Machine Learning and AI are becoming the core capabilities for solving complex real-world problems and delivering value in all domains. More and more industries are adopting Machine Learning and AI technologies to increase productivity, personalize content, and reduce costs. In simple words, the goal of Machine Learning is to make businesses grow better.
Machine Learning and AI has provided some of the best jobs of the 21st century, and has been welcoming professionals from a wide range of backgrounds. Therefore, Machine Learning Engineers must have the right skill-set and the latest technological know-how to manage any complex projects.
The current rising demand for Machine Learning Engineers is strong while supply is low. It is considered the top job across the globe in terms of salary, growth of postings, and general demand.
It is a solid choice for a high-paying career that will be in demand for decades.
The Machine Learning Engineer handles the ML development process and collaborates closely with other stakeholders for application development, feature engineering, infrastructure management, data engineering, and data governance.
To work effectively as a Machine Learning Engineer, you must be a technically sound programmer with a solid foundation in mathematics, statistics, cloud computing and software engineering.
There are several certifications that show your competency in Machine Learning. However, the certification program that is right for you will depend on your experience and your career goals.
You do not need a Machine Learning Certificate to get a job, but there are valuable certification programs designed to validate hands-on experience and expertise to design ML solutions, implement algorithms, perform statistical analysis, secure, and maintain ML systems.
A few of the most popular Machine Learning Certification programs are from big tech companies like AWS, Microsoft Azure, Google Cloud, Udacity and DataCamp. They have standards and their own accredited certification authority.
In this direct-to-point guide, we assume that you're already familiar with Machine Learning and you want to have some formal proof of your abilities. We have evaluated a few learning paths that emphasize specialization and job readiness with technical guidance/ mentorship.
These learning tracks will strengthen your expertise in hiring contexts, increase your proficiency in machine learning, test your abilities, equip you for the interviews and assessment exams.
Let's dig deeper.
The goal of this Nanodegree program by Udacity and AWS is to equip learners with machine learning skills required to build, train and deploy ML models in production using Amazon SageMaker.
This program comprises four courses and one capstone project that teaches learners the latest best ML practices and capabilities enabled by Amazon SageMaker.
Amazon SageMaker is a fully managed service to build, train, and deploy machine learning (ML) models rapidly.
The primary responsibilities of the AWS Machine Learning Engineers are to use machine learning (ML) and artificial intelligence (AI) technologies in AWS Cloud Platform to help scale business.
This Nanodegree is for experienced individuals who are looking to advance their career with cutting-edge Machine Learning skills. It also comprises content and curriculum to support real-world projects that you can showcase in your portfolio.
You will receive technical mentor support throughout the program, personalized feedback, practical suggestions, and career support.
This program provides the most exhaustive and practical knowledge for job roles, such as ML practitioners, Data Scientists and AI Developers.
- Creating Machine learning models using AWS tools
- Deploy trained machine learning models
- Computer Vision
- Neural Networks and Deep Learning
- Machine Learning with AWS SageMaker
This Nanodegree program is suitable for software developers/ data scientists with a good programming experience.
- Advanced Python programming skills
- Basic understanding of Machine Learning algorithms
- Basic understanding of machine learning workflow
This Nanodegree program is developed by Udacity and Microsoft to help learners strengthen their skills by building and deploying Machine Learning solutions using open source tools and popular frameworks.
This program will provide exposure to Azure Machine Learning’s MLOps capabilities that enable students to learn to understand their ML models and manage end-to-end ML lifecycle at scale.
You will gain practical experience by using the built-in Azure labs to run complex machine learning tasks.
Microsoft Azure Machine Learning Engineers build, manage, and deploy Machine Learning solutions that leverage Azure Cognitive Services and Azure Applied AI services. They work with solution architects, data engineers, data scientists, IoT specialists, and software developers to construct complete end-to-end ML solutions.
This Nanodegree program will help you demonstrate your proficiency in using Microsoft Azure to solve ML problems. You will become highly equipped to author new models, store your compute targets, models, deployments and metrics, and run histories in Azure cloud platform.
