It’s critical to keep adding new tools to your arsenal, especially if it’s one of Gartner’s Leaders in the Magic Quadrant for Data Science and Machine Learning 2020 report.
The two most important features of this platform are usability across profiles (Data Engineers, Scientists, and Analysts) and flexibility (Visual and Coding environments). If you’re a great coder, you can do it yourself with Python, R, Spark, and Scala, to name a few and have complete control over your models. Otherwise, you can use the Click and Run visual recipes/models. The Dataiku platform makes post-analysis of EDA and ML models straightforward. Easy integration in Data tools like Hadoop, Spark, and others is a bonus. It fits into CI/CD pipelines and deploys to Production without a hitch.
Dataiku is a well-known name in the world of AI and analytics insights and is one of the rapidly growing data science and machine learning platforms. You have a fantastic opportunity to advance your career in the given sector with Dataiku professional certificates. With these excellent credentials, you can advance your career whether you have some experience or little knowledge of the Dataiku cloud framework.
To prepare for this certification you get to learn advanced code integration, advanced analytics, and data pipeline operationalization, allowing you to qualify for higher-paying jobs and transform into a valuable asset in the IT market. The certifications exam has the following sections:
- Glossary: Intro to Machine Learning
- Quiz: Introductory Concepts
- Concept: Intro to Machine Learning
- Concept: Model Evaluation
- Concept: What is Predictive Modelling?
- Concept: Model Validation
- Quiz: Predictive Modelling
- Concept: Regression Algorithms
- Quiz: Prediction: Regression
- Concept: Classification Algorithms
- Quiz: Prediction: Classification
- Concept: Clustering Algorithms
- Quiz: Clustering
- Tell Us What You Think: Intro to Machine Learning
- Course Checkpoint: Intro to Machine Learning
- Course Complete
Why should you take the Dataiku ML exam?
- With the DSS Visual ML tool, you can create, evaluate, and tune machine learning models.
- Transfer your model from the lab to the flow and use it.
- Responsible AI tools can help you understand and explain your model.
- Learn the fundamentals of model lifecycle management.
- Use the Interactive Stats tool to examine your data.
- The most vital thing to keep in focus when preparing a dataset for machine learning
- The value of Dataiku AutoML for quick modelling and how it can be the foundation for your first machine learning model
- In Dataiku, there are concepts for evaluating and tuning a model.
Before starting this lesson, make sure you’ve completed Basics 101, 102, and 103, which are part of Dataiku Academy’s Core Designer Learning Path.
This course is for anyone who wants to learn how to use Dataiku DSS for visual machine learning.