Top 9 Machine learning certifications

What is machine-learning certification?
Machine learning certificates are available for data science professionals to show their understanding of algorithmic concepts. These certificates will show employers that you are well-versed in ML, and have a lot of industry experience. Most certifications provide regular refresher courses, which are ideal for staying up to date with the latest ML developments. Although these certifications are not required to get a job in data science, they can boost your competitiveness. These certifications can also help you start your career by teaching you computer algorithms and how they are applied in the real world.
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There are nine different types of certifications for machine learning
Depending on your career goal or specialisation, you can earn different ML certifications. Here are nine examples.
Machine Learning Professional Certificate
This certificate focuses on ML technologies. This certificate includes both theoretical as well as applied applications. it divides into six segments:
• This course introduces learners to machine-learning, high quality data, and retrieval methods. Topics covered include recovering missing values, detecting anomalies, and scaling techniques.
• This course will show you how to create regression models which can be used to predict continuous events. Regression can be used by data scientists to establish the relationship between independent variables, and a dependent one.
• Students will learn how to describe and use logistic regression models. Students also learn about different sampling techniques and predictive models for categorical results. Comparing models is something you can learn.
• This course will show you how to select the best algorithms for the data in your organization and how to extract insight from datasets without labelled variables. This course requires some knowledge of Python programming and data cleaning.
• Deep Learning and Reinforcement Learning: This course is designed for data scientists who want to know more about creating data distribution models. This course requires a basic knowledge of probability, statistics, calculus and linear algebra.
• Machine Learning Capstone (last class): This course covers practical application of Python-based Machine Learning library such as scikit Learn and Pandas. You can share and analyze data related to the course.

AWS Certified Machine Learning
AWS offers information on how to design, create and deploy ML models. This course is for professionals who have two years experience in architecture and implementation ML workloads using the AWS cloud. This course will also require you to have a solid understanding of hyperparameters and operational best practices. Topics covered include:
• AWS ML Fundamentals
• AWSML Data Engineering
• AWS ML: exploratory Data Analysis
• Modeling with AWSML
• AWS Ml: Implementation and operation
• A Guide to Understanding the AWS-certified ML Specialty

Google Cloud Professional Machine Learning Engineer
This certificate is perfect for data scientists or ML engineers who want to know more about Cloud Platform. This certificate is focused on developing and improving models, deploying them to production environments and automating the entire process. You can earn the accreditation by passing a two-hour exam, without having to finish the entire course. You can enroll in a six-section program that includes:
This topic is all about framing ML issues, which includes converting business problems into ML use cases that can be implemented. This includes defining ML problems and determining success measures. It also includes how to identify and eliminate common risks when implementing ML.
You can also learn how to choose the best search Google Cloud Hardware and cybersecurity solutions for various ML hardware. You can also find out how to select the best Google Cloud Hardware for your ML hardware and learn about cybersecurity solutions.
This part is heavily based on Machine Learning Classes in Pune . This includes data visualizations, data quality evaluation and setting data constraints. This course will teach you how to encode structured data types and the basics of feature engineering.
This section focuses on developing, testing and training models of ML before they are deployed in different environments.
Automation and orchestration ML pipelines. A ML pipeline is a workflow which can be run independently in order to automate data input into ML models. This topic covers how to deploy pipelines and the basics of tracking meta data.
Monitoring, optimising, and maintaining ML solutions. The final section will teach data scientists how to troubleshoot ML errors and fix them.

TensorFlow Developer Certificate
This certification is perfect for data scientists who want to enhance their ML skills. TensorFlow can be used to design and train models. The certification can be obtained by passing a five-hour exam covering foundational ML and deep learning, image recognition and developing ML with TensorFlow. You will need to know Python, as well as some statistics, probability calculus and linear algebra.
Deep Learning AI TensorFlow Professional Certificate
This program teaches candidates ML by 16 Python programming exercises. TensorFlow can be used to develop neural networks, train computers and teach them to understand and respond to human speech. The certificate includes four classes.
• TensorFlow: AI and deep learning.
• TensorFlow includes support for convolutional neural networks in TensorFlow
• TensorFlow Natural Language Processing
• Sequences, time-series, and prediction

Azure AI Engineer
Microsoft's cloud-based solutions are used by many organizations to implement ML. Microsoft's Azure Certified Engineer can confirm your ability to deploy AI using Azure Cognitive Services and Azure Applied AI Services. Candidates will need experience with Python or C# as well as a thorough understanding of Azure. They should also be familiar with conversational AI, computer vision, knowledge mining and natural language processing. Each of the five topics included in the certificate exam will contribute between 15-25% to your final grade.
• Azure Cognitive Services: Management and Planning
• Conversational AI solutions
• Natural Language Processing Solutions
• Computer vision solutions
• Knowledge mining solutions

AI Engineering Professional Certificate
This certification will validate the knowledge you have 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 covers TensorFlow, SciPy and image processing using text analytics, video processing and TensorFlow. Candidates will build up different portfolios to gain experience with ML/deep-learning. The certificate includes six course.
• Python Machine Learning
• Deep learning with Keras
• Introduction to Computer Vision and Image Processing
• PyTorch: deep neural networks using PyTorch
• TensorFlow is a powerful tool to build deep learning models
• Deep Learning AI Capstone Project
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