The program consists of two courses and one capstone project developed by the Microsoft Azure team.
- Learn to use Azure ML SDK to design, create, and manage machine learning pipelines in Azure
- Configure machine learning pipelines in Azure
- Learn Automated Machine Learning
- Learn MLOps and its core features to train models with AutoML
- Deploying and shipping machine learning models into production.
This Nanodegree program is suitable for learners with prior experience with Python, Machine Learning, and Statistics.
- Intermediate-level Python skills
- Beginner level statistics and mathematics
- Basic understanding of fundamental Machine Learning concepts
- Basic familiarity with Azure and Docker/Container experience
Professional ML Engineer Certification - Google Cloud
A Google Certified - Professional Machine Learning Engineer designs, builds, optimizes, operates, and maintains ML systems to solve complex business challenges using Google Cloud Machine Learning Engine.
This learning program is developed by Google Cloud to help learners become equipped to implement the latest machine learning and artificial intelligence technology using BigQuery, TensorFlow, Cloud Vision, Natural Language API, etc.
You will be guided through a series of on-demand courses, labs, exercises and skill badges that provide you with real-world, hands-on experience using Google Cloud ML Engine essential to the Machine Learning Engineer job roles.
You will become highly equipped with employable skills to oversee the ML development process, and proficient in model architecture, data pipeline interaction, and metrics interpretation.
Once you complete the learning program, you can enrol for a Professional Machine Learning Engineer Certification Exam by Google Cloud to take the next steps in your professional journey.
The learning program helps you to learn the skills needed to be successful in an ML Engineer job role using Google Cloud ML Engine.
- Learn to Frame Machine Learning problems
- Learn ML Solution Architecture
- Data preparation and processing
- Develop Machine Learning models
- Automating and orchestrating ML pipelines
- Monitoring, optimizing, and maintaining ML solutions
- Learn Feature Engineering and building input data pipelines
- Popular Machine Learning tools, frameworks and Google Cloud ML Engine Services
This professional learning program is suitable for experienced programmers, software developers, and data engineers.
- Experience in Python programming
- Statistics and Mathematics
- Basic understanding of building data pipelines
- Basic familiarity with API development
- Basic understanding of Machine Learning
This Nanodegree program equips practitioners to employ the best DevOps practices for building, training, and deployment of ML models.
In this program, you’ll gain DevOps skills and get a solid grasp of software engineering principles to automate the various facets and stages of machine learning model building and monitoring.
This learning track is excellent for learning critical DevOps skills to streamline the integration of machine-learning models and production-level deployment.
ML DevOps skills bring distinct technical benefits and this program will equip you to adopt the best practices to push the models from modeling environments to production to be self-functioning.
These Machine Learning DevOps skills will be instrumental in job roles such as Machine Learning Engineer, Data Scientist, Data Engineers and more.
This program is excellent for learning to design, research, and developing scalable machine learning pipelines that automate the machine learning workflow.
- Implement production-ready Python code and processes for deploying Machine Learning models using AWS SageMaker, Azure ML, etc.
- Building a reproducible model workflow
- Learn continuous delivery and automation pipelines in machine learning
- Automate data workflows that perform continuous training (CT) and model validation within a CI/CD pipeline
- Automated model scoring and monitoring
- Create multi-step pipelines that will retrain and deploy models automatically after data updates
- Monitor model performance to prevent model-degradation
This program is suitable for learners with experience in Python and Machine Learning. You must have comfortability with ML concepts and using Python in an AI context.
- Good understanding of the overall workflow of building machine learning models
- Ability to write scripts using NumPy, pandas, Scikit-learn, TensorFlow/PyTorch in Jupyter notebooks.
- ETL Skills
- Using the Terminal, version control in Git, and using GitHub
This comprehensive career-track is designed and developed by DataCamp to help learners master the essential skills to land a machine learning job role.
