How to Become a Machine Learning Engineer | A Complete Learning Path

Ever since the companies have realized that the regular software are not going to address the growing competition and that they need something additional to pull them, concepts like Data Science and Machine Learning have started gaining momentum.

Whether it is Voice Recognition based searching, Fraud Detection Systems, or a Recommendation System by Amazon or Netflix, Machine Learning has been the most implemented technology over the period of time.

This is the reason every company wants to hire Machine Learning Professionals and a huge crowd of aspirants wish to become one. Let’s uncover the right way anyone can pursue this field!

In this blog we will uncover following aspects of Machine Learning:

  • Definition and Applications of Machine Learning
  • Data Science Vs Machine Learning
  • Why Should You Learn Machine Learning?
  • Industry Trends and Future Scope of Machine Learning
  • Role of Machine Learning in Business
  • Detailed Analysis of the ‘Machine Learning Engineer’ Profile
  • Prerequisites to Become a Machine Learning Engineer
  • Learning Path for Machine Learning Engineer
  • A Snippet of Machine Learning Engineer Resume

What is Machine Learning?

Well, speaking broadly, Machine Learning is the field that deals with educating the machines to make them able to make decisions like humans. But, that is not enough; as it is too broad to help you understand the purview of Machine Learning.

Definition of Machine Learning

So, here are several definitions on Machine Learning:

As per SAS,Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”

As per IBM, “Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process.”

As per Google, “Machine learning is functionality that helps software perform a task without explicit programming or rules. Traditionally considered a subcategory of artificial intelligence, machine learning involves statistical techniques, such as deep learning (aka neural networks), that are inspired by theories about how the human brain processes information.”

So, if we combine all of them together and try to come to a common ground, the definition would be:

“Machine Learning is the field that is a subset of Artificial Intelligence, is a process that deals with educating a computer system so that it learns from its own feedback, instead of having to explicitly program it for every task.”

Applications of Machine Learning

    1. Image Recognition: Machine Learning is used for Image recognition. It is one of the most popular applications of Machine Learning. Identifying objects like persons, places, etc., on the images are done using Machine Learning Techniques. Facebook uses Machine Learning for Auto-friend Tagging Suggestion.
    2. Virtual Assistance: Various Virtual Assistance Systems like Cortana, Siri, Alexa recognize and respond to Natural Language using Machine Learning Algorithms. ML Algorithms decode Natural Language voice instructions and act accordingly.
    3. Email Spam and Malware Filtering: Whenever a suspicious mail arrives it lands on Spam folder. Any mail that violates the filtering rules, Machine Learning Algorithms push them to junk folder. It also saves the users from unnecessary malware attacks.
    4. Self-driving Cars: Companies like Google and Tesla are manufacturing Driverless cars that do not require human drivers. This is done by Machine Learning and Deep Learning Algorithms that help Cars to make decisions like humans.
    5. Speech Recognition: Google’s voice-based search option works on Machine Learning and Deep Learning Algorithms. Understanding the Natural Language and fetching the web results based on indexed words from the lexicon  
    6. Automatic Language Translation: Similar to Speech Recognition, Automatic Language Translation deals with Natural Language Processing and works on Machine Learning Algorithms.

Data Science Vs Machine Learning

Quite often it has been observed that people, especially the aspirants, get confused between the concepts of Data Science and Machine Learning. This typically affects the way they approach to enter either of these domains. Let’s see how they are different from each other!

Above table was a general and wider knowledge on the comparison of the two widely popular streams, i.e., Data Science and Machine Learning. In order to get a deeper insight on Machine Learning and the Career Scope offered by this field, keep reading!

Get your fundamentals of Machine Learning with the blog Data Science Vs Machine Learning Vs Data Analytics, and come back to these interview questions to brush up your skills.

Why Learn Machine Learning?

Machine Learning is not a new area in technology. It was present even few decades back. Initially limited to Pattern Recognition and Model Building, Machine Learning now deals with complex Computational Models and feeding Big Data to make the machines intelligent has gained momentum quite recently.

Machine Learning Techniques are applied in almost every industry for various purposes in various ways. Industries like Banking, Information Technology, Healthcare, Retail, Transportation, etc., have begun evolving into more customized service providers, with the help of Machine Learning.

