**Introduction:**In 1959, Arthur Samuel, a computer scientist who pioneered the study of artificial intelligence, described the

machine learning – ML- as «the study that gives computers the ability to learn

without being explicitly programmed. Alan Turing’s seminal article (Turing, 1950) introduced a

reference standard to demonstrate the intelligence of machines, such that a machine has

to be smart and respond in a way that cannot be differentiated from that of a human being.

Machine learning is an application of artificial intelligence in which a computer /

machine learns from past experiences (input data) and makes future predictions. The

The performance of such a system should at least be human level.

In this material, we will focus on clustering problems for non-machine learning.

supervised with the K-Means algorithm. For supervised machine learning we will describe the

classification problem with a proof of the design tree algorithm and the regression algorithm

with an example of linear regression. Below is a summary representing the types

machine learning and some algorithms as examples in the following figure:

**Learning objectives:**

• Understand the fundamentals of

Artificial Intelligence and Learning

Automatic

• Describe the learning methods

automatic: supervised and unsupervised

• Use Data Analysis to take

decisions

• Understand the limits of algorithms

• Understand and understand programming

in Python, mathematical knowledge

essentials in AI and basic methods of

programming

**Target audiences:**

Anyone interested in expanding

their knowledge in Artificial Intelligence and

Machine learning

• Engineers, analysts, marketing directors

• Data analysts, data scientists,

data managers

• Anyone interested in techniques of

data mining and machine learning

**Prerequisites**

There are no formal prerequisites for this

certification.