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How to Become A Machine Learning Engineer: Ultimate Learn Guide

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Team Careervira

22 September 2023

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machine learning engineer career path

Machine Learning (ML) is a highly coveted skill in today's tech industry. This learn guide aims to help you understand how to become a machine learning engineer. Explore the fundamentals, essential skills, career paths, and more to kick-start your journey in this exciting field.

Features

Table of Contents

  • 1. Types of Machine Learning

  • 2. Machine Learning (ML) Learn Guide: Introduction

  • 3. What does a Machine Learning Engineer do?

  • 8. Educational Requirements

  • 9. Top Skills Required to Become ML Engineer

  • 18. Final Take

  • 21. Explore Artificial Intelligence & Data Science Category

Machine Learning (ML) Learn Guide: Introduction

Machine Learning (ML) is a revolutionary sub-field of Artificial Intelligence (AI) that focuses on developing algorithms and models capable of automatically learning and making predictions or decisions from data without being explicitly programmed. By leveraging the power of statistical techniques and computational tools, ML algorithms can extract valuable insights, recognize patterns, and solve complex problems across various domains.

In recent years, the widespread availability of vast amounts of data, along with advancements in computing power and various advantages of Machine Learning has propelled its growth and applications. ML has become a vital part of our daily lives, from self-driving cars and virtual personal assistants (Siri and Alexa) to recommendation systems and fraud detection. It includes everything from video surveillance to facial recognition on your smartphone.

Today, companies across industries seek professionals who can help them make data-driven decisions and gain a competitive edge. The advantages of Machine Learning are abundant in industries such as banking & finance, retail and healthcare, paving the way for more opportunities. Thus, becoming an expert in machine learning is increasingly important for individuals looking to secure high-paying jobs and advance their careers. Many cutting-edge businesses have hired Machine Learning engineers to help them accelerate their growth.

Before moving onto the step-by-step path of how to become a machine learning expert let’s understand a few basics.

Types of Machine Learning

Machine Learning encompasses several different types of algorithms and approaches, each suited to different types of problems and data. Understanding these various types of ML is essential for aspiring Machine Learning engineers. 

Labeled data and unlabeled data are the two types used in machine learning. Although labeled data has both input and output parameters in a machine-readable pattern, labeling the data initially takes significant human effort. Unlabeled data may only contain one or no parameters in a machine-readable manner. This eliminates the need for human work but calls for more difficult fixes. 

Below mentioned are the three key categories for types of Machine Learning:

Supervised Learning

One of the most fundamental types of machine learning is supervised learning. Labeled data are used to train the machine learning algorithm in this case. Even though precise labeling of the data is required for this method to function, supervised learning is incredibly effective when applied in the appropriate situations.

  • Working
    In supervised learning, a small training dataset is provided to the ML algorithm. This training dataset, a smaller portion of the larger dataset, gives the algorithm a general understanding of the issue, the solution, and the data points that need to be handled. The training dataset gives the algorithm the labeled parameters it needs for the task and shares many properties with the final dataset.

    In essence, the algorithm establishes a cause-and-effect relationship between the variables in the dataset by looking for relationships between the parameters that have been provided. After training, the algorithm has a general understanding of the operation of the data and the link between input and output.

    The final dataset is then used to test this solution, and it learns from it, in the same manner it learned from the training dataset. This implies that supervised machine learning algorithms will improve even after being used, learning new relationships and patterns as it trains on fresh data.

Unsupervised Learning

The benefit of unsupervised machine learning is that it can use data that hasn't been labeled. By eliminating the need for human intervention, the software may now process the dataset machine-readable, even larger datasets.

  • Working
    Hidden structures are produced by unsupervised learning since it lacks labels on which to base their work. The algorithm understands relationships between data points; human input is not necessary. These hidden structures are what give unsupervised learning systems their flexibility.

    Unsupervised learning algorithms can adjust to the data by dynamically adjusting hidden structures rather than using a predetermined and stated problem statement. Compared to supervised learning techniques, this offers more post-deployment development.