This is probably the reason Machine Learning has become a lucrative domain for many aspirants who see a dynamic future in it. Let us unfold the ways Machine Learning is helping various companies proliferate business and witness growth! Also, we will find out what all companies are hiring Machine Learning professionals.

Industry Trends and Future Scope of Machine Learning

In July 2019, Gartner published a Hype Cycle for Artificial Intelligence, which highlighted the emerging trends to keep an eye on, in the domain of AI. Take a look:

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It is quite clear from the above graph that technologies like Adaptive Machine Learning, Edge AI, Edge Analytics, Graph Analytics, Autonomous Driving Level 4 &5, etc., are booming and hence have quite a bright future in the span of 2 to 10 years.

Moreover all these technologies are still in their nascent stage, and are predicted to again see a boom post the period of 10 years, which clearly hints the way Machine Learning is being adopted across the industries.

Description: F:\Shravani\Data Science Campaign\Main Blogs\How to Become a Machine Learning Engineer\machine learning graph.jpeg

In the Bar Chart above, the numbers clearly hint that most of the companies across the globe are aggressively spending around $28.5 Billion in Machine Learning Applications. The next highest expenditure by the companies is on the Machine Learning Platforms that goes to approximately $14.4 Billion.

The Global Machine Learning Market is expected to expand at 42.08% CAGR during the forecast period 2018–2024. – Market Research Future

Description: F:\Shravani\Data Science Tutorial\Tutorial 3\Machine_Learning_Market_fig_1.jpg

As per the ongoing trend, the cloud services are on the rise and the unstructured data is growing every second. This will lead to the companies deploy Machine Learning Solutions even more widely. Tech behemoths like Amazon, Google, Microsoft, etc., are going to invest heavily in the AI-based technologies in future.

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Role of Machine Learning in Business

Machine Learning has become an imperative part of businesses, helping them perform critical tasks that they had not been able to before the evolution of this concept. Let’s take a look at how Machine Learning helps businesses!

    1. Financial Services: Various financial institutes use Machine Learning for various purposes. The two major applications are Fraud Detection and Stock Market Trading. Financial Institutes use Machine Learning Techniques to identify the transactions that are not genuine. Various measures like fake accounts, fake IDs, log in and log out time, etc., identify and differentiate a fraudulent activity using Machine Learning Techniques.
    Financial Institutes also engage in trading in Stock Markets, where making a secure prediction is highly important. That is when Machine Learning plays a huge role by analyzing the market, giving the right insight about the market scenario.
    Apart from the above two applications financial Institutions also use Machine Learning to Profile the High Risk Clients and formulate strategies for that.
    2. Healthcare: Healthcare is one of the fastest growing industries that have tapped almost all the segments of common public. With growing complexity of illnesses and lack of time to treat the people, Machine Learning has given ways to Diagnose and Treat the Patients with utmost accuracy and security.
    For an instance, Machine Learning has actually help diagnose the Covid-19 patients with the help of CT Scan visuals. Not only this but various Pharmaceutical companies are trying to find the right molecule, the antibiotic that can be a potential treatment for this disease.
    Medical researchers and experts use various Machine Learning methods to Anticipate the Future Health Conditions and raise the red flag whenever necessary.
    3. Retail: Retail is one of the most dynamic industries that require constant attention on smallest of details. This is why Machine Learning was been introduced for handling functions like Product Recommendation, Managing Inventory Level, Formulating Routing Strategies, and Anticipating Product Demand.
    Now that most part of retailing has gone digital, the importance of Machine Learning has increased like never before. Recommending products to the customers, keeping their purchase history and likes in mind has become the prime application of Machine Learning. Apart from this, anticipating the demand of products and maintaining the inventory levels accordingly is another task that is done on a regular basis and way easily with the help of Machine Learning Techniques.
    Not just this, but operations like formulating a marketing strategy, crafting a price optimization plan, etc., are all handled by Machine Learning methods.
    4. Manufacturing: Manufacturing firms are also utilizing Machine Learning Techniques to benefit at plenty of areas. Some of them are General Process Improvement, Product Development, Quality Control, and much more.
    Machine Learning has proven to be extremely beneficial when it comes to detecting issues that can affect the production lines, increase waste, and decrease efficiency. Machine Learning detects bottlenecks in the unprofitable production lines and helps formulate strategies to deal with the underlying issues.
    One of the most popular applications of Machine Learning in the production domain is coming up with new product ideas. Machine Learning certainly takes the centre stage in this matter by facilitating deep insights about consumer data that helps design and plan the stages of new product development or improve the existing one.
    Machine Learning can also help a company achieve better quality in manufacturing by detecting the anomalies in products and packaging, thereby helping the companies stop the defective products from reaching the market. Also, with the help of Machine Learning and IoT, companies can analyze the performance of all the equipments, which in turn helps in predictive maintenance.
    5. Transportation: Machine Learning has given a whole new dimension to the Transportation Industry. Some of the major benefits this industry has witnessed are ability to take the entire transportation business digital through Real-time Location Updates and Real-time Traffic Updates.
    Machine Learning has helped companies like Uber to find the shortest route possible with lowest traffic congestion. The methods of Machine Learning have also allowed companies to maintain security by tracking the travel in real-time keeping driver’s profile and customer’s profile in transparency.
    Not only this but companies like Google and Tesla are coming up with driverless cars that are totally based on Machine Learning and Deep Learning Techniques.
    6. Oil and Gas: Though Oil and Gas Industry is a late adopter of Machine Learning, but slowly this industry has come up with advanced mechanisms that are benefitting widely. Some of the major ways Machine Learning is helping this industry are Accurate Modeling and Drilling Automation.
    Machine Learning allows the firms to analyze the signals and noise in lesser time and create geological models based on which the accurate prediction can be made about what is beneath the surface.
    Moreover, Machine Learning helps drilling operations easier where a lot of workforce is deployed and everyone is carrying out different operations. In order to smooth out and lessen the burden Machine Learning helps monitoring all the operations in real-time and helps run the entire operation in a smoother and safer way.

