Training: Data Science with AI & ML

$89.00
$107.69 incl. vat

duration: 2 hours |

Language: English (US) |

access duration: 90 days |

Details

In this course, you'll acquire a comprehensive understanding of both foundational and advanced data science methods. Dive into the world of machine learning, unravelling its prediction algorithms and applications across industries. From understanding categories and challenges to exploring tasks like clustering, classification, and regression, you'll learn it all. Progress to advanced data science methods, delving into statistical techniques, text mining, graph analysis, and more.

Result

After completing this course, you'll have knowledge of both foundational and advanced data science method and you'll be capable of extracting valuable insights from data.

Prerequisites

No formal prerequisites. However, some prior knowledge about the topic is highly recommended.

Target audience

Manager, Data analist

Content

Data Science with AI & ML

2 hours

Fundamentals of AI & ML: Foundational Data Science Methods

Data science methods are used across several industries to deliver value to businesses. Machine learning (ML) is a data science method that uses prediction algorithms that find patterns in massive amounts of data, allowing machines to predict future results and make decisions with minimal human intervention. Through this course, learn foundational methods for using machine learning. Examine what machine learning is, how it is categorized, and common machine learning challenges. Next, learn about common types of machine learning tasks, such as clustering, classification, and regression. Finally, explore the types of regression, including simple and multiple linear regression. Upon completion, you'll be able to define machine learning and methods for using it.

Fundamentals of AI & ML: Advanced Data Science Methods

In data science, many statistical and analytical techniques can be used to pull meaningful insights from data. Additionally, some advanced data science methods rely on other foundation data science methods, such as the case of text mining. Through this course, learn about advanced data science methods and their use cases. Explore advanced machine learning (ML) methods such as text mining and graph analysis and their uses. Next, learn about the anomaly and novelty detection processes. Finally, examine association rule mining and neural networks and their use cases across industries. After course completion, you'll be able to outline advanced methods for data science.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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What is included?

Certificate of participation Yes
Monitor Progress Yes
Award Winning E-learning Yes
Mobile ready Yes
Sharing knowledge Unlimited access to our IT professionals community
Study advice Our consultants are here for you to advice about your study career and options
Study materials Certified teachers with in depth knowledge about the subject.
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