The demand for professionals in Data Mining, Artificial Intelligence and Machine Learning It is constantly growing, and its impact on various industries is undeniable. This course offers you the opportunity to immerse yourself in a booming field, where you will learn to uncover hidden patterns in large volumes of data and apply advanced techniques to make informed decisions. The skills you will acquire, such as the algorithm handling machine learning and the design of recommendation systems are increasingly valued in the labour market. By the end of the course, you’ll be ready to tackle the challenges of an industry that not only transforms businesses but also redefines the way we interact with technology. Don’t get left behind – join this digital revolution!
Data Mining, Artificial Intelligence and Machine Learning
AI Academy
40 hours
Introduction
Objectives
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Understanding the fundamental concepts of data mining and its applicability across various sectors.
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Identify and apply methodologies relating to the cycle of data mining to solve real-world problems.
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To analyse the relationship between artificial intelligence and big data to optimise processes and decision-making.
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To evaluate machine learning algorithms and how they differ from the deep learning.
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Designing expert systems and understand its operation and applications in various fields.
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Implement techniques for clustering and recommendation systems to improve personalisation.
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Develop skills in neural networks and deep learning to tackle complex problems.
Table of Contents
TEACHING UNIT 1. DATA MINING
1. Data mining
2. What can we do with data mining?
3. What are the potential applications of data mining?
4. Data mining methodology
5. Some statistical techniques used in data mining
6. Decision trees
7. Induction rules
8. Bayesian networks
9. Genetic Algorithms
TEACHING UNIT 2. DATA MINING CYCLE
1. Data mining cycle
2. Text Mining and Web Mining
3. Data mining and marketing
TEACHING UNIT 3. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
1. An Introduction to Artificial Intelligence
2. History
3. The importance of AI
TEACHING UNIT 4. ALGORITHMS APPLIED TO ARTIFICIAL INTELLIGENCE
1. Algorithms applied to artificial intelligence
TEACHING UNIT 5. THE RELATIONSHIP BETWEEN ARTIFICIAL INTELLIGENCE AND BIG DATA
1. The relationship between artificial intelligence and big data
2. AI and Big Data combined
3. The role of Big Data in AI
4. AI technologies currently being used with Big Data
TEACHING UNIT 6. EXPERT SYSTEMS
1. Expert systems
2. Structure of an expert system
3. Inference: Types
4. Stages in the construction of a system
5. Performance and improvements
6. Areas of application
7. Development of an expert system in C#
8. Add uncertainty and probabilities
TEACHING UNIT 7. INTRODUCTION TO MACHINE LEARNING
1. Introduction
2. Classification of machine learning algorithms
3. Examples of machine learning
4. Differences between machine learning and deep learning
5. Types of machine learning algorithms
6. The future of machine learning
TEACHING UNIT 8. DATA STRUCTURE EXTRACTION: CLUSTERING
1. Introduction
2. Algorithms
TEACHING UNIT 9. RECOMMENDATION SYSTEMS
1. Introduction
2. Collaborative filtering
3. Clustering
4. Hybrid recommendation systems
TEACHING UNIT 10. CLASSIFICATION
1. Filing systems
2. Algorithms
TEACHING UNIT 11. NEURAL NETWORKS AND DEEP LEARNING
1. Components
2. Learning
TEACHING UNIT 12. ELECTION SYSTEMS
1. Introduction
2. The process of transitioning from DSS to IDSS
3. Examples of use