Companies Hiring Machine Learning Engineers

Following graph will give you a clear hint on the top companies that are hiring Machine Learning Engineers and paying skyrocketing paycheck.


Clearly the top 3 companies paying the highest to Machine Learning Engineers are Selby Jennings, Twitter, and DoorDash. On the other hand, some of the cities that are paying the highest amounts to Machine Learning Engineers are:


As it is visible that the top 3 locations in U.S. that are the melting pots for Machine Learning Engineers are San Francisco, Bellevue, and New York.

Let’s take a look at the profile of Machine Learning Engineer in a better way!

Career Path to Become a Machine Learning Engineer

Machine Learning is a domain that deals with algorithms and complex datasets to make a machine intelligent. In other words, Machine Learning evolves a system in a way that it learns from its own outputs or feedbacks, just like humans do.

Before we discuss the job roles offered in the field of Machine Learning, you need to take a closer look at following Venn diagram:

Description: C:\Users\satya\Downloads\Artificial Intelligence vs Machine Learning vs Data Science - Diagram.jpg

From the above diagram it is clear that Machine Learning and Data Science, both are the subsets of Artificial Intelligence. Therefore, there are roles, responsibilities, skills, and techniques that are shared between these two domains.

Let’s take a look at the Different Roles Offered in the Area of Machine Learning:

    1. Machine Learning Engineer: Machine Learning Engineers create AI-based solutions that let machines to perform certain tasks without human intervention. Basically they develop various machine learning programs with the help of library files.
    This way some of the mandatory skills to be acquired by Machine Learning Engineers are Programming languages like Java, Scala,and Python, statistics, Mathematics,etc.We will discuss about Machine Learning Engineer in detail in following sections.
    2. Data Scientist: Data Scientists are the professionals who wrangle with the data to solve a business problem.
    For example, a Sales firm gets a plethora of data from multiple sources, like Direct Customer interaction, Digital interaction, Feedback forms, bills, ledgers, cost sheets, balance sheets, and much more. Now if the firm wants to invest in various marketing strategies to attract more customers, it has to know what kind of activities would pay-off. For this, the firm has to analyze the past and on-going data to understand customer preferences, which would in turn suggest the best marketing strategy to be deployed.
    A Data Scientist must be equipped with Data Mining skills, R, Scala, SQL, Mathematics, Python, etc.
    3. NLP Scientist: NLP stands for‘Natural Language Processing’.NLP Scientists develop machines that are able to understand the natural language and translate it into other spoken languages.
    For example, the voice assistants like Siri, Cortana, and Alexa understand the natural languages and are able to communicate to humans.
    The prime responsibility of NLP scientists is to develop a machine that recognizes the language patterns and patterns of speech. It mandates the NLP Scientists to master various natural languages with respect to grammar, spelling, syntax, etc.
    4. Business Intelligence Developer: ABusiness Intelligence Developer can be understood as the professional who collects, analyzes, and interprets huge amounts of data in order to draw actionable insights that can be used to solve a business issue.
    Therefore, a Business Intelligence Developer should have a sound knowledge of Relational and Multidimensional Databases, SQL, Python, Scala, Power BI, etc.
    5. Human-Centered Machine Learning (HCML) Developer: A Human-Centered Machine Learning Developer is a professional who is responsible for developing systems that can process the information based on Human-based Machine Learning Algorithms and recognize the patterns. This way the machines learn the preferences of humans without having to execute complex programs.  
    A HCML Developer should be equipped with TensorFlow, JavaScript, etc.

