The Data Mining, Machine Learning and Deep Learning (Big Data) course offers you the chance to immerse yourself in the fascinating world of artificial intelligence and data analysis. The technology sector is currently booming, and