The Ultimate Machine Learning Engineer Career Path for 2024

Here’s the perfect Machine Learning Engineer Career path you need to follow 2024!

The Ultimate Machine Learning Engineer Career Path for 2024
 |  BY Daivi

Did you know that the global machine learning market, according to Fortune Business Insights, is expected to reach a whopping $152.24 billion in 2028? Machine learning, unlike other fields, has a global reach when it comes to job opportunities. The machine learning career path is perfect for you if you are curious about data, automation, and algorithms, as your days will be crammed with analyzing, implementing, and automating large amounts of knowledge.


End-to-End Speech Emotion Recognition Project using ANN

Downloadable solution code | Explanatory videos | Tech Support

Start Project

Having a deep interest in machine learning is one thing; applying it in the real world is another thing. This guide will help you better understand the overall approach and the specific skillset necessary to begin working as a machine learning professional.

Evolution of Machine Learning Applications in Finance : From Theory to Practice

Here’s Your Ideal Machine Learning Engineer Career Path

There are a few prerequisites you should focus on when you start your machine learning career path. These include the skills needed for a machine learning career, a few machine learning projects for practice to develop those skills, and a clear grasp of the different types of machine learning careers available.

Machine Learning Engineer Career Path

Skills Required to Build a Machine Learning Career

The skillset of a machine learning expert is versatile and varies based on the numerous machine learning job responsibilities. However, there are a few core areas that every individual seeking a job in the machine learning domain must focus on, such as programming skills, statistics, mathematics, Computer Science fundamentals, and so on.

ProjectPro Free Projects on Big Data and Data Science

1. Computer Science Basics and Programming Knowledge

Machine learning demands high-level data computation; therefore, one of the most fundamental skills needed for becoming a good machine learning professional is to have strong conceptual knowledge in computer science. This includes knowledge of data structures (such as stack, queue, tree, etc.), various algorithms (such as searching, sorting, etc.), space and time complexities, and other CS principles.

A Machine Learning professional needs to have a solid grasp on at least one programming language such as Python, C/C++, R, Java, Spark, Hadoop, etc. Even those with no prior programming experience/knowledge can quickly learn any of the languages mentioned above. Amongst all the options, Python is the go-to language for machine learning. To develop machine learning systems, you can easily leverage NumPy, Pandas, Matplotlib, and Scikit-learn.  Also, you will find many Python code snippets available online that will assist you in the same. Once you’re confident enough with your theoretical knowledge of Python, you can try gaining some hands-on experience by working on a few Python projects as well.

Get Closer To Your Dream of Becoming a Data Scientist with 150+ Solved End-to-End ML Projects

2. Statistics and Probability

Many machine learning algorithms are applications derived from basic statistical techniques, so it is pretty simple to follow them if you have a solid base in Statistics. You need to possess knowledge of statistical concepts, including various measures (mean, median, variance, and so on), distributions (uniform, normal, Poisson, etc.), and analysis methods (ANOVA, hypothesis testing, etc.). Good knowledge of probabilistic topics such as conditional probability, Bayes rule, likelihood, Markov Decision Processes, etc., is also an essential skill to pursue a machine learning career.

3. Data Modeling

Analyzing unstructured data models is one of the key responsibilities of a machine learning career, which brings us to the next required skill- data modeling and evaluation. Having a solid knowledge of data modeling concepts is essential for every machine learning professional. They need to analyze the data structures, identify patterns, and then evaluate the data with the help of a suitable algorithm such as a classification algorithm, clustering algorithm, etc.

Ace Your Next Job Interview with Mock Interviews from Experts to Improve Your Skills and Boost Confidence!

Data Science Interview Preparation

4. Machine Learning Algorithms

Another critical skill required for a Machine Learning professional is, having a good grasp of the commonly-used machine learning algorithms and knowing how to apply them effectively. Supervised, Unsupervised, and Reinforcement Machine Learning Algorithms are the three most prevalent forms of machine learning algorithms. These algorithms can further be divided into- Naive Bayes Classifier, K Means Clustering, Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc. If you’re willing to start your career in the machine learning domain, you should have a solid understanding of all of these algorithms.

There are numerous machine learning libraries/packages/APIs support machine learning algorithm implementations such as scikit-learn, Spark MLlib, H2O, TensorFlow, etc. The practical application of these algorithms involves three stages-

  • selecting an appropriate model (decision tree, neural networks, etc.),

  • picking the suitable learning method (linear regression, gradient descent, and other model-specific methods), and

  • deeply understanding the working of hyperparameters tuning.