Who is Machine Learning Engineer?

A Machine Learning Engineer can be defined as a professional who ensures that the models developed by Data Scientists are running without obstacles and producing accurate information at the right time. Due to the work nature, Machine Learning Engineers operate in close association with the Data Scientists. Data Scientists lay out the design of the entire system which is then designed by Machine Learning Engineers.

In order to perform this task, Machine Learning Engineers use big data tools and various programming frameworks. They also make sure that the data pipeline through which raw data travels to the data warehouses and databases, are working smoothly, and are ready to be scaled-up.

For an instance, Machine Learning Engineers’ job is to design the programming so that the search results fetch the appropriate results.

Let’s take a better look at the profile of a Machine Learning Engineer in following sections!

Roles and Responsibilities of a Machine Learning Engineer

A Machine Learning Engineer is responsible for carrying out following jobs:

    1. Develop the models that have the potential to improve the machine learning systems.
    2. Monitor and expand the models, build the datasets and streamline the parameters to accelerate the system performance.
    3. Develop software that can improve the experimentation and allows making better business decisions.
    4. Build the tools for analysis and simulations that can understand the process of complex systems.
    5. Apply Machine Learning techniques to resolve new and critical areas.

Take a look at the following job description posted by Amazon:

Source: LinkedIn

Above Amazon Job Description clearly shows that Machine Learning Engineers strive to solve complex problems with the help of scalable Machine Learning algorithms and models.

Salary of a Machine Learning Engineer

Machine Learning is not a new domain. However with the advent of smart technologies and growing need of tailored services the need of this concept has witnessed a huge spike in the trend. As per the IT industry standards, Machine Learning Engineers are some of the highest paid professionals of the industry bagging way more than average IT employees.

As per LinkedIn Reports, there are 6,650 Job Posts for Machine Learning Engineers only in the U.S.

Source: LinkedIn

As per the above bar graph, the Median Salary or the Average Salary drawn by the Machine Learning Engineers is $1,25,000 annually.

The top 3 industries offering highest salary packages to the candidates are Consumer Goods, Hardware & Networking, Software & IT Services. The top 3 locations hiring Machine Learning Exerts in highest packages are San Francisco Bay Area, Greater Seattle Area, and New York City Metropolitan Area.

Prerequisites to Become a Machine Learning Engineer

 Machine Learning Engineer is a domain that requires the professional to be equipped with Software Engineering Skills as well as Machine Learning Skills.

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Must-Have Skills: Some of the must-have skills for a Machine Learning Engineer are:

