Common Applications of AI: Data Analysis, Distributed AI, Natural Language Processing Training

Common Applications of AI: Data Analysis, Distributed AI, Natural Language Processing

This training is crafted for developers and data scientists desiring to leverage AI in their applications.

With an emphasis on Data Analysis, Distributed AI, and NLP, participants are equipped with hands-on expertise to transform their projects.

What will you learn?

Dive deep into the world of Artificial Intelligence, tailored specifically for developers and data scientists. By the end of this course, you will possess the skills and knowledge to efficiently implement AI techniques within your applications, maximizing potential and driving results.

In this intensive training:

Grasp the complexities of Distributed Big Data.
Understand Data Mining methods, from traditional algorithms to distributed prediction.
Explore Natural Language Processing, from text clustering to semantic analysis.
Implement proven recommendation and advertising techniques.
Discover modern tools like Apache Spark MLlib and intricate architectures like RNN's long-term memory (TSTM).

Requirements:

Basic understanding of programming concepts.
Familiarity with data science principles.

Course Outline*:

*We know each team has their own needs and specifications. That is why we can modify the training outline per need.

Distributed Big Data:

Introduction to distribution in AI and its significance.

Data Mining Methods:

Training single systems and distributed prediction.
Traditional machine learning algorithms vs. Mapreduce distributed prediction.

Introduction to Apache Spark MLlib:

Basics and benefits for AI applications.‍

‍Recommendations and Advertising:

Fundamentals of text clustering, categorization, and labeling.
User profile restoration and labeling systems.
Insights into recommended algorithms.
Methods to ensure accuracy of 'lift' in categories.

Natural Language Processing (NLP):

Deep dive into text clustering, classification, and keyword extraction.
Overview of semantic analysis, semantic parsers, and word2vec.
‍

Advanced NLP Techniques:

Exploring Chinese word segmentation.
Delving into theme models and text clustering techniques.
Introduction to RNN long-term memory (TSTM) architecture.

Finalizing AI Implementation:

Logical regression and RankingSVM.
Feature recognition with deep learning and automated graphic feature recognition.

Hands-on learning with expert instructors at your location for organizations.

Level:

Intermediate

Duration:

Hours (days:

)

Training customized to your needs

Immersive hands-on experience in a dedicated setting

*Price can range depending on number of participants, change of outline, location etc.

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Master new skills guided by experienced instructors from anywhere.

Level:

Intermediate

Duration:

Hours (days:

)

Training customized to your needs

Reduced training costs

*Price can range depending on number of participants, change of outline, location etc.

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You can participate in a Public Course with people from other organisations.

/per trainee

Thanks for the numbers, they could be going to your emails. But they're going to mine... Thanks ;D

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Level:

Intermediate

Duration:

Hours (days:

)

Fits ideally for individuals and small groups

Networking opportunities with fellow participants.

*Price can range depending on number of participants, change of outline, location etc.

Get Quote

Common Applications of AI: Data Analysis, Distributed AI, Natural Language Processing

What will you learn?

Requirements:

Course Outline*:

Distributed Big Data:

Data Mining Methods:

Introduction to Apache Spark MLlib:

‍Recommendations and Advertising:

Natural Language Processing (NLP):

Advanced NLP Techniques:

Finalizing AI Implementation:

Get Quote for:

Common Applications of AI: Data Analysis, Distributed AI, Natural Language Processing

Start your training experience here!