How to be a machine learning engineer?
If the same question is inside your head, you are not alone!
Machine learning, Data Science, Cyber Security - are ever-growing industries that work at the forefront of the development of artificial intelligence in many industries ranging from retail, finance, healthcare, tech, and automobile.
But, these fields are relatively new, and most people have no idea what a machine learning engineer is and how to become one.
Nevertheless, you can rest assured because this article can help you answer all your questions regarding becoming a machine learning engineer.
Here are the things you will learn from this article:
- What is machine learning?
- Who is a machine learning engineer?
- What does a machine learning engineer do?
- Job description of a machine learning engineer
- What is the difference between a machine learning engineer and a data scientist?
- What are the skills you need to learn to become a machine learning engineer?
- Google Machine Learning Engineer Salary & Trend
- How to Become a Machine Learning Engineer Without a Degree
What is Machine Learning?
Machine learning is one of the most exciting discoveries in the field of technology.
And we use it every day in our daily life.
Don't believe me?
We all use YouTube and Instagram in our free time.
You can notice that once you watch 2-3 dog videos on Youtube, the feed will automatically fill with suggestions for more dog videos.
But, how does Youtube know to suggest more dog videos?
Youtube does it by analyzing your watch history and predicting the type of content you like most. And it does everything by itself, without any assistance from a human.
Machine learning has been ingrained into our lives so well that we don't even notice it.
In simple terms, machine learning gives our computers the ability to learn independently and without any assistance from an engineer.
How Machine Learning Works:
It starts with training the algorithm with datasets. Let's say you want to teach the machine to understand a cat and a dog.
Then you have to start with a list of dog and cat photos which will work as data sets.
When the data sets are defined, the machine learning engineer starts to train the algorithm iteratively by inputting data to achieve better accuracy.
Who is a Machine Learning Engineer?
Machine learning engineer is the person behind building and training machine learning algorithms.
They are also computer programmers, but their focus goes beyond creating software and applications.
Their job is to create models that can take action without any assistance from users.
What Does a Machine Learning Engineer Do?
We've already established that machine learning engineers generally create and maintain self-learning algorithms without human intervention.
This sounds linear, but many things go behind it.
- Engineers have to run machine learning experiments using Python, R, and ML libraries.
- Deploy ML algorithms into software and test workflow while working on optimizing performance
- Perform foundational data science experiments such as analyzing data and understanding use-cases.
Machine learning engineers also collaborate with data scientists, researchers, engineers, and product managers to understand clients' requirements and product roadmap to provide a perfect solution.
ML Engineer Job Description
We are looking for a motivated and data-oriented machine learning engineer to optimize systems. You will evaluate the existing ML algorithm, perform data analysis and statistical analysis to assess data sets, and improve the accuracy of algorithms.
You should possess a deep understanding of Python, R, machine learning libraries and should have solid data science knowledge.
Machine Learning Engineer Roles and Responsibilities:
- Consult- with stakeholders and manager to understand product requirements and objective
- Develop machine learning systems to automate the predictive models.
- Transform data science prototypes and coordinate with users to collect feedback
- Test algorithm and maximum accuracy of the ML models
- Convert unstructured data into useful information by implementing ML techniques to find patterns
Machine Learning Engineer Requirements:
- Ability to solve complex problems, experience with multi-layered datasets
- Good knowledge of developing libraries and frameworks, data modeling, data structure
- Skill to write complex ML algorithms to analyze data for making a sound prediction
- Well versed in technical documentation
- Machine Learning Engineer Degree: Bachelors' degree in computer science, data science, mathematics, statistics, or any other related field
- Minimum two years experience in ML or data science field
- Extensive knowledge in Python, R, Java coding
- Superb interpersonal skills with management and organizational abilities
Difference Between Machine Learning Engineer and Data Scientist?
Data scientists are primarily responsible for understanding organizational issues by gathering data and resolving problems.
The Job Description of Data Scientist:
- Finding and removing duplicate data on data sets to maximize performance
- Analyzing data and preparing reports for stakeholders to ensure sound decision making
- Developing graphs, charts, and pivot tables to display data in an understandable way
Compared to the job of a machine learning engineer, which is to create algorithms that enable automatic pattern recognition and even make decisions on their own.
The Job Description of Machine Learning
- Creating machine learning algorithms by deploying Python, R, and Java
- Teaching ML algorithms by deploying datasets and improving prediction performance
- Developing prototypes for submitting to stakeholders and functionality testing of software
Skills to Learn to Become a Machine Learning Engineer
There are six skills you need to master to become a machine learning engineer.
Statistics & Applied Mathematics
It's one of the essential skills a machine learning engineer should have in his arsenal.
Statistics and mathematics have a wide range of applications in the field of machine learning. It is required to create various ML algorithms to develop statistical models for interpreting data.
Some of the essential topics you need to know are:
- Linear algebra,
- Multivariate calculus, etc.
Computer Science and Programming
Computer science is another essential requirement to become a machine learning engineer. The more familiar you are with coding and the basics of CS concepts like data structures, data algorithms, the better you are at these, the better your chances of becoming a machine learning engineer.
Apart from that, you need to have a good grasp of programming languages like Python, R, and various libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc.
Machine Learning Algorithms
Machine learning algorithms are the basis of becoming an ML engineer. Here are some standard ML algorithms you should know:
- Naïve Bayes Classifier
- K Means Clustering
- Support Vector Machine
- Apriori Algorithm
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests, etc.
