A Complete Guide to Become a Machine Learning Engineer in 2021

AlphaGo, Google DeepMind’s artificial intelligence (AI) program is the first-ever computer program to defeat a human Go player!

Built with machine learning techniques and systems integrated hand-crafted rules, AlphaGo’s legacy is spoken worldwide.

AI and machine learning have started taking the technology world by storm. In addition, job opportunities in machine learning are rising dramatically.

  • At the global level, the machine learning market is estimated to be worth USD 31 billion by 2024, as predicted by Market Research Future.
  • The key players in the market include tech giants like Microsoft Corporation. (US), Intel Corporation (US), Google (US), IBM Corporation (US), Amazon.com, Inc. (US), Nuance Communications (US), Facebook, Inc. (US), Apple Inc (US), Nuance Communications (US), Cisco Systems, Inc. (US), and Wipro Limited (India).

Major companies adopting machine learning and AI in business across industries reflect how job opportunities in the field will eventually propel.

Currently, there are about 62,267 jobs in the U.S. that list machine learning as a required skillset, and around 151,390 jobs, according to LinkedIn.

So, it’s not surprising to see the number of AI and machine learning jobs skyrocket.

With job trends rising in both fields, it is certainly a good time for you to pursue a career in 2021.

Machine learning engineer: Job roles and responsibilities

Machine learning engineers are responsible for feeding data into models that have been defined by data scientists. Below are two possible scenarios:

As a machine learning specialist:

  • Accountable in analytics and selecting features that rely on information processes i.e. development of a feature vector for every object to be analyzed.
  • Development of algorithms for the machine to learn.

As a deep learning specialist:

  • Develop artificial neural networks.
  • Create algorithms to analyze and identify features of the object processes for better structuring. For instance, analysis and sorting content such as texts, audios, and images.

Some key parts in the machine learning process
Source: techtarget

In general, as a machine learning engineer:

  • Utilize in-depth mathematical skills to work with algorithms and perform computation skills.
  • Deliver results and solve complex issues to make the programs effective.
  • Work in close synchrony with data engineers to build data pipelines and model them.
  • Demonstrate extensive knowledge in computer science fundamentals – computability, computer architecture, data structures, and algorithms.
  • Implement AI and machine learning algorithms.
  • Build algorithms according to statistical modeling procedures.
  • Build and maintain scalable machine learning solutions.
  • Collaborate with stakeholders, analyze business problems and find relevant solutions.
  • Management of the infrastructure and data pipelines that need to go to production.
  • Showcase end-to-end knowledge of the applications that are developed.
  • Analyze large volumes of data to extract valuable insights critical for decision making.
  • Act as a support to the product managers and engineers and help implement machine learning in the product.
  • In-depth research and utilize the best practices to boost the existing machine learning infrastructure.
  • Utilize data modeling to evaluate strategy and help detect patterns to predict unforeseen challenges.
  • End-to-end communication with non-programmers to explain complex processes to them.

What machine learning engineers must know?

Machine learning jobs are well-suited for tech professionals with great analytical skills. In-depth knowledge in the areas mentioned below is a must-have:

  • Mathematics and statistics
  • Computer science
  • Physics

Source: coursera

Technical skills include:

  • Python
  • R
  • SQL
  • Big Data
  • Apache Spark
  • Hadoop
  • Java
  • Scala
  • NLP
  • ETL
  • Deep Learning
  • TensorFlow
  • Pandas
  • Scikit-Learn
  • NumPy
  • Keras
  • Computer Vision
  • PyTorch

However, to perform standard tasks in machine learning, below are the prerequisites you need to master:

  • Learn discrete mathematics, statistics, and probability theory
  • Solid grasp in machine learning algorithms
  • Capability to work with data warehouses
  • Analyze and model data using programming languages like Python, R or SPSS, SAS
  • Data visualization using Matplotlib tools

AI and machine learning are among the trending jobs in the labor market. And with multiple companies investing in these technologies, the demand for machine learning and AI engineers will surge.

Now that you’re aware of the skills you need to master, we will further discuss what you need to study within the next three months.

Are you looking to master machine learning in 3 months? Here’s what you need to study

Month 1 – mathematics and algorithms

  • Week 1: Linear algebra
  • Week 2: Mathematical analysis
  • Week 3: Probability theory
  • Week 4: Algorithms

Month 2 – Machine Learning

  • Week 1: Python for data science
  • Week 2: Introduction to machine learning
  • Week 3 and 4: Ideas for machine learning projects

Month 3 – Deep Learning

GitHub and Kaggle are two great platforms for you to get started with projects and building your own apps. Projects provide an overview regarding your hands-on experience in the technology. Since employers look for practical skills, having multiple projects on your portfolio is an added advantage.

A rigorous effort and hard work in these three months will equip you with practical knowledge in machine learning.

