Artificial intelligence

Become an expert in artificial intelligence!

KBS Academy offers specialized training in Artificial Intelligence, designed for all levels. Our programs cover AI fundamentals, machine learning, data analysis, and advanced algorithms. Whether you’re a beginner or an experienced professional, our training will equip you with practical skills and insights on applying AI to innovative solutions in your sector. Stay ahead with tomorrow’s technologies—enroll now in our Artificial Intelligence training and become a key player in innovation within your field!

Machine learning training

Training Objectives

Main Objective : Are you interested in artificial intelligence and its applications? Do you want to master machine learning and implement real-world projects? Do you seek to enhance your knowledge and skills? Are you looking for high-quality training? This program is tailored for you, providing a crucial package of essential knowledge to effectively master machine learning.

Specific Objectives : By the end of this training, you will: 1. Understand the structure of a real ML project. 2. Learn the most renowned ML algorithms. 3. Implement various machine learning projects. 4. Be able to include these case studies in your resume. 5. Enhance your profile as an ML specialist. 6. Gain confidence for ML-related interviews.

Module descriptions

Module 01: General framework of machine learning (one day)

1. Artificial Intelligence.

2.Data Analysis.

3.Machine Learning.

4.Machine Learning Approaches.

5.Machine Learning Models.

Module 02: Supervised machine learning (one and a half days)

1. Theoretical aspects of K-NN.

2.Practical case with a K-NN project.

3.Theoretical aspects of Naïve Bayes.

4.Practical case with a Naïve Bayes project.

5.Theoretical aspects of SVM.

6.Practical case with an SVM project.

7.Theoretical aspects of Decision Tree.

8.Practical case with a Decision Tree project.

9.Theoretical aspects of Random Forest.

10.Practical case with a Random Forest project.

11.Theoretical aspects of Linear Regression.

12.Practical case with a Linear Regression project.

Module 03: unsupervised machine learning (one day)

1.Theoretical aspects of K-means.

2.Practical case with a K-means project.

3.Theoretical aspects of Apriori.

4.Practical case with an Apriori project.

Price (Excl. Tax): €2000 (3 days)

Field :

Artificial intelligence, development, data

Level :

Beginner and intermediate

Target audience

Participant profile : Enthusiasts of data science and artificial intelligence, and anyone with knowledge in AI and Python.
Prerequisites : Basic understanding of mathematics and statistics, and fundamentals of the Python programming language.

Training duration

Number of modules : Three modules.
Total estimated duration : 3 or 4 days.

Teaching methodology

The training combines two pedagogical methods:

Affirmative method: To explain the fundamentals of machine learning.
Applicative/Demonstrative method: To conduct exercises, case studies, and practical projects.

Evaluation and certification

Evaluation methods: Quizzes, tests, final exam.
Validation criteria: Minimum score of 80%.
Type of certification: Certificate of participation.

Equipment and resources

Necessary tools and software: Python, Anaconda, Jupyter Notebook.

Lab environment

In this training, learners will use Jupyter Notebook to complete their tasks.