Technologies like machine learning, artificial intelligence, automation, and others that seem to be a part of science fiction are already dominating our lives. But if you think about it, it can also be a place full of opportunities. Considering the rage about it, a career as a machine learning engineer can be very fruitful. The article will guide to to become a machine learning engineer. Learn about the job roles of machine learning engineers, skills required, salaries, and resume hacks, among others.
- Who is a Machine Learning Engineer and What Does He Do?
- Machine Learning Engineer Salaries
- Important Machine Learning Engineer Skills
- Must Know Algorithms to Become a Machine Learning Engineer
- Must Know Libraries to become a Machine Learning Engineer
- Popular Projects to Practice to become a Machine Learning Engineer
- Resume Hacks for Machine Learning Engineer Interview
- Learn from Online Machine Learning Communities
- Join the Bootcamps and Seminars Hosted by Industry Experts
You would need to learn machine learning concepts, develop the right skills, work on machine learning projects, etc. It can be a good start to earn a relevant certification or degree to help you advance your career. To increase your chances of landing a machine learning position, we suggest you go through the blog and make notes on how to become a machine learning engineer.
Who is a Machine Learning Engineer, and What Does He Do?
Machine learning engineers are programmers responsible for developing systems that can learn and apply knowledge without being explicitly programmed.
To be precise, they create specific tasks that enable machines to take action without being specifically programmed to perform those tasks.
Common job responsibilities of Machine learning engineers include –
- Understand business objectives and develop models that help to achieve them, along with metrics to track their progress
- Develop and analyze the ML algorithms to solve a given problem and rank them by their success probability
- Research and develop continual learning and personalization ML algorithms
- Deploying and maintaining deep learning systems and services
- Innovate novel methods to improve AI on-device performance, model size and accuracy
- Develop state-of-the-art solutions for real-world problems through AI & ML with commercialization goals
- Translate complex functional and technical requirements into detailed design
- Manage the technical risks across different projects and empower the team to achieve great results.
- Bring in the technical expertise around the implementation of best coding standards and practices across the team
- Design and implement software for embedded devices and systems from requirements to production and commercial deployment
- Integrate and validate new product designs
- Support software QA and optimize I/O performance
- Provide post-production support
- Interface with hardware design and development
- Assess third-party and open-source software
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Machine Learning Engineer Salaries
As per Ambitionbox, the salaries of a Machine Learning Engineer in India lie in the range of Rs- 3.2 – 22 Lakh. The average annual salary of Machine Learning Engineers is Rs. 7.3 Lakh.
Important Machine Learning Engineer Skills
Let’s take a look at the must-have skills that you would need to become a machine learning engineer.
- Machine Learning
- Programming Languages(R/Java/Python/C++)
- Data Visualization
- Business Intelligence Tools
- Math & Statistics
- Deep Learning
- Natural Language Processing
- Data Acquisition
- Model Deployment
- Cloud Computing
- Big Data (Hadoop/ Pig/ Hive/ Cassandra)
It may sound obvious to you, but it’s a crucial step. You would need to understand what machine learning is, what is the basic math and technology behind it. Start with the below basic machine learning concepts –
- Supervised learning (Classification, Regression, Forecasting)
- Semi-supervised learning
- Unsupervised learning (Clustering, Recommendation System Association Rule Mining, Dimensionality Reduction)
- Reinforcement learning (Dynamic programming, Monte Carlo methods, Heuristic methods)
- Other concepts: Data cleaning techniques, Cross-validation technique, feature selection, feature engineering, hyperparameter tuning, model optimisation techniques, model deployment, Exploratory data analysis
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It is crucial for machine learning engineers to communicate their findings in visualized formats like charts, infographics, diagrams or maps.
Tools – Matplotlib, Seaborn, plotnine, plotly, bokeh, ggplot to tell your story with the data.
Business Intelligence Tools
Business intelligence tools help to provide users with information from the data and make smarter business decisions. This makes BI tools an essential part of machine learning.
Tools – Learn to work on tools like Tableau, PowerBI, SAS Business Intelligence, Oracle Business Intelligence, Zoho Analytics, MicroStrategy etc.
Math & Statistics
Machine learning is powered by Math and Statistics, making it a must-have skill. You should have an impeccable command over Linear Algebra, Calculus, Probability, Discrete Math, Statistics, Algebra, Information Theory, differential calculus, etc. It can be a good idea to take up relevant courses in case you think you need to brush up on some concepts.
