artificial intelligence explained

artificial intelligence explained

🔍 Diploma in Machine Learning with Python
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This free online artificial intelligence course expands your knowledge of machine learning using Python as a base..

Are you interested in AI and would like to delve deeper into machine learning?

In the course, you will learn about the various models of machine learning depending on the objectives to be achieved. Be introduced to multiple logarithms, along with the numerous parametric and non-parametric models. Be a cut above the rest by understanding machine learning with this easy-to-follow course with optional certification!

🎓 What You’ll Learn in this FREE Course:
Define machine learning
Explain feature scaling and data cleaning
Outline feature engineering
Distinguish between parametric and non-parametric models
Discuss the exploratory data analysis on the Iris dataset
Contrast categorical variables to numerical variables
Explain decision trees and implementation
Define ensemble learning
Outline bootstrap sampling
Compare SVM (support vector machines) hard margin to SVM soft margin
List the various drawbacks of the PCA (principal component analysis)
Recall the main applications of the PCA

📚 Course Highlights:

This diploma course in machine learning with Python highlights the many benefits that Python has within the frameworks of artificial intelligence (AI). Machine learning is a specific branch of artificial learning that is associated with computer science. It is about making software applications predict outcomes more accurately without any prior explicit programming. It provides computers with the capacity to learn without being obviously programmed. This course will cover two types of machine learning: supervised and unsupervised. Linear regression used to solve a regression problem is an excellent example of supervised machine learning. On the other hand, highlighting various customer groups to strategies marketing is a practical example of unsupervised learning. Within these two types of machine learning, various models depend on the purpose you would like to achieve. For instance, there are parametric and non-parametric models, as covered in this course.

Another crucial theme covered is the use of algorithms. Many algorithms will be examined, each of which, as models, has its advantages and disadvantages. In this course, you are taught about the random forest. It is a slightly modified bagging algorithm, a collection of a decorrelated decision tree. The random forest is used because it works well with non-linear data, lowers the risk of overfitting and runs efficiently on large datasets.

This course is a windfall to computer science students and professionals who are already familiar with Python and would like to become an expert in the field of machine learning. This course has not been updated with the use of Generative AI models, like ChatGPT.

🔥 Pro Tip: Start your free AI course today and explore artificial intelligence in English with this artificial intelligence introduction. Subscribe for more tutorials and join our community to learn about artificial intelligence in computer systems!

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