Remote Course for Artificial Intelligence and Machine Learning Fundamentals

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Description
Content
Target Audience
Benefits
Certificates

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.

As you make your way through the course, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. By the end of this course, you will be confident when it comes to building your own AI applications with your newly acquired skills!

Artificial Intelligence and Machine Learning Fundamentals course is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).

Principles of Artificial Intelligence
AI with Search Techniques and Games
Regression Polynomial and Support Vector Regression
Classification
Using Trees for Predictive Analysis
Clustering
Deep Learning with Neural Networks

This course is intended for developers and data scientists who want to enrich their projects with machine learning.

You don’t need previous experience in AI. However, it is recommended that you have high school level math skills and at least one programming language (preferably Python).

In this course, you will implement AI techniques in order to solve business problems, and will learn to:

Understand the importance, principles, and fields of AI
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
Carry out clustering using the k-means and mean shift algorithms
Understand the fundamentals of deep learning via practical examples

Certificate of attendance at the course published by Semos Education

Description

Content

Principles of Artificial Intelligence
AI with Search Techniques and Games
Regression Polynomial and Support Vector Regression
Classification
Using Trees for Predictive Analysis
Clustering
Deep Learning with Neural Networks

Target Audience

This course is intended for developers and data scientists who want to enrich their projects with machine learning.

You don’t need previous experience in AI. However, it is recommended that you have high school level math skills and at least one programming language (preferably Python).

Benefits

In this course, you will implement AI techniques in order to solve business problems, and will learn to:

Understand the importance, principles, and fields of AI
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
Carry out clustering using the k-means and mean shift algorithms
Understand the fundamentals of deep learning via practical examples

Certificates

Certificate of attendance at the course published by Semos Education

Past experiences

What people say about us

- Aleksandar Maksimov Student CertNexus Artificial Intelligence Academy

Because Artificial Intelligence is the challenge of the future. With the modernization of lifestyles and technological advancements on a global scale, artificial intelligence is increasingly playing a key role in all aspects of life and development in society.
- Kristijan Stojoski Artificial Intelligence Academy

With taking the first step and investing enough effort, everyone can master this modern topic and stand out in the job market in one of the fastest-growing industries in the world.
- Viktor Vanchov Artificial Intelligence Academy

The final project taught me many useful things, beyond the realm of video games. However, it greatly helped me get an idea of how machines 'learn' and how powerful they can be.