You will build world-class Machine Learning capabilities and orchestrate your competence in the deployment and management of ML models in production and provide inputs about the applicability of natural language processing, feature engineering, image processing, and popular libraries such as TensorFlow, PyTorch and Keras.
This program comprises 23 courses to help bootstrap your Python programming skills with a toolbox to perform supervised, unsupervised, and deep learning.
Upon successful completion, you will be highly equipped for the job roles such as Machine Learning Engineer, Machine Learning Scientist, AI Scientist, Natural Language Processing (NLP) Scientist, and Data Scientist.
This program is excellent for building the right skill-set and learn the latest technological know-how to manage any complex ML projects.
- Machine Learning Fundamentals in Python
- Supervised and Unsupervised Learning
- Advanced Machine Learning
- Feature Engineering for Machine Learning, and NLP
- Basics of Deep Learning with Python
- Deep Learning with TensorFlow, Keras, and PyTorch
- Winning a Kaggle Competition in Python
This is learning track is suitable for Python programmers with a background in Statistics and high-school level Maths.
- Statistical programming with Python
- Mathematical Statistics and Algorithms
- Basic Understanding Machine Learning
This program aims to equip R Programmers to become ML/AI Scientist by learning the Scientific libraries and frameworks in Machine Learning, Data Analysis, Cluster Analysis, Deep Learning Frameworks, Advanced Machine Learning techniques, etc.
You will learn the fundamentals of Machine Learning in R with the all-new course curriculum that includes Supervised and Unsupervised Learning, Support Vector Machines, Regression Modeling, Tree-Based Models, and more
Machine Learning Scientist is one of the highly respected designation for any technology professional seeking to jumpstart AI career.
This program will significantly increase your chances of landing on your favorite job. Your accomplishments through this career track will set you apart from your peer right from the start of your AI career.
You'll augment your Statistical programming skills with the tools, libraries and frameworks.
- Machine Learning Fundamentals in R
- Supervised and Unsupervised Machine Learning
- Bayesian Data Analysis
- Bayesian Regression Modeling
- Natural Language Processing
- Support Vector Machine Learning
- Apache Spark and R
This program is suitable for learners with a background in Statistics and Mathematics.
- R Programming
- Bayesian Statistics
- Bayesian Linear Regression
- Basic understanding of Machine Learning and Algorithms
This comprehensive certification program will equip you with the working knowledge of Machine Learning and Artificial Intelligence through a combination of both theory and practice.
You will, through a series of interactive lectures and real-world projects, learn different paradigms in machine learning, create intelligent autonomous agents, train and optimize deep neural networks.
This program will equip you with a sound understanding of the Machine Learning techniques to advance your career in Data Science and AI. You will learn to work more effectively on machine learning projects, identify ML opportunities, frame ML problems, architect solutions, and develop ML models.
This certification program provides exposure to the tools to enhance your data mining skills and machine learning competencies to rise to the next level in your career.
Earning the Artificial Intelligence and Machine Learning MasterTrack Certificate will also be a pathway to the online Master of Computer Science degree at Arizona State University. You may be able to transfer credits to other Master's degree programs offered via Coursera.
This program will help you gain in-demand skills in machine learning, deep learning, supervised and unsupervised learning, knowledge representation, and reasoning.
- Statistical Machine Learning
- Deep Learning Architectures
- Machine Learning Methods
- Probabilistic Inference
- Deep Learning in Visual Computing
- Image Classification
This Certification program is recommended for learners with background in Statistics and Mathematics.
- Statistical Programming
- Basic understanding of Machine Learning Algorithms
- Statistics, Probability, Calculus, Linear Algebra and optimization
These machine learning certifications will equip you to master specific tools, learn the technical concepts and guide you through building realistic, complete machine learning applications.
We have also compiled the high-quality Machine Learning resources suitable according to the specialization and experience-level.
- Machine Learning for Finance
- TensorFlow Courses
- Math for Machine Learning
- How to Build a Data Science Portfolio (Relevant to ML Engineers)
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