Supervised Learning Algorithms and Techniques Course

Explore essential supervised learning algorithms and techniques, gain practical skills, and master predictive modeling for real-world applications in this course.

Artificial Intelligence and Machine Learning Courses Confirmed

Training Locations

This Supervised Learning Algorithms and Techniques Course is available in multiple cities. Please select your preferred location from the list below

Durrës

Albania

Tirana

Albania

Andorra la Vella

Andorra

Escaldes-Engordany

Andorra

Innsbruck

Austria

Salzburg

Austria

Vienna

Austria

Gomel

Belarus

Minsk

Belarus

Antwerp

Belgium

Brussels

Belgium

Banja Luka

Bosnia and Herzegovina

Sarajevo

Bosnia and Herzegovina

Plovdiv

Bulgaria

Sofia

Bulgaria

Dubrovnik

Croatia

Split

Croatia

Zagreb

Croatia

Limassol

Cyprus

Nicosia

Cyprus

Brno

Czech Republic

Prague

Czech Republic

Aarhus

Denmark

Copenhagen

Denmark

Tallinn

Estonia

Tartu

Estonia

Helsinki

Finland

Tampere

Finland

Lyon

France

Marseille

France

Nice

France

Paris

France

Berlin

Germany

Frankfurt

Germany

Hamburg

Germany

Munich

Germany

Athens

Greece

Thessaloniki

Greece

Budapest

Hungary

Debrecen

Hungary

Akureyri

Iceland

Reykjavík

Iceland

Cork

Ireland

Dublin

Ireland

Florence

Italy

Milan

Italy

Naples

Italy

Rome

Italy

Pristina

Kosovo

Prizren

Kosovo

Liepāja

Latvia

Riga

Latvia

Schaan

Liechtenstein

Vaduz

Liechtenstein

Kaunas

Lithuania

Vilnius

Lithuania

Esch-sur-Alzette

Luxembourg

Luxembourg City

Luxembourg

St. Julian's

Malta

Valletta

Malta

Bălți

Moldova

Chișinău

Moldova

La Condamine

Monaco

Monte Carlo

Monaco

Budva

Montenegro

Podgorica

Montenegro

Amsterdam

Netherlands

Rotterdam

Netherlands

The Hague

Netherlands

Ohrid

North Macedonia

Skopje

North Macedonia

Bergen

Norway

Oslo

Norway

Gdańsk

Poland

Kraków

Poland

Warsaw

Poland

Faro

Portugal

Lisbon

Portugal

Porto

Portugal

Bucharest

Romania

Cluj-Napoca

Romania

City of San Marino

San Marino

Serravalle

San Marino

Belgrade

Serbia

Novi Sad

Serbia

Bratislava

Slovakia

Košice

Slovakia

Bled

Slovenia

Ljubljana

Slovenia

Barcelona

Spain

Madrid

Spain

Valencia

Spain

Gothenburg

Sweden

Stockholm

Sweden

Bern

Switzerland

Geneva

Switzerland

Zurich

Switzerland

Kyiv

Ukraine

Lviv

Ukraine

Odesa

Ukraine

Dubai

United Arab Emirates

Birmingham

United Kingdom

Edinburgh

United Kingdom

London

United Kingdom

Manchester

United Kingdom

Rome (Vatican-adjacent)

Vatican City

Training Outlines

Introduction

Supervised learning is a cornerstone of machine learning, used extensively in prediction and classification tasks. This 5-day professional course provides a comprehensive understanding of supervised learning algorithms and techniques, focusing on their implementation and application to real-world problems.

To understand the fundamentals of supervised learning.
To explore different supervised learning algorithms.
To apply supervised learning techniques to solve practical problems.
To evaluate and optimize the performance of machine learning models.
To gain hands-on experience with popular machine learning tools and frameworks.

Course Outlines

Day 1: Introduction to Supervised Learning

Overview of machine learning and supervised learning.
Key concepts: training data, features, labels, and datasets.
Types of supervised learning tasks: regression and classification.
Basic steps in developing a supervised learning model.
Introduction to Python libraries for machine learning.

Day 2: Linear Regression and Extensions

Understanding linear regression and its assumptions.
Implementing linear regression using Python.
Introduction to regularization: Lasso and Ridge regression.
Polynomial regression and dealing with non-linearity.
Evaluating regression models: metrics and error analysis.

Day 3: Classification Algorithms

Overview of classification techniques and applications.
Logistic regression: theory and implementation.
Decision trees: building and pruning techniques.
Support Vector Machines (SVM): kernel tricks and margins.
Metrics for evaluating classification models: accuracy, precision, recall, and F1 score.

Day 4: Ensemble Methods and Advanced Techniques

Introduction to ensemble learning and its benefits.
Bagging methods: Random Forests and their applications.
Boosting techniques: AdaBoost, Gradient Boosting, and XGBoost.
Advanced techniques: Neural Networks and Deep Learning overview.
Parameter tuning and hyperparameter optimization strategies.

Day 5: Model Evaluation and Real-world Applications

Cross-validation techniques for model assessment.
Overfitting vs. underfitting: strategies to find a balance.
Case studies of supervised learning in different industries.
Hands-on project: building a supervised learning model from scratch.
Wrap-up and future directions in supervised learning.

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Supervised Learning Algorithms and Techniques Course. Please review the schedule to find the most convenient option for you. You can also use the below search bar to type the city name and filter the results.