Deep learning is a subset of machine learning. However, some job roles require machine learning engineers to focus on working on both ML algorithms as well as specific deep learning algorithms. learn the concepts of deep learning. It could be a useful skill to list on your resume and apply in the real world.
Algorithms – Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short Term Memory Networks (LSTMs), Generative Adversarial Networks (GANs), GRU, Autoencoders, Transformers, YOLO V4, Mask RCNN, Detectron, etc.
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Natural Language Processing
Natural language processing enables machines to understand the human language. It allows machines to analyze text and voice data easily. Many machine learning applications like automated chatbots, Google translate, spam mail filtering, speech recognition, autocorrect, etc. use NLP to deliver accurate responses, making it an important skill for machine learning engineers.
Concepts – Tokenization, Lemmatization, Stemming, Parts-of-Speech (POS) tagging, N-gram, Named-Entity-Recognition (NER), Vectorization, Word Embedding, frequency Inverse document frequency (TF-IDF), Machine Translation, Attention Models, Hugging face transformers, Word Embedding Models, etc.
Data acquisition is the process of sampling signals that measure real-world physical conditions, converting the resulting samples into values.
Libraries – Knowledge of webscraping libraries like BeautifulSoup, Scrapy, Selenium, Tweepy, and URLlib can be helpful to acquire/ scrape specific data from a target web page.
After creating a machine learning model, we come to model deployment which is simply exposing the ML model for real use. It is the final step of the machine learning life cycle.
Platforms – Platforms, and tools like Docker, Gradio, Kubernetes, SageMaker, MLFlow, Streamlit, Heroku can help you seamlessly deploy your machine learning models.
Learn about cloud platforms like AWS Sagemaker, Google Cloud AI, IBM Watson, Azure Machine Learning to experiment with machine learning capabilities and improve machine learning projects.
Big Data (Hadoop/ Pig/ Hive/ Cassandra)
Hadoop, HPCC, Apache Storm, Qubole, MongoDB, Cassandra, Cloudera, OpenRefine, Pig, Hive, etc. are some of the essential big data tools to master.
Database management systems help to sort and manage massive amounts of data, and derive useful information from it.
Examples – MongoDB, Redis, Couchbase, DynamoDB, Elasticsearch, Machine Learning Database (MLDB), etc. are some of the good databases for machine learning systems.
Other important skills to learn for becoming a machine learning engineer are –
- Data Manipulation and cleaning
- Exploratory Data Analysis
- Data Analysis & Modeling
- Sequence Learning
Must-Know Algorithms to Become a Machine Learning Engineer
It is crucial to choose the right machine learning algorithm for building your machine learning systems. This selection depends on data size, quality of data, parameters, data points, and the problems you want to solve.
You would need to experiment with different algorithms to find the most suitable one. Below are some of the important machine learning algorithms that you must learn.
Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Tree Classification (CART)
- Random Forest Classification
- Support Vector Machine (SVM)
- Naive Bayes Classifier
Unsupervised Learning Algorithms
- K-Means Clustering
- Hierarchical Clustering
- Fuzzy C-Means Clustering
- Neural Network
- Apriori Algorithm
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
Reinforcement Learning Algorithms
- Deep Q Learning Algorithm
- Deep Adversarial Networks
- Temporal Difference (TD)
- Multilayer Perceptrons (MCP
- Recurrent Neural Networks (RNN)
- Convolutional Neural Networks (CNN)
- Long Short Term Memory Networks (LSTMs)
- Generative Adversarial Networks (GANs)
- Restricted Boltzmann Machine (RBMs)
Must Know Libraries to become a Machine Learning Engineer
A machine learning library is a set of functions written in a specific programming language. These libraries help machine learning engineers perform complex tasks without rewriting long codes. If you are to start a career in machine learning then it’s a good idea to get yourself acquainted with these machine learning libraries.
Top Machine Learning Libraries
These libraries are written in Python and each performs different functions. To know more about these machine learning libraries read our blog.
Popular Projects to Practice to become a Machine Learning Engineer
Some of the beginner level machine learning projects that can help you gain new machine learning skills are –
- Stock Price Prediction
- Sentiment Analysis
- Sales Forecasting
- Movie Recommendation
- Iris Flower Classification
- Object Detection with Deep Learning
- Enron Email Dataset
To start working on your machine learning projects, we recommend you build your profile and then create projects on Kaggle and GitHub.
- Build Your Kaggle repository
Kaggle is a perfect platform for machine learning and data science enthusiasts to connect and learn. Kaggle covers free GPUs and a huge repository of community-published data and codes. You can participate in discussions around data sets and competitions to solve real-world machine learning problems to earn scores. There is a leaderboard for each competition. Many of these contests also come with cash prizes and status points. Participants can refine their models till the contest is on and improve their scores to climb the ladder.
