utilize in your projects.