Machine Learning Projects for Practice to Hone Machine Learning Skills

You can acquire theoretical knowledge and develop the skillset essential for pursuing a career in machine learning. Knowledge and skillset can be used only by working on some beginner-friendly projects. Hands-on experience is essential because it enables you to comprehend the practical applications of all the theoretical knowledge you've gained so far. You can work on multiple end-to-end machine learning projects to understand machine learning applications across various business use cases. 

Here are a few beginner-friendly machine learning projects with source code that you might want to explore-

1. Rossman Store Sales Prediction Project

This sales forecasting project will focus on building your beginner-level machine learning skills such as the implementation of regression algorithms. It will teach you how to handle outliers, how to explore and visualize data by creating heatmaps, etc. You will also be introduced to the concept of feature Selection and its importance, in this machine learning project.

At least seven nations on the European continent have more than 3000 Rossmann drugstores, employing over 50K individuals. The Rossmann Stores dataset is one of the most popular datasets used by Data Science beginners. You can use the dataset and the linear regression machine-learning algorithm to forecast retail sales in this project. Also, you should try to analyze which factors influence sales fluctuations.

Source Code: Rossman Store Sales Prediction Project

Get FREE Access to Machine Learning Example Codes for Data Cleaning, Data Munging, and Data Visualization

2. NLP Chatbot Application

Building a chatbot application is a beginner-friendly machine learning project to explore the machine learning domain. This is mainly because it will walk you through various machine learning algorithms such as Naive Bayes, Decision trees, etc. By practicing this project, you’ll get familiar with various NLP techniques such as lemmatization, tokenization, part of speech (POS tagging), stemming, etc. For this project, use the Python NLP library NLTK, as well as neural networks, to create your chatbot engine function from the ground up.

Source Code: NLP Chatbot Application using NLTK

3. Loan Eligibility Prediction Project

This intermediate-level project will teach you machine learning aspects such as feature engineering, performing in-depth exploratory data analysis, etc. In this project, you will also learn how to design statistical models using boosting algorithms like Gradient Boosting and XGBoost, as well as metrics like the ROC Curve and MCC scoring. You will train and test the data model using the cross-validation method. The model must be trained using a dataset that includes marital status, number of dependents, income, qualifications, credit card history, etc. You can use the SYL bank dataset for this project.

Source Code: Loan Eligibility Prediction Project using ML

Here's what valued users are saying about ProjectPro

ProjectPro is a unique platform and helps many people in the industry to solve real-life problems with a step-by-step walkthrough of projects. A platform with some fantastic resources to gain hands-on experience and prepare for job interviews. I would highly recommend this platform to anyone...

Anand Kumpatla

Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd

Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic was "Credit Risk Modelling". To understand other domains, it is important to wear a thinking cap and...

Gautam Vermani

Data Consultant at Confidential

Not sure what you are looking for?

View All Projects

4. Credit Card Fraud Detection

Credit card companies take various precautions to guarantee that cardholders are not victims of fraudulent transactions. Even still, a few of their clients fall for them. Therefore, companies try to detect such transactions to avoid being unfair to their customers. This advanced-level machine learning project teaches you how to handle outliers, perform basic EDA. By working on this project, you’ll gain a better understanding of the ‘near-miss’ technique as well as the ‘SMOTE’ technique which are used to handle imbalanced data. It’s a great project to enhance your knowledge of machine learning algorithms. This is because it shows you how you can implement multiple machine learning algorithms such as logistic regression, SVM, etc. to detect fraudulent transactions from the given dataset. You should use the Credit Card dataset from Universite Libre de Bruxelles for this project.

Source Code: Credit Card Fraud Detection

5. Customer Churn Prediction

Machine learning algorithms for churn prediction can enable enterprises to maintain customers who are most likely to leave. In this advanced-level project, you will learn how you can leverage classification algorithms like the Ridge classifier or the Random Forest classifier to create your prediction model. With help of this project, you’ll be taught the application of performance metrics like AUC, Recall, F1-score, etc. You will also learn how matplotlib graph plots can be used to visualize the data for various properties. Visualizing the data will assist you in gaining a better understanding of the dataset. For this project, you can use the Telecom Dataset for Churn Prediction available on Kaggle. 

Source Code: Churn Prediction in Telecom Using Machine Learning 

You can explore many more open-source machine learning projects and upskill yourself before starting a career in Machine Learning.