Reinforcement Learning

Reinforcement learning directly mimics how people learn from data in their daily lives. It has a self-improving algorithm that adapts to new circumstances and learns from mistakes. Positive results are "reinforced" or encouraged, while negative results are "punished" or discouraged.

  • Working
    Based on the psychological idea of conditioning, reinforcement learning operates by placing the algorithm in a setting with an interpreter and a reward system. The output result from each iteration of the algorithm is provided to the interpreter, who determines whether the result is favorable.

    If the program finds the right answer, the interpreter strengthens the answer by rewarding the algorithm. If the result is unfavorable, the algorithm must repeat the process until a better conclusion is obtained. Most of the time, the efficacy of the outcome is closely related to the reward system.

    For example, when using reinforcement learning to find the quickest route between two points on a map, the solution is not always an absolute value. Instead, it adopts an efficiency rating with a % value. The algorithm gets rewarded more generously the greater this percentage value is. As a result, the software is taught to provide the best solution for the best reward.

What does a Machine Learning Engineer do?

A machine learning engineer specializes in developing and managing artificial intelligence systems and predictive models while working with vast amounts of data. However, the specifics will vary based on the size of a company and the broader data science team. The best way for aspirants looking for ways how to become machine learning engineer is to be familiar with the responsibilities of such professionals.

Some basic functions that all machine learning engineers perform include:

  • Constructing, creating, and studying machine learning systems, models, and plans.
     
  • Data science prototypes are being studied, changed, and converted.
     
  • Employing the results of statistical analysis to enhance models.
     
  • ML systems and models may be trained and retrained as necessary.
     
  • Recognizing data distribution variations that can impact a model's performance in practical applications. 
     
  • Data visualization for greater understanding.
     
  • Evaluating the ML algorithms' application cases and classifying them according to their likelihood of success.
     
  • Knowing when to use your insights to inform business decisions.
     
  • The improvement of current ML frameworks and libraries. 
     
  • Data quality assurance and/or verification through data cleansing.
     
  • Searching for and choosing acceptable data sets before beginning data collecting and modeling before beginning data collecting and modeling, search for and choose acceptable data sets.

Now that we are familiar with the role of ML engineering professionals, let’s move onto the part of how to become ML engineer that covers essential qualifications and skills required.

Educational Requirements

Machine Learning is a constantly growing field where data and algorithms are used to imitate how humans learn to improve accuracy. The transient skills that it offers and the lucrative opportunities that one can explore have made a lot of software developers switch careers to Machine Learning. The reason is that both are technical fields.

Let’s look at the first set of prerequisites that one must fulfill if they want to become an ML Professional:

  • Bachelor’s Degree
    A Bachelor's degree in Computer Science, Information Technology or a related field.
     
  • Master's Degree
    A master's degree in machine learning, although important, is not necessarily required for all jobs or roles. It can demonstrate to employers that you have a deep understanding of the field and are committed to developing your skills and knowledge.
     
  • Certifications
    Having machine learning engineering course certifications helps in having a good profile in front of recruiters. Professional Certificate Program in Machine Learning & Artificial Intelligence, Certificate in Machine Learning, IBM Machine Learning Professional Certificate, etc., are preferred.

Top Skills Required to Become ML Engineer

Now, look at the skills required for Machine Learning engineer. While the first two are foundational, and you may have even acquired them in high school or your undergraduate degree, they become more sophisticated and domain-specific as you proceed down the list.

  • Applied Maths
    A machine learning engineer's set of crucial skills includes math. Machine learning can make extensive use of mathematics. The right ML algorithm for your data can be chosen using a variety of mathematical formulas. It's very simple to understand them if you have a strong background in math. It is one of the basic requirements for most entry-level machine learning jobs.

    You should be familiar with important math concepts for data science and AI, including linear algebra, probability, statistics, multivariate calculus, and distributions like Poisson, normal, and binomial. A basic understanding of physics concepts is also helpful if you wish to work as a machine learning engineer.
     
  • Basics of Computer Science and Programming
    Computer science concepts, such as data structures (stack, queue, tree, and graph), algorithms (searching, sorting and dynamic programming), space and time complexity, etc., must be acquainted with you. You should know programming languages, including Python and R for Machine Learning, Spark and Hadoop for distributed computing, SQL for managing databases, Apache Kafka for pre-processing data, etc. Furthermore, it is ideal if you are familiar with Python's libraries, such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc. 
     
