9 machine learning certifications

Posted on 29 June, 2023 by Naina Mule

What is machine learning certification?

Data science professionals can obtain machine learning certificates to demonstrate their knowledge of specific algorithmic concepts. These certificates can demonstrate to employers that you have received training in ML and have extensive industry experience. Many certifications offer regular refresher training, which is ideal for keeping up with the latest ML trends. These certifications may not be required to land a data science job, but they can enhance your competitiveness. These certifications will also help you launch your career, by teaching you about computer algorithms and their practical application. 

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Machine learning certifications: 9 different types

You can earn different ML certificates depending on your career goals or specialisation. Here are nine examples.

Machine Learning Professional Certificate

• This certificate is a focus on different ML technologies. It includes both theoretical and applied applications. it divides into six segments:
• This course will introduce learners to machine learning, high-quality data, and retrieval techniques. Topics include also recovering missing values, detecting outliers, and scaling techniques.
• This course will teach you how to develop regression models that can be used to predict the outcome of continuous events. Data scientists can use regression as a statistical tool to determine the relationship between independent variables and a dependent variable.
• This course teaches students how to use and describe logistic regression models. They also learn about predictive models for categorical outcomes and different sampling techniques. You can learn to compare models.
• This course will teach you how to choose the right algorithms for your organisation's data, and how to extract insights from datasets that do not have labelled variables. This course may require some experience in Python programming and data cleansing.
• Deep Learning and Reinforcement Learning : This course is ideal for data scientists that want to learn about building data distribution models. This course may require a basic understanding of statistics, calculus, linear algebra, and probability.
• Machine Learning Capstone (last course): This course covers practical applications of Python-based Machine Learning libraries such as scikit learn and Pandas. You can learn how to analyze course-related data and share your work.

AWS Certified Machine Learning

AWS provides information on how to create, design and deploy ML models. This course is designed for professionals with two years of experience in architecture and implementation of ML workloads on the AWS cloud. This course may also require that you have a good understanding of hyperparameter optimization and follow operational best practices. Topics include:

• Fundamentals of ML in AWS
• Data engineering with AWSML
• AWS ML: exploratory data analysis
• Modeling with AWS ML
• Implementing and operating AWS Ml
• The AWS-certified ML Specialty: A Guide to Understanding

• Google Cloud Professional Machine Learning Engineer

This certificate is ideal for data scientists and ML engineers that want to learn more about the Google Cloud Platform. This certificate focuses on improving and developing models, deploying to production environments, and automating the process. The accreditation can be earned by passing a 2-hour exam without having to complete the entire course. You can also enroll in the six-section training program, which includes:

This topic is about framing ML problems, which involves converting business challenges into ML use-cases that can be implemented. It also includes defining ML issues and determining success metrics. This includes how to identify common risks and resolve them when implementing ML.

The second section is about developing scalable, reliable ML solutions. You can also learn how to choose the best Google Cloud Hardware and cybersecurity solutions for various ML hardware.

Data preparation and processing: The part draws heavily from Machine Learning Course in Pune. It includes statistical basics, visualisations and data quality evaluation, as well as the setting of data constraints. This course can teach you the fundamentals of data pipelines and feature engineering, as well as how to encode structured types of data.

Developing ML Models: This section is focused on creating, testing and training ML models before deploying them in different environments.

Automation and orchestration of ML pipelines. An ML pipe is a workflow that can be executed independently to automate the input of data into ML models. This topic explains how to create and deploy pipelines, and covers the basics of tracking metadata.

Monitoring, optimising and sustaining ML solutions. The last section will help data scientists to learn how they can troubleshoot and fix ML errors.

• TensorFlow Developer Certificate

This certification is ideal for data scientist who wish to improve their ML skills. They can learn how to use TensorFlow in order to train and design models. It can be earned by passing a 5-hour exam that covers foundational ML principles and deep-learning, image recognition, and developing ML using TensorFlow. You may need to have a basic understanding of Python and some background knowledge in statistics, probability calculus, and linear algebra.

• Deep Learning AI TensorFlow Certificate Professional

This program teaches candidates ML through 16 Python programming exercises. You can learn how to use TensorFlow for neural network development and training, and to teach computers to respond and understand human speech. The certificate includes four courses.

• TensorFlow: AI, deep learning and machine learning.
• TensorFlow supports convolutional neural network in TensorFlow
• TensorFlow for natural language processing
• Sequences, time series, and prediction

• Azure AI Engineer

Microsoft's cloud solutions are used by many organisations to implement ML. Microsoft's Azure Engineer certification can verify your abilities to deploy AI with Azure Cognitive Services and Azure Applied AI Services. Candidates may need to have experience in Python or C#, as well as considerable knowledge of Azure, and be able to implement conversational AI, knowledge mining, computer vision, and natural language processing. Each of the five topics in the certificate exam contributes between 15-25% towards your final grade.

• Azure Cognitive Services: Planning and Management
• Conversational AI solutions
• Natural language processing solutions
• Computer vision solutions
• Knowledge mining solutions

• AI Engineering Professional Certificate

This certification will validate your knowledge of AI and ML. The course includes ML basics, deep learning, supervised/unsupervised learning, and the practical application of programming language such as Python or C#. The course also covers how to use TensorFlow and SciPy to perform image processing, text analytics and video processing. Candidates build different portfolios throughout the programme to gain experience in ML/deep learning. The certificate includes six courses.

• Machine learning with Python
• Introduction to deep learning with Keras
• Introduction to Computer Vision and Image Processing
• PyTorch: deep neural networks using PyTorch
• TensorFlow: a powerful tool for building deep learning models
• AI capstone project with Deep Learning

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