Machine Learning Careers to Pursue in 2024

1. Machine Learning Engineer

The Machine Learning Engineer career path is one of the most desirable and potential career paths in Data Science. Machine Learning Engineers are mainly responsible for applying machine learning algorithms and tools to design and develop scalable machine learning models by statistical analysis. They must use their fundamental knowledge of computer science and mathematical skills to provide effective project outcomes. Machine Learning engineers are often required to collaborate with data engineers to build data workflows. Also, you need to gain an excellent understanding of Scala, Python, and Java to work as a machine learning engineer.

In the US, the average annual pay for a machine learning engineer is $133,196. 

Here’s a 7-step guide to becoming a Machine Learning engineer.

2. NLP Scientist

NLP scientists are in charge of inventing and developing technologies and software to learn human speech patterns and translate spoken words into other languages. The goal behind this is to enable machines to understand human languages in the same way that humans do. A Natural Language Processing Scientist creates technologies that grasp human languages to communicate successfully with humans. This machine learning career necessitates a thorough understanding of at least one human language and how machines operate. 

The average annual income of an NLP scientist is $145,000 per year or $74.36 per hour in the US.

3. Machine Learning Cloud Architect

The key responsibility of a cloud architect involves overseeing an organization's cloud platform. As cloud technologies get more advanced, this profession will continue to rise. Experience in architecting solutions in AWS and Azure and knowledge of configuration management systems like Chef/Puppet/Ansible are among some of the required skills for cloud architects. Recruiters often seek a strong grasp of coding languages such as Go or Python and expertise with monitoring technologies such as AppDynamics, Solarwinds, etc., in a Machine Learning Cloud Architect.

In the United States, the average compensation for a Cloud Architect is $137,265 per year.

4. Data Engineer

Data engineers develop and maintain the data platforms that machine learning and AI systems rely on. Their primary task is to create information systems for the following purposes- data acquisition, data process development, data conversion, data mining, and data pattern discovery, etc.

A data engineer's average annual pay in the United States is $116,950, with a $5,000 cash bonus.

Explore More Data Science and Machine Learning Projects for Practice. Fast-Track Your Career Transition with ProjectPro

Career Path Comparison: Machine Learning vs. Data Science

Although these two domains are often considered the same, if you specialize in either of these, you are well aware that there is a thin line of difference between the two career paths. Although both fields involve working on machine learning algorithms, the remaining factors in the two areas can be somewhat different. 

In more detail below, let us go over the differences between the data scientist career path and the machine learning career path in specialization, job roles, salary, required key skills, and roles/responsibilities.

Machine Learning vs. Data Science Career Path

1. Specialization of Machine Learning and Data Science Experts

Computer Vision Engineers, NLP Scientists, and other Machine Learning roles demand specialization in areas such as NLP techniques, predictive model construction, computer vision algorithms, signal processing techniques, and so on. Data Scientists, on the other hand, are experts in nearly every aspect of data science, including data mining and analysis, big data, statistical analysis, deep learning, and so on. In a nutshell, Data Scientists have a "know-it-all" approach!!

2. Job Roles and Average Salary

Having Machine Learning expertise can help you land jobs such as Machine Learning Engineer, NLP Scientist, Research Engineer, Machine Learning Cloud Architect, etc. On the other hand, Data Science experts can work as Data Scientists, Data Analysts, Data Administrators, Data Architects, etc.

A Data Science expert's average annual income is $96,961 (US) or ₹854,985 (IND), whereas a Machine Learning expert's average annual pay is $112,356 (US) or ₹732,099 (IND).

3. Skills Required

Some of the basic skills needed in a Data Science or a Machine Learning career are common, such as knowledge of programming languages (preferable Python or R), experience with popular frameworks such as Keras, Tensorflow, etc. Both, data science as well as machine learning professionals, require mathematical knowledge and data visualization skills.

Apart from these common skills, every machine learning professional must have knowledge of NLP, model deployment, AI, Signal Processing techniques, etc. In contrast, a Data Science professional needs data analysis skills and a critical thinking mind.

Access to a curated library of 250+ end-to-end industry projects with solution code, videos and tech support.

Request a demo

4. Roles and Responsibilities

A data science expert

  • takes up a business problem and employs various machine learning algorithms to build a solution,

  • conducts market research, and identifies patterns in a given dataset,

  • build deep learning models using frameworks such as Tensorflow, Keras, etc., and

  • performs data analysis and data cleaning activities, etc.

