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How to become a Machine Learning Engineer: Full Guide 2021

How to become a Machine Learning Engineer: Full Guide 2021

What’s Machine Learning?

Using machine learning to automate a task is a way of achieving a goal with a minimal amount of human input. This is done by using the computer’s pattern recognition capabilities to recognize patterns in data and accurately predict future outcomes, often without specific instructions from the user.

These predictions can be used for good or bad – good examples include spam detection, spam filtering, loan rejection, auto-booking deals. Bad examples include botnet attacks or online advertising fraud. In this blog post I will outline the skills you’ll need to become a machine learning engineer and what you need to study at university to get industry experience as an ML Engineer.

What does a Machine Learning Engineer Do?

A machine learning engineer practices one of the many branches within the broader field of artificial intelligence and develops and applies solutions in software, hardware or any other technology to make better predictions in a variety of areas.

This involves:

ML Models and Big Data

Developing models that can learn from data. This involves collecting data and training algorithms to recognize patterns in data and predict future outcomes with varying levels of accuracy. At the end of this process, there is a reduced need for human input when it comes to making decisions regarding the outcome. The trained algorithm will be able to recognise patterns in the data and continuously improve itself over time so it can be more accurate at predicting future outcomes.

ML Engineers Building Infrastructure

Designing, building, and maintaining the technological infrastructure that underpins the application of machine learning. This may involve building servers in the cloud to host these models or setting up an automated pipeline to update models over time.

ML Engineers Helping with Business Strategy Development

Helping the business understand how the model can be used in their strategy, for example by proving ROI or improving customer experience. This involves not only explaining technical solutions, but also translating them in a way that business leaders can understand.

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How to become a Machine Learning Engineer?

Any software engineer can do machine learning, right? After all you just need to find open source machine learning libraries online. Unfortunately, it’s often not that simple, especially when it comes to applying machine learning at scale in production environments.

What you need to know to become a Machine Learning Engineer:

  • ML Libraries
  • Algorithms
  • Computer Science Background
  • Data Management
  • Architecture

It helps to understand not just how to use machine learning libraries but also how they work under the hood. You’ll also need to understand the theory behind machine learning algorithms so you can make informed decisions when applying them in practice. This includes data pre-processing, feature extraction, regularisation techniques and tuning parameters relevant to different problem domains. On the other hand, you will generally need to have a computer science / software engineering background and need less domain expertise when it comes to machine learning.

Top Machine Learning Courses in 2021

Some courses worth checking out:

https://www.coursera.org/learn/machine-learning 

Machine Learning by Stanford Online

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning Education by Google

Machine Learning by TensorFlow

Machine Learning Engineer vs. Data Scientist?

Machine Learning Engineer vs. Data Scientist is an ongoing debate that’s likely to continue for some time yet. I’ve covered this in more detail in another blog post so I’ll just leave you with a few thoughts here.

I believe there are some distinct differences between ML Engineers, Data Scientists and Data Analysts. A good Data Scientist will be able to evaluate different machine learning algorithms, design tests to evaluate the predicted outcomes, understand how to extract meaningful features from data, troubleshoot problems that arise during testing etc. A good Data Scientist will likely have a PhD or Master’s degree in machine learning or at least significant industry experience to back up their skills.

A Machine Learning Engineer on the other hand will generally have a computer science / software engineering background and need less domain expertise when it comes to machine learning.

Do you need a degree in computer science to become a machine learning engineer?

Many companies are looking for candidates with degrees in specific subjects such as computer science, mathematics or statistics. This is because degrees are designed to equip students with broad skills sets that are not immediately applicable to the workplace. The academic approach teaches students how to think about problems critically, which is crucial for success in any industry including technology. The syllabus also includes many topics that are relevant for working as an engineer – algorithms, data management, computer architecture etc.

However, this does not mean that you don’t need specific skills or knowledge to become a machine learning engineer. You may be able to get by without a degree in computer science but you’ll likely be more limited in your skillset. The core competencies required for machine learning are similar to programming and software engineering – the ability to program well, design effective algorithms, be able to clearly communicate your solution, and make sound technical decisions when faced with unforeseen problems.

If you wanted to become a Machine Learning Engineer, you could start by finishing your bachelor’s degree in Computer Science. But there is so much more than just programming on this path. One of the best ways to learn more about engineering and how it works is by taking online courses. You can also enroll in a Massive Open Online Course (MOOC) offered by a reputable institution. Metrics from Coursera and edX show that the percent of students who complete a course increases significantly when that course has been offered as a MOOC.

How to become a Machine Learning Engineer: Full Guide 2021
How to become a Machine Learning Engineer: Full Guide 2021

Will a degree from a top university give you a competitive advantage over candidates from other universities?

In an ideal world, there wouldn’t be any differences between candidates from different universities. However, the ability to learn new concepts quickly is highly valued in today’s hiring process. University lecturers know that, and as a result, may design coursework to be more accessible to students who have demonstrated the ability to learn fast. This may mean that concepts are explained more clearly and at a slightly less technical level than perhaps is required for the industry.