Machine learning engineers have to work with a large amount of data daily. That's why they need to understand data modeling and evaluation.
The fundamental of data modeling is identifying the underlying data pattern and evaluating it to find meaningful insights.
You need to be well aware of these various algorithms to manage large datasets and speed up the machine learning process.
Neural networks work like a human brain for a computer. They have multiple input and output layers. The machine interacts with the outside world with various input layers, and that information passes through multiple layers to process the information for transforming it into valuable output.
These are all the functions of neural networks. There are various types of neural networks.
- Feedforward Neural Network
- Recurrent Neural Network
- Convolutional Neural Network
- Modular Neural Network
- Radial basis function Neural Network
You don't need to learn about all the types of neural networks globally, but you should have a strong understanding of the core foundation of the concept to become an ML engineer.
Natural Language Processing
NLP or natural language processing is another fundamental skill you should learn before jumping into machine learning. NLP helps computers understand the nuances of human language in a better way.
You need to understand NLP libraries such as Natural Language Toolkit to work on ML applications related to language processing.
Machine Learning Engineer Qualifications
To become a machine learning engineer, you should have a bachelor's degree in any computer science, mathematics, or related field.
Here are some courses you can opt for to become an ML engineer:
- Computer Science
- Data Analysis
- Data Science
- Artificial Intelligence
You can get a master's degree in a more advanced field such as
- Artificial Intelligence
- Machine Learning
- Neural Networks
- Deep Learning
- Big Data
Google Machine Learning Engineer salary
According to Glassdoor, a typical Google machine learning engineer salary is $1,42,568 per year
According to Indeed.com, the maiden salary of a Machine learning engineer is around $141,214 in the USA.
And according to PayScale, here is the salary graph for an entry-level to an experienced ML engineer.
If you look at the below trend chart, you will see that the value of a machine learning engineer is ever-increasing, and you can make a lot of money if you jump into this career early.
Machine Learning Engineer Career path
Here is a representation of a typical career path of a Machine Learning Engineer:
How to Become a Machine Learning Engineer Without a Degree
The demand for machine learning and AI-related jobs has jumped by 75% over the last year, and it's set to increase in coming years.
Many candidates think that they need a computer science degree to get into machine learning.
We will tell you ways to become a machine learning engineer without getting a degree:
Learn Machine Learning Skills
Before applying for machine learning jobs, you need to learn the basic skills to use machine learning.
Here are some tools and skills you should learn to become a machine learning engineer:
- R Programming
- Apache Kafka
- Google Cloud ML Engine
- Amazon Machine Learning
- PytorchJupyter Notebook
- IBM Watson
Apart from these tools, you need to clearly understand statistics, Linear algebra, data structures, algorithms, and calculus to grasp all the concepts of machine learning models.
Here are the top 5 courses to learn machine learning skills in 2022:
- Machine Learning by Coursera
- Machine Learning Course by Google AI
- Machine Learning with Python by Coursera
- Advanced Machine Learning Specialization by Coursera
- Machine Learning by EdX
Gain Some Experience
After you gain the skills, the next step is to get some experience.
The best way to gain experience is by participating in competitions and hackathons.
Different machine learning competitions are happening on platforms like Kaggle, where if you can solve the problems, you get a certificate and prize money. You can include these experiences and achievements in your resume as well.
However, the inputs and datasets you get from these competitions are pre-optimized based on the puzzle. So it is important to do some real-world projects to add to your portfolio.
After you become proficient in using the codes in machine learning, make a machine learning project that you like.
This project will help the recruiter understand if you have a good grasp of machine learning skills. For instance:
- If you can import data correctly
- If you can sort the data efficiently
- If you can process the data and create data visualization etc.
After you create a project, do not forget to add the project to GitHub. It will help recruiters understand your projects and help them gauge your skills.
Apart from creating your projects, you can also start contributing to open source projects as well.
It will build your team collaboration skills, and you will also get to expand your networks.
Get into a Bootcamp
Bootcamps are a great way to become a machine learning engineer if you don't focus on staying motivated throughout the entire course of learning. Yes, boot camps are expensive, but it helps you stay accountable and focused throughout your learning process.
And often, boot camps come with job guarantees. So, it's a great resource for anyone wanting to become a machine learning engineer.
Go to Networking Events
Even if you have all the skills to become a machine learning engineer, you won't get the opportunities you desire unless you put yourself out there. So, go into networking events, online webinars, meetups, and connect with professionals from different industries. It will help you get the machine learning job you require.
Join Machine Learning Communities
Joining machine learning communities online or offline allows you to get support with your learning.You can also start building projects with your communities and gain more hands-on experiences. It's an excellent way to learn and grow in the field.
There are also competitions that happen inside communities. And these competitions help you gain credibility and experience in machine learning.
Create a Machine Learning Resume
Creating a professional machine learning resume is essential to get a machine learning job. You can read the Hiration machine learning resume guide to create a stellar resume for yourself.
Also Read: How to Become a Machine Learning Engineer?
We hope this blog has helped you learn more about becoming a machine learning engineer. However, here are some of the key takeaways you can get from this blog.
- Learn machine learning skills such as neural network, NLP before jumping into a machine learning job.
- Get experience in machine learning with personal or open-source projects or internships.
- Apply for machine learning jobs with a stellar machine learning resume.
If you have any more questions, please leave us a message at email@example.com, and we will get back to you as soon as possible.