A machine learning expert

  • explores data science prototypes, and designs Machine Learning systems,

  • performs research and implements relevant machine learning algorithms and tools,

  • develop machine learning applications according to requirements,

  • performs statistical analysis and fine-tuning,

  • extends existing machine learning libraries and frameworks, etc.

Here’s a detailed comparison of the Machine Learning vs. Data Science career path.

How to Make a Mid Career Switch to Machine Learning?

Career transition at mid-level is not a linear progression, and one needs to work their way up through careful planning and thinking on how to hone the required skills. The following section will help you plan your transition into machine learning in a better way. 

How to Transition to a Machine Learning Career from Software Development? 

The transition from software development to machine learning may be relatively seamless and well-balanced for all software developers out there. This is mainly because you already have experience in a few machine learning areas while requiring additional work on a few others. Being a software developer, you already have a wide range of skills to take advantage of when making a career transition to machine learning. Knowledge of programming languages, particularly Python or R, is a plus and can smooth the career transition curve.. Still, the most significant benefit is that you can essentially present machine learning models to the audience in meaningful ways instead of an ordinary person who would have to download those models from open-source platforms (GitHub) and then deploy them.

Now, if you're transitioning to a machine learning career, the first skill you need to work on is your ability to acquire the correct data and analyze it in a meaningful way for your machine learning models. The next phase is to brush up on your fundamental knowledge, such as basic mathematical understanding, machine learning algorithms, and so forth. The third step is to learn how to use a popular machine learning framework like Tensorflow or PyTorch. These frameworks take care of the statistical modeling techniques, leaving you free to concentrate on building and structuring your machine learning model. The best way to learn all these skills is to apply them to a real-world business use case by working on enterprise-grade projects.

How to Transition to a Machine Learning Career from a Non-Tech Field? 

Coming from any non-IT background, say Mechanical Engineering, you might not be skilled at any programming language or coding, etc., which is one of the biggest challenges. Any machine learning career would require you to have efficient coding skills (preferably in Python). But, before you even start strengthening your coding skills, the first step is to mitigate the self-doubt that entering an IT-specific domain would be nearly impossible for you! Once you’re beyond that, you can start working on your programming knowledge and skillset. You should try working on some beginner-level machine learning projects and then move to intermediate-level projects. Apart from coding skills, you must be proficient in implementing machine learning algorithms and frameworks.

Explore Machine Learning Careers at Different Organizations

Google Machine Learning Careers 

Google is a dream company for anyone who wants to push the boundaries of technology. It seeks people with a diverse range of technical skills who are well-suited to resolve some of technology's most challenging problems and have a significant influence on millions around the world. Machine learning is used widely by Google developers. At Google, there are a variety of machine learning-related career roles available, such as Machine Learning Engineer, Research Scientist, AI/ML Solution Engineer, etc. A Google machine learning engineer is responsible for creating, developing, and deploying machine learning models and other effective machine learning methods to address many business concerns. As a Google research scientist, one must work on cutting-edge machine intelligence and machine learning systems and generate solutions for real-world, large-scale challenges. An AI/ML Solution Engineer at Google builds solutions to help Google Cloud differentiate itself among enterprise customers. They are assigned the responsibility of designing, implementing, and enhancing data analytics solutions for SAP with Google Cloud business by leveraging machine learning algorithms and data science methodologies.

Access Data Science and Machine Learning Project Code Examples

Amazon Machine Learning Careers

Amazon has a variety of machine learning job openings, including Software Developer, Research Scientist, Applied Scientist, etc. Designing and creating scalable, efficient, and extendable deep learning algorithms that are applied to complex challenges are among the key responsibilities of a machine learning software developer at Amazon. In addition, the job entails collaborating with Amazon AI's leading scientists and engineers to create high-quality, dependable software. Amazon expects a research scientist to develop system models in a virtual environment. An applied scientist is expected to collaborate with research scientists to create new tools and algorithms but then scale up the models developed by the research scientists. The massive deployment ability differentiates between a research scientist and an applied scientist at Amazon. 

Begin your journey by following the ideal career path for machine learning and working on unique machine learning project ideas. Hands-on experience working on projects helps one train data models and build a solid understanding of the various machine learning algorithms, tools, techniques, frameworks, etc.  We assure you will become one of the best machine learning professionals in the organization with a versatile machine learning skillset. 

 

PREVIOUS

NEXT

Access Solved Big Data and Data Science Projects

About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

Meet The Author arrow link