AIM1010

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Machine Learning in Data Analytics

Utilization of artificial intelligence applications is becoming an increasingly important part of data analytics. In particular, the increase in the amount of data to be collected will allow the development of ever better models. Software robotics and the development of machine learning methods have also provided data analysts with new tools for modeling data and dynamic systems.

Audience

Machine learning in data analytics is intended for data analysts who are not yet familiar with machine learning methods. The course is also suitable for individuals interested in data analytics or artificial intelligence. The course is basic in nature.

Prior knowledge

The exercises requires basic knowledge of Python e.g. course Python 3 - Fundamentals 

Participants will benefit from mastering statistical methods.

Courseware

The course material is in English.

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Day 1 

Overview of artificial intelligence methods

- Machine learning drivers 

- History of artificial intelligence

- Applications of artificial intelligence methods now and in the future

- Artificially intelligent? 

Three methods of machine learning

- Controlled learning

- Uncontrolled learning

- Incentive learning

- It's just analytics! 

AI Analyst Toolkit

- Scikit-learn

- Azure

- Hadoop

- Excel is dead? 

Review of statistical methods

- Standard deviation and correlation

- Statistical independence

- Correlated, so hanging? 

Self-reflection of artificial intelligence

- Cross-validation

- Error types

- Brush and variance dilemma

- #OccaminParty Knife

Day 2 

Decrease variables

- Selection of variables

- Extraction of variables

- Path Selection Methods

- Genetic algorithms

- Curse of dimensions! 

Data visualization

- Principal Component Analysis

- Analysis of independent components

- I believe when I see! 

classification methods

- Overview of grading methods

- K-neighbor method

- Bayesian classification

- Class trip to artificial neural networks 

Artificial neural networks

- Multilevel perceptrons

- Extreme learning machines

- Deep learning neural networks

- Convolutional neural networks

- AlphaGo 

- The return of the positron brain? 

Artificial Intelligence and Software Robotics

-... Läs mer

Day 1 

Overview of artificial intelligence methods

- Machine learning drivers 

- History of artificial intelligence

- Applications of artificial intelligence methods now and in the future

- Artificially intelligent? 

Three methods of machine learning

- Controlled learning

- Uncontrolled learning

- Incentive learning

- It's just analytics! 

AI Analyst Toolkit

- Scikit-learn

- Azure

- Hadoop

- Excel is dead? 

Review of statistical methods

- Standard deviation and correlation

- Statistical independence

- Correlated, so hanging? 

Self-reflection of artificial intelligence

- Cross-validation

- Error types

- Brush and variance dilemma

- #OccaminParty Knife

Day 2 

Decrease variables

- Selection of variables

- Extraction of variables

- Path Selection Methods

- Genetic algorithms

- Curse of dimensions! 

Data visualization

- Principal Component Analysis

- Analysis of independent components

- I believe when I see! 

classification methods

- Overview of grading methods

- K-neighbor method

- Bayesian classification

- Class trip to artificial neural networks 

Artificial neural networks

- Multilevel perceptrons

- Extreme learning machines

- Deep learning neural networks

- Convolutional neural networks

- AlphaGo 

- The return of the positron brain? 

Artificial Intelligence and Software Robotics

- Utilization of artificial intelligence in software robotics

- RPA + AI == Skynet

Kursfakta

Kurs-ID: AIM1010
Längd: 2 dagar
Inregistrering: 09.00
Kursstart: 09.30
Kursslut (ca): 17.00

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Ort och datum

Stockholm
24 okt-25 okt
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5 dec-6 dec
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Göteborg
24 okt-25 okt
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5 dec-6 dec
Boka nu!