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How to become a machine learning engineer?
Machine learning and artificial intelligence are contributing immensely to empowering businesses and brands in this digital era. It can be a promising career for you if you have a knack for data, automation, and algorithms.
The market for machine learning is projected to grow to $152.24 billion in 2028 at a CAGR of 38.6%. If you are looking forward to becoming a machine learning engineer, you must brush up on programming and algorithms to advance professional growth in your career.
Read the blog to know all the aspects of the job and the machine learning engineer skills required to become a machine learning engineer.
Following are the summary points of our blog:
- The demand for machine learning engineers is rapidly increasing across industries to automate products with technological advancements.
- A machine learning engineer designs algorithms, develops systems, performs statistical analysis, etc., to deliver compelling business value.
- Proficiency in programming languages, machine learning platforms, and analytical skills are essential to becoming a machine learning engineer.
- Drafting an ATS-friendly machine learning engineer resume is required to present your skills and experience in the right manner for bagging the desired job.
- Possessing machine learning engineer certification and experience in real-world projects will give you an edge over the competition.
In our machine learning engineer resume guide, we have answered a few frequently asked questions:
- What is machine learning, and what does a machine learning engineer do?
- How to become a machine learning engineer?
- What are the best machine learning certifications to pursue?
- What are machine learning engineer projects required to enhance your resume?
Machine Learning Engineer Salary
- According to a trusted source Indeed, the average salary for a machine learning engineer is $141,712 per year in the United States.
- According to Payscale, the average machine learning engineer salary is $113,358 per year.
- According to Salary.com, the average Machine Learning Engineer salary in the United States is $121,233.
Also read: How much should a machine learning engineer be paid?
What is Machine Learning and What Do Machine Learning Engineers Do?
What is machine learning?
- Machine learning is a branch of artificial intelligence that facilitates systems to operate by using data and algorithms.
- ML applications focus on enhancing user experience without being explicitly programmed.
- Machine learning develops algorithms to allow computers to learn their functions automatically without human interventions by accessing data, gradually improving its accuracy and meeting business needs.
What does a machine learning engineer do?
- A machine learning engineer assesses, analyzes, and organizes complex datasets while designing & optimizing artificial intelligence systems, machine learning models, and algorithms.
- Machine learning engineering is a rewarding career for anybody who is an analytical thinker and can solve technical problems.
Roles and responsibilities of a machine learning engineer:
- Design Machine Learning (ML) systems
- Create and implement ML models and algorithms
- Select and verify data sets while choosing suitable data representation methods
- Develop machine learning applications as per client requirements
- Perform statistical analysis
- Develop deep learning systems adhering to business requirements
The roles and responsibilities of a machine learning engineer will vary across industries & organizations and can take different forms depending on the project type.
Fresher Machine Learning Engineer Resume Sample
How to Become a Machine Learning Engineer?
What are the skills and qualifications required to be a machine learning engineer?
A machine learning engineer is expected to possess a master's degree and sometimes a Ph.D. in machine learning or a related field.
A professional with a minimum of 1 year experience and a Bachelor’s degree in computer science or related field can also pursue their career in machine learning.
A deep understanding of advanced statistical modeling, analytical methods, deep learning, etc., is a critical component to become a successful machine learning engineer.
We have mentioned the machine learning engineer job description for your reference:
- Proficient in machine learning programming languages such as Python, Java, R, etc.
- Adept at using machine learning platforms such as Microsoft Azure, IBM Watson, Amazon, etc.
- Expertise in probability, data modeling, and strong problem-solving ability
- Ph.D. or MS degree in Computer Science, Statistics, Applied Math, Econometrics, or other related fields.
Source: Atlexsoft.com
Bagging the first job or finding new machine learning engineer jobs can be nerve-wracking. Years of professional experience, certification, and projects can go in vain if you fail to present them at their best in your machine learning engineer resume.
Senior Machine Learning Engineer Resume Sample
Machine Learning Engineer Certifications to Pursue in 2023
Technological advancements have exponentially increased the demand for machine learning engineers. Businesses are implementing ML features in their products to provide tremendous business value to their customers.
To ascend your career ladder, from entry-level to junior and then to senior ranks, you need to challenge yourself every day to pick up new skills and stay updated.
Pursuing Andrew Ng’ courses or any machine learning engineer certifications will showcase your curiosity in the field and showcase how updated you are with the latest industry trends to potential employers.
Here are the top 5 high-ranked machine learning engineer certifications to help you uplift your career and bag a high-paying job:
- Google Cloud Certifications: Machine Learning with TensorFlow
- Udacity Nanodegree: Artificial Intelligence for Trading
- Standford Online: Machine Learning Certification
- Coursera: Deep Learning with Python
- AWS: Machine Learning – Specialty
How to display machine learning certification in your resume?
Create a 'Certifications' section in your machine learning resume and list the following details:
- Name of the certification/course name
- Name of the certified authority
- Location of the institute of affiliation
- Date of both enrollment and course completion
Make a bulleted list and add your machine learning engineer certification in the format mentioned below:
Certifications | Certifying Authority | Time
Also read: When to add certifications on a resume?
Possessing machine learning engineer certification can give you an edge over others. Therefore you must present them flawlessly.
5 Machine Learning Engineer Project Ideas to Get You Started
In a saturated job market, it is crucial to stand out from the crowd. Having machine learning projects in your portfolio is convincing evidence of your talent and interest in machine learning. The employer knows why they should hire you over other potential candidates.
This project can be great to work on for beginners. You can use linear regression techniques to build the model or an advanced approach like random forest regressor or gradient boosting.
Sales forecasts can be used to plan out resources and project budgets w.r.t expected demand. For predicting sales, you can use ARIMA, Vector Autoregression, or deep learning.
You can analyze the team performance graphically via Plotly library in Python to interpret team players' performance efficiently.
With advancements in technology and data collection methods, you can now predict heart disease via machine learning algorithms. You can utilize logistic regression machine-learning algorithms to predict heart disease.
If you are looking for a machine learning engineer job in the marketing industry, this can be a great project to work on. You can utilize K-Means clustering or hierarchical clustering to divide customers to render business value.
Junior Machine Learning Engineer Resume Sample
- Tools & Languages: Pandas, scikit-learn, Neo4j, PostgreSQL, Hive, Spark, Python
- Statistics/Machine Learning: Statistical Analysis, Linear/Logistic Regression, Clustering, k-means clustering
- Honoured with Employee of the Year Award for surpassing the assigned targets by 35%
- Increased market share by 25% as a result of developing customer segmentation algorithm in R
- Performed an instrumental role in revamping algorithms for the company's application with 3M+ users
- Designing and developing analysis systems as part of extracting valuable information from large scale databases
- Researching and developing analysis, forecasting & optimizing methods to improve the quality of 5+ user-facing products
- Designed graph database via Neo4j for recommending real-time products to users via a smaller codebase
- Creating synthetic datasets as part of developing models and slashing data acquisition costs by 18%
- Developing OpenCV machine learning solutions for senior management as part of facilitating smooth decision making
- Building ML models and pipelines for use cases across facets spanning e-commerce, consumer data, markets, logistics, etc.
- Establishing seamless interactions between edge machine learning modules and cloud-based learning systems
- Scrutinizing existing Machine Learning (ML) models to identify key areas of modifications and deliver optimal solutions
- Developing and implementing Machine learning operations (MLOps) framework by liaising with a team of 10+ encompassing
- Data Engineers, Data Scientists and DevOps engineers
- Developed SQL stored procedures, functions, and style sheets for reducing data retrieval time by 40%
- Liaised with the product team of 10+ professionals to integrate ideas into products and develop Machine Learning solutions
- Employed decision trees to build predictive models as part of anticipating customers' behaviour
- Performed a key role in enhancing data integrity by revamping schemas with 150+ tables
- Key Project 1: Customer Segmentation
- Utilized k-means clustering algorithm to divide customers based on their purchase history, gender, age, interest, etc.
- Key Achievement: Increased sales by 25% by running user-specific campaigns
- Key Project 2: Housing Prices Prediction
- Gathered and analyzed data to forecast house prices in Boston based on crime rate, number of rooms, etc.
- Key Project 3: Heart Disease Prediction
- Predicted the 10-year risk of Heart Disease w.r.t. diabetes, smoking, high blood pressure, and high cholesterol levels
- Certificate Program in Machine Learning & Artificial Intelligence | eCornell | Jan '21
- Deep Learning Specialization | Coursera | Aug '19
- Data Science: Foundations using R Specialization | Coursera | May '19
- Master of Science in Machine Learning | Georgetown University | Washington, D.C. | Sep '16 - Nov '19
- Bachelor of Science in Computer Science | University of San Francisco | SF, CA | Jun '11 - Aug '15
Key Takeaways
- Create an ATS-friendly machine learning engineer resume with the right keywords to beat the bots and get shortlisted.
- Gain expertise in programming languages like Python and R & operate machine learning tools & software to reach a higher and better position.
- Pursue machine learning certification, attend online courses, work on machine learning projects to stay updated with the latest industry standards.
- A Ph.D. or MS degree in Statistics, Computer Science, Applied Math, Econometrics, or any related field is essential to bag a machine learning engineer job.
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