    1. Programming: A Machine Learning Engineer develops various applications that perform certain tasks without requiring an intervention. Therefore, the first and foremost skill that a Machine Learning Engineer should be equipped with is Programming. Programming Languages like Java, C#, C++, and Python are on the priority for a Machine Learning Professional.
    Among all the other programming languages, Python is considered to be the most important one for the domain of Data Science and Machine Learning. A sound knowledge of Python Libraries and various Python Frameworks is of utmost important for a Machine Learning Engineer.
    Choosing the right language to start off your journey in Machine Learning is very important. Read this blog SQL For Data Science | Python, R, Hadoop, & Tableau | What Should You Learn? And make the right decision right away!
    2. TensorFlow: In order to become a Machine Learning Engineer an aspirant must learn the deep learning libraries like TensorFlow and Keras. Learning these frameworks allow the professional to implement Neural Networks and Reinforcement Learning more efficiently.
    3. Distributed Computing: A Machine Learning Engineer works towards scaling the systems to implement the process at the enterprise level. For this purpose, she has to have sound knowledge of Distributed Computing and Frameworks that can allow her to harness the power of cloud computing-based servers or different on-premise servers.
    4. Machine Learning Algorithms: For a Machine Learning Engineer to work smoothly, she has to have a command of Machine Learning Algorithms which are helpful in developing models and getting the desired outcome. Hence, having a good grasp of old algorithms like Regression Modeling, Random Forest, etc., or new inventions likeas XGBoost and Catboost, etc., are required for a Machine Learning Engineer.

Good-to-Have Skills: In addition to the must-have skills, there are some skills possessing that will certainly help Machine Learning professionals to climb the success ladder smoothly. Take a look:

    1. Spark/Hadoop: Distributed Systems end up processing huge volumes of information which cannot be processed without using Big Data Frameworks.Hence, a familiarity with Big Data technologies like Hadoop and Spark are very important for a Machine Learning Engineer to learn and master. Frameworks like PySpark and SparkML are widely popular among the Machine Learning professional. Hence learning them will give an added advantage to the professional.
    2. Data Wrangling: A Machine Learning Engineer should not only spend time learning the programming and developing skills, but should also know how to wrangle with data. They should know how to clean up the datasets and how to validate and manipulate it to process further using Machine Learning Algorithms.
    3. Data Storage and Pipeline: In order to work with data, the knowledge of database technologies is highly important. Knowledge of relational and non-relational databases and database query languages like SQL are highly important. At the same time, working with complex datasets and modeling using various machine learning algorithms to establish a coherent data pipeline.
    4. Attention to Detail: A Machine Learning Engineer should be someone who has eyes for details. She should be able to find the patterns in data, analyze the models, monitor the performance of entire system, and upscale or improve the performance wherever required.
    5. Teamwork: Automating a task is not a one person job. It requires a team to develop the entire business situation and create a solution that can run without intervention. It requires Data Scientists, Machine Learning Engineers, and Software Engineers to build and execute the entire system smoothly. Therefore, a team spirit is highly required.
    6. Communication Skill: Diving into the technical aspects will pay-off only when they are communicated and resented well. Therefore, having strong communication skill and presentation ability in the Machine Learning Engineers cannot be ignored.

Learning Path for Machine Learning Engineer

Becoming a Machine Learning Engineer requires an aspirant to follow a certain pathway. Let’s take a look at what you have to do to become a Machine Learning Engineer:

    1. Learning the skills: Learning the required skills begin with getting the right educational degree. Someone who wishes to become a Machine Learning Engineer should get a Master’s Degree or Ph.D. in computer science and engineering as merely getting a Bachelor’s degree will not suffice. Getting a higher education will expose the aspirants to advanced technologies, distributed computing, programming and computer architecture.
    You can also reach out to Online Training Platforms that provide trainings on various technologies and provide certifications too. This works better as the companies look for candidates who are skilled and certified.
    2. Gaining Experience: In order to gain the hands-on expertise, Machine Learning Engineers need to work on theoretical and practical aspects models. Platforms like Github and Kaggle work the best for freelancer Machine Learning professionals too. They can brush up their skills and understand the real-world scenarios for various projects.
    3. Acquiring a Job: As soon as you gain the skills and acquire the experience, applying for the job becomes easier. If you are a fresh graduate, there are more chances that you will get a position of Junior-level Machine Learning Engineer will be expected to work on the applications and data wrangling activities. As you go up the ladder, you will be expected to lead a team of juniors and work alongside the team of Data Scientists and Software Engineers.

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Machine Learning Engineer Resume Sample

Take a look at the Resume Sample for Machine Learning Engineer to build a similar one for yourself!

Description: F:\Shravani\Data Science Campaign\Main Blogs\How to Become a Machine Learning Engineer\image2.png

Source: VelvetJobs

This course for Artificial Intelligence and Machine Learning is just the right package for Machine Learning aspirants to land a high-paying job in no time.

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