Of course, this may change over time. For example, a few years ago a degree from a top university still meant a great advantage when it came to getting a decent salary and high-level jobs. However, this may no longer be the case – or it might mean that you need to spend more time reading up on industry-standard best practices before you apply for a job. In other words, don’t be put off applying for jobs based on your university results but strive to get excellent results regardless of what school you go to.

A machine learning engineer will have an active role in the design and development of software systems requiring machine learning functionality.

Don’t forget that this is a rapidly evolving field, and that there may be new techniques, tools or algorithms becoming available over time. Applying these in practice will not only give you a competitive advantage but also provide you with valuable experience even if your job title doesn’t change. Whitepapers published by companies such as Google and Microsoft are a great resource for beginners – they cover topics such as recommended practices for data analysis using various libraries and frameworks.

Ultimately, if you want to become a machine learning engineer it’s crucial to stay up-to-date with the latest innovations and techniques – and of course, new opportunities.

That’s it for this post, I’ll be back soon with more posts covering specific topics in depth.

ML Job: A summary

First things first: what is machine learning? Put simply, it’s a process that allows computers or robots (or even people) to learn new tasks on their own without human instruction.

For example, you could train a computer to recognize the difference between a dog and a cat. The machine can actually learn what makes each one unique as it searches for visual clues on your computer screen. In other words, it’s not simply taking in data and spitting out some kind of generic result. It’s actually “thinking” about the information as it looks for specific elements that make each animal unique from the other.

That’s the idea behind machine learning: to give computers a better understanding of their world through a series of instructions. This helps them solve problems they’ve never encountered before on their own.

 

Future Jobs: ML Engineer

You can find your way into many different types of careers, but some are harder to break into than others. Among those careers, the most difficult by far is becoming a machine learning engineer. This is because of the special skills you’ll need in order to become one of these programmers. However, if you’re up for the challenge and willing to put in the work, here are some tips that will help guide you on your quest to master machine learning programs.

Machine learning engineering is the application of statistical pattern recognition to practical problems. Studying machine learning engineering can qualify you for an exciting new career in artificial intelligence, computer science, data mining, information retrieval, etc.

Most people are aware of the importance machine learning has on our everyday lives—it is present in almost every aspect of our economic life. For example, without machine learning systems it would be hard to even imagine what products we wear or use on a regular basis. We rely heavily on robots that are controlled by machine-learning algorithms to transform raw materials into finished products at large scale. Machines are now capable of using images and sound to detect hazards or gather information that humans cannot process as easily with their five senses alone.

What’s Machine Learning?

Using machine learning to automate a task is a way of achieving a goal with a minimal amount of human input. This is done by using the computer’s pattern recognition capabilities to recognize patterns in data and accurately predict future outcomes, often without specific instructions from the user.

These predictions can be used for good or bad – good examples include spam detection, spam filtering, loan rejection, auto-booking deals. Bad examples include botnet attacks or online advertising fraud. In this blog post I will outline the skills you’ll need to become a machine learning engineer and what you need to study at university to get industry experience as an ML Engineer.

What does a Machine Learning Engineer Do?

A machine learning engineer practices one of the many branches within the broader field of artificial intelligence and develops and applies solutions in software, hardware or any other technology to make better predictions in a variety of areas.

This involves:

ML Models and Big Data

Developing models that can learn from data. This involves collecting data and training algorithms to recognize patterns in data and predict future outcomes with varying levels of accuracy. At the end of this process, there is a reduced need for human input when it comes to making decisions regarding the outcome. The trained algorithm will be able to recognise patterns in the data and continuously improve itself over time so it can be more accurate at predicting future outcomes.

ML Engineers Building Infrastructure

Designing, building, and maintaining the technological infrastructure that underpins the application of machine learning. This may involve building servers in the cloud to host these models or setting up an automated pipeline to update models over time.

ML Engineers Helping with Business Strategy Development

Helping the business understand how the model can be used in their strategy, for example by proving ROI or improving customer experience. This involves not only explaining technical solutions but also translating them in a way that business leaders can understand.

How to become a Machine Learning Engineer?

Any software engineer can do machine learning, right? After all you just need to find open source machine learning libraries online. Unfortunately, it’s often not that simple, especially when it comes to applying machine learning at scale in production environments.

What you need to know to become a Machine Learning Engineer:

  • ML Libraries
  • Algorithms
  • Computer Science Background
  • Data Management
  • Architecture

It helps to understand not just how to use machine learning libraries but also how they work under the hood. You’ll also need to understand the theory behind machine learning algorithms so you can make informed decisions when applying them in practice. This includes data pre-processing, feature extraction, regularisation techniques and tuning parameters relevant to different problem domains. On the other hand, you will generally need to have a computer science / software engineering background and need less domain expertise when it comes to machine learning.

