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Artificial Intelligence and Machine Learning Fundamentals

 

Artificial Intelligence and Machine Learning Fundamentals

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Artificial Intelligence and Machine Learning Fundamentals Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing .

What you'll learn

  • Understand the importance, principles, and fields of AI
  • Learn to implement basic artificial intelligence concepts with Python
  • Apply regression and classification concepts to real-world problems
  • Perform predictive analysis using decision trees and random forests
  • Perform clustering using the k-means and mean shift algorithms
  • Understand the fundamentals of deep learning via practical examples

Requirements

  • You do not need any prior experience in AI.
  • We recommend that you have knowledge of high school level mathematics and at least one programming language, preferably Python.

Description

Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing you to Python and discussing using AI search algorithms. You will learn math-heavy topics, such as regression and classification, illustrated by Python examples.

You will then progress on to advanced AI techniques and concepts, and work on real-life data sets to form decision trees and clusters. You will be introduced to neural networks, which is a powerful tool benefiting from Moore's law applied on 21st-century computing power. By the end of this course, you will feel confident and look forward to building your own AI applications with your newly-acquired skills!

About the Author

Zsolt Nagy is an engineering manager in an ad tech company heavy on data science. After acquiring his MSc in inference on ontologies, he used AI mainly for analyzing online poker strategies to aid professional poker players in decision making. After the poker boom ended, he put extra effort into building a T-shaped profile in leadership and software engineering.


Who this course is for:

  • This course is ideal for software developers and data scientists, who want to enrich their projects with machine learning.
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