List of the best online courses in machine learning you cannot afford to miss:

  • Machine learning course from Coursera: Offered by Stanford University, this course gives you the privilege to learn machine learning and its practical usage.
  • Free machine learning course from Udacity: The Udacity’s free machine learning course takes you through the journey of grabbing one of the most exciting careers of the era. This course is free of cost and will take you nearly 10 weeks to complete your machine learning engineer journey. Through this course, you will be able to learn the end-to-end process of how to investigate data using machine learning.
  • The machine learning engineer nano-degree (MLND) program from Udacity: The MLND program is perfect for aspirants looking to gain hands-on experience in AI and machine learning techniques. This program provides projects and quizzes for applicants to get their hands dirty.
  • The school of AI from Udacity: The school of AI comes up with interesting programs for applicants to start within the AI and machine learning field. You can choose the relevant program based on your interest.
  • Deep leaning nano-degree program from Udacity: Deep learning is becoming an important aspect of AI. Therefore, candidates who can build and apply their own deep neural network will be the most sought after in the current tech industry.

List of conferences a machine learning engineer must attend:

Perhaps you’re keen on knowing what the leaders in AI and machine learning have to say, well, attend a conference.

Below are a few lists of conferences you need to attend:

  • NVIDIA GPU Technology Conference: April 12 – 16, 2021
  • NeurIPS: Virtual conference on neural networks. From March 31 to December 14, 2021
  • QCon Software: May 17-28, 2021

You’re not too late, you can still get an early bird ticket and register for the conference. Hurry while you still have the chance.

Are you all set for an interview? This is how you need to prepare

Perhaps this is the most challenging part of landing a career in machine learning because this is where it gets tough. Despite skills and knowledge, you need to tell the employers what they actually want to hear. Therefore, you need to be smart and wise to tackle the interview.

Here’s how you can ace it

Step 1: Prep-up by taking up coding challenges

Though it is difficult, you can prepare and assess yourself and evaluate whether you’re a potential candidate. Here’s what you can start doing:

Most employers might ask to solve coding challenges on HackerRank before they can even conduct the real interview. Before you might be caught unaware, it is advisable for you to start solving challenges and work on projects on HackerRank.

Engaging yourself with competitions on Advent of Code and Google Code Jam is an added advantage.

Also, having an array of machine learning projects to demonstrate to the employers can set you apart from the other candidates applying for the same job role.

Step 2: Apply for jobs

The next step is to start looking out for relevant jobs. Don’t randomly apply for jobs, ensure you make a checklist of the areas you’re an expert in and start seeking jobs based on your skillset.

Your resume should demonstrate more of projects and practical applications and skillset rather than just theoretical achievements. Remember the employer is looking for candidates with hands-on experience in the technologies. Make sure you have what it takes for them to hire you. With AI and machine learning being the talk of the town, people have started building skills in the field.

Source: PayScale

Step 3: Schedule an interview

Interviews might be challenging especially when it comes to getting a job as a machine learning engineer. You might be asked anything about the aspects of machine learning. Therefore, you need to start from the basics. They might ask you about algorithms, make sure you have solid points to back any answer you put out in front of your employers.

Give them a hint that you’re aware of deep knowledge in certain topics. Doing so gives them the leverage to choose you over other candidates.

Some of the common questions you’re likely to be asked are as follows. The list goes from easy to difficult questions:

  1. Explain the different types of machine learning?
  2. How would you explain what machine learning is to a kid?
  3. Explain the difference between machine learning and deep learning?
  4. What are classification and regression?
  5. Can you define overfitting and how can you avoid it?
  6. What’s the difference between a test set and a training set in machine learning?
  7. What is selection bias?
  8. Define precision and recall?
  9. Explain the confusion matrix?
  10. Is there a difference between KNN and K-means clustering? If yes, how do you explain?
  11. What would you do to handle missing data in a dataset?
  12. What are false positives and false negatives and how are they important?
  13. Explain naïve in a naïve bias classifier?
  14. How to choose the right machine learning algorithm for your classification problem?
  15. Can you explain pruning in the decision tree and how it is done?

Step 4: Prepare for technical tasks

Potential employers will most certainly give you a technical task to solve. These tasks are given just to check how capable you are in your tech skills. Therefore, you need to solve it patiently and don’t get anxious.

Below are a few tips to help you prepare for such tasks:

  • Describe the process: Keep it simple for the employers to judge the steps you’ve taken. Make sure to add things you could have improved if given more time. Jupyter NoteBook is perfect to get started with. It helps you describe and code at the same time.
  • Keep it simple: Always keep it simple when trying to solve any machine learning tasks.
  • Keep your code clear: The employers need to know which are the codes you’ve copied and which ones you’ve written by yourself. When solving a machine learning task, you get the liberty of choosing algorithms with their functionality to use. Therefore, you also need to note that you’re required to mention the significant functionality you’ve used.
  • Quality triumphs: Always choose quality over quantity. So, you need to adhere to certain ground rules – coding guidelines, documentation to your functions, and conventions. For this, you can use linter, a tool that helps analyze your code for style violations and bugs.
  • Unit tests: Part of being in the technical field is writing tests for every code you write.

The way forward

AI and machine learning are known as the technologies of tomorrow and will be critical for all businesses looking to stay relevant. Therefore, it would be ideal to say, that the highest-skill and the highest paying careers today fall under these technologies. As we move toward 2021 and beyond, there will be massive growth of opportunities across technologies like machine learning and AI.

Editorial Team
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