Top Machine Learning Courses in 2021

Some courses worth checking out:

Machine Learning Engineer vs Data Scientist?

Machine Learning Engineer vs Data Scientist is an ongoing debate that’s likely to continue for some time yet. I’ve covered this in more detail in another blog post so I’ll just leave you with a few thoughts here.

I believe there are some distinct differences between ML Engineers, Data Scientists and Data Analysts. A good Data Scientist will be able to evaluate different machine learning algorithms, design tests to evaluate the predicted outcomes, understand how to extract meaningful features from data, troubleshoot problems that arise during testing etc. A good Data Scientist will likely have a PhD or Master’s degree in machine learning or at least significant industry experience to back up their skills.

A Machine Learning Engineer on the other hand will generally have a computer science / software engineering background and need less domain expertise when it comes to machine learning.

Do you need a degree in computer science to become a machine learning engineer?

Many companies are looking for candidates with degrees in specific subjects such as computer science, mathematics or statistics. This is because degrees are designed to equip students with broad skills sets that are not immediately applicable to the workplace. The academic approach teaches students how to think about problems critically, which is crucial for success in any industry including technology. The syllabus also includes many topics that are relevant for working as an engineer – algorithms, data management, computer architecture etc.

However, this does not mean that you don’t need specific skills or knowledge to become a machine learning engineer. You may be able to get by without a degree in computer science but you’ll likely be more limited in your skillset. The core competencies required for machine learning are similar to programming and software engineering – the ability to program well, design effective algorithms, be able to clearly communicate your solution, and make sound technical decisions when faced with unforeseen problems.

If you wanted to become a Machine Learning Engineer, you could start by finishing your bachelor’s degree in Computer Science. But there is so much more than just programming on this path. One of the best ways to learn more about engineering and how it works is by taking online courses. You can also enroll in a Massive Open Online Course (MOOC) offered by a reputable institution. Metrics from Coursera and edX show that the percent of students who complete a course increases significantly when that course has been offered as a MOOC.

Will a degree from a top university give you a competitive advantage over candidates from other universities?

In an ideal world, there wouldn’t be any differences between candidates from different universities. However, the ability to learn new concepts quickly is highly valued in today’s hiring process. University lecturers know that, and as a result, may design coursework to be more accessible to students who have demonstrated the ability to learn fast. This may mean that concepts are explained more clearly and at a slightly less technical level than perhaps is required for the industry.

Of course, this may change over time. For example, a few years ago a degree from a top university still meant a great advantage when it came to getting a decent salary and high-level jobs. However, this may no longer be the case – or it might mean that you need to spend more time reading up on industry-standard best practices before you apply for a job. In other words, don’t be put off applying for jobs based on your university results but strive to get excellent results regardless of what school you go to.

A machine learning engineer will have an active role in the design and development of software systems requiring machine learning functionality.

Don’t forget that this is a rapidly evolving field, and that there may be new techniques, tools or algorithms becoming available over time. Applying these in practice will not only give you a competitive advantage but also provide you with valuable experience even if your job title doesn’t change. Whitepapers published by companies such as Google and Microsoft are a great resource for beginners – they cover topics such as recommended practices for data analysis using various libraries and frameworks.

Ultimately, if you want to become a machine learning engineer it’s crucial to stay up-to-date with the latest innovations and techniques – and of course, new opportunities.

That’s it for this post, I’ll be back soon with more posts covering specific topics in depth.

ML Engineering Job: A summary

First things first: what is machine learning? Put simply, it’s a process that allows computers or robots (or even people) to learn new tasks on their own without human instruction.

For example, you could train a computer to recognize the difference between a dog and a cat. The machine can actually learn what makes each one unique as it searches for visual clues on your computer screen. In other words, it’s not simply taking in data and spitting out some kind of generic result. It’s actually “thinking” about the information as it looks for specific elements that make each animal unique from the other.

That’s the idea behind machine learning: to give computers a better understanding of their world through a series of instructions. This helps them solve problems they’ve never encountered before on their own.

Future Jobs: ML Engineer

You can find your way into many different types of careers, but some are harder to break into than others. Among those careers, the most difficult by far is becoming a machine learning engineer. This is because of the special skills you’ll need in order to become one of these programmers. However, if you’re up for the challenge and willing to put in the work, here are some tips that will help guide you on your quest to master machine learning programs.

Machine learning engineering is the application of statistical pattern recognition to practical problems. Studying machine learning engineering can qualify you for an exciting new career in artificial intelligence, computer science, data mining, information retrieval, etc.

Most people are aware of the importance machine learning has on our everyday lives—it is present in almost every aspect of our economic life. For example, without machine learning systems it would be hard to even imagine what products we wear or use on a regular basis. We rely heavily on robots that are controlled by machine-learning algorithms to transform raw materials into finished products at large scale. Machines are now capable of using images and sound to detect hazards or gather information that humans cannot process as easily with their five senses alone.

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