  • Machine Learning Libraries and Algorithms
    Several API Packages and libraries are available, including Google TensorFlow, Apache Spark's MLlib, and Microsoft's CNTK. Utilizing these technologies will require clear comprehension of how they might be connected with various systems and their potential dangers. Apriori Algorithm, Naive Bayes Classifier, K Means Clustering, Support Vector Machine, Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc., are some of the most popular ML algorithms. Before starting your career as an ML developer, understanding all these methods is a good idea.
     
  • Data Evaluation and Modeling
    Machine learning must process enormous volumes of data and use predictive analytics to make decisions. When working with large data quantities, data modeling and evaluation are crucial for creating efficient algorithms and determining how effective the final model will be. 
     
  • Neural Networks
    Neural networks are a branch of AI and are a succession of algorithms that set out to identify relationships in vast amounts of data. Machine learning has undergone a revolution thanks to neural networks. We require machine learning for jobs that are too difficult for humans to code directly or so complex that it is unfeasible.

    Neural networks can solve nearly any machine learning problem involving learning a complex mapping from the input to the output space because they are generic function approximations themselves. The most accurate method for solving various issues, including translation, speech recognition, and image classification, has been neural networks.
     
  • Natural Language Processing (NLP)
    Natural Language Processing is a vital skill for Machine learning that tries to instruct computers in the complexity of human language, improving our understanding of human communication. The foundation for Natural Language Processing is the Natural Language Toolkit. These libraries can help computers understand natural language by breaking the text down according to its grammar, extracting key phrases, deleting unnecessary words, etc.
     
  • Software Development
    Machine Learning should be used with different technologies to realize its full potential. You must create algorithms and systems for machine learning that are simple to integrate and connect with other technologies. You must therefore possess good knowledge of Application User Interfaces (APIs) of many varieties (web APIs, dynamic and static libraries, etc.), as well as the ability to build interfaces that can withstand modifications in the future.
     
  • Communication Skills
    Your career trajectory can be greatly impacted by your ability to communicate effectively. This is because, even though you are the best at understanding the data and the insights produced by machine learning, it is equally crucial that you can communicate these findings to a non-technical team, your shareholders, or clients.

    This may also mean employing data storytelling, wherein you must present your data narratively with a beginning and an end at specific outcomes you have discovered by applying machine learning to the data. This is so that a firm may focus more on the actionable insights drawn from the data, which are ultimately more essential to a company than the data analysis.

To gain or hone such skills, you can easily take help of online machine learning engineer courses offered by leading course providers. 

Beginner

Intermediate

Advanced

Final Take

Machine Learning is being used in practically most of the industry these days. This includes industries such as pharmaceuticals, cybersecurity, marketing, etc. The size of the global market for machine learning as a service, projected at USD 15.47 billion in 2021, is expected to increase at a compound annual growth rate (CAGR) of 39.3% from 2022 to 2030, as per a report by Precedence Research.

It clears the fact that being a Machine Learning engineer and studying more about ML are excellent ideas and extremely good career choices. Thus, you should begin your journey in Machine Learning to build a rewarding and exciting career for yourself.

We at Careervira assist you in finding and comparing the best machine-learning courses from leading partners and institutes, getting in-depth rankings and analyses of the best machine-learning programs, and getting career and educational guidance from top professionals and subject matter experts.

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For comprehensive learning, Machine Learning courses are available at Careervira that helps learners gain all the industry-relevant skills from beginner to advanced. Learn at different pace modes in all major formats, including certifications, labs, bachelor's programs, etc. Upgrade your professional journey to showcase your expertise to potential employers. 

 

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Features

Table of Contents

  • 1. Types of Machine Learning

  • 2. Machine Learning (ML) Learn Guide: Introduction

  • 3. What does a Machine Learning Engineer do?

  • 8. Educational Requirements

  • 9. Top Skills Required to Become ML Engineer

  • 18. Final Take

  • 21. Explore Artificial Intelligence & Data Science Category