- Build your GitHub Repository
Github is an open-source distributed revision control system to store and manage codes. It offers capabilities like version control, collaboration, and a community of experts as well as peers. These professionals can suggest fixing broken code or making corrections to content. Your work won’t get overridden here.
Your project should include a wiki and issue tracker so that you can include more in-depth documentation and get feedback about your project. If you wish to contribute, you can fork a project, make your changes and then send them a pull request using the GitHub web interface. GitHub offers integration with platforms like Amazon and Google Cloud. With these, you can track your feedback, and highlight syntax in over 200 different programming languages.
Resume Hacks for Machine Learning Engineer Interview
CIO suggests that over 98% of Fortune 500 companies use an AI-enabled tool called Applicant Tracking System (ATS) in the recruitment process. Create a resume that impresses the AI bots and your resume is picked.
When creating a resume for a machine learning position, focus on things relevant to the field, like your professional experience and educational credentials. Here are some hacks to help you create your machine learning engineer resume.
- Use keywords like data science, machine learning projects, Python, neural networks, SQL, etc.
- Add a link to your Kaggle portfolio and/or Github profile page
- Highlight any machine learning or software development internships
- Keep your resume clutter-free, don’t fill in with text
- Always update the resume as per the job description. List the skills they explicitly ask for (only if you have)
- List your skills, achievements, etc. using bullet points
- Use active voice and shorter sentences
- Check your resume for any spelling/grammatical error
- Get it checked by another pair of eyes
Format – Use reverse-chronological resume format, meaning, your best achievements should appear on top
Fonts – Use professional fonts like Arial, Cambria, Calibri, Didot, Times New Roman, Helvetica, etc. for a crisp look
Font size: 11–12 points for text, 13–14 points for resume headings
Header – Should include correct contact information, list your portfolio
Projects – Add relevant ML or data science projects
Work Experience – List your job title and your relevant profile description
Summary – Key points of your experience and skills
Here is a sample of two machine learning engineer resumes for your reference. You can explore more such samples online to draft your perfect machine learning engineer resume.
Learn from Online Machine Learning Communities
It’s always good to learn from the experts. Machine learning communities will give you the opportunity to connect with like-minded people. You will also get to learn about the newest technologies and tools. Here are some popular machine learning communities –
Stack Overflow – Stack Overflow is an open community for developers and coders. It can be a great place to find and contribute answers to technical challenges and discover new things in this space.
Machine Learning Stories – Hacker Noon – As a machine learning beginner, you can explore stories and opinions shared by machine learning professionals
Reddit – It is a question-and-answer site where you can explore the sub-communities called subreddits and discuss. You can also find links to helpful machine learning blogs and even link your own projects and get feedback from experts.
AIM Community – On this platform, you can discuss and gain knowledge on new trends, technologies, and tools.
Quora – Quora is a very popular question-and-answer platform. Here you can read and participate in discussions on machine learning and related topics.
MetaOptimize Q+A – A question and answer site for beginner queries on algorithms and methods.
Cross Validated – It is a good platform for machine learning enthusiasts and beginners. They can discuss and solve their queries on algorithms and statistical methods.
Join the Bootcamps and Seminars Hosted by Industry Experts
Machine learning bootcamps are in the rage these days. Bootcamps are accelerated and short-term intensive programs that offer a practical learning environment. You can get enrolled in any of these bootcamps and learn from real work examples.
These bootcamps are mostly available for a fee. Do your research before enrolling. Some popular machine learning bootcamps are –
- Machine Learning Bootcamp: Deploy Algorithms and Build a Portfolio in 6 months by Springboard
- Complete 2022 Data Science & Machine Learning Bootcamp by Udemy
- Data Science with Python: Machine Learning by NYC Data Science Academy
- Data Science and Machine Learning Bootcamp by Metis
- Python for Data Science and Machine Learning Bootcamp by Udemy
- Data Science and Machine Learning Bootcamp with R by Udemy
You can check out these online seminars, participated by leading AI and ML professionals globally.
- The Rising 2022 by Analytics India Magazine (Hybrid)
- AIAI 2022 Conference (Hybrid)
- World AI Cannes Festival (Hybrid)
- Data+AI Summit 2022 (Online)
Forbes estimates that AI and machine learning skills will grow at a CAGR of 71% by 2025. It further suggests that over 200,000 positions today require a background in machine learning.
Hope this article helped you to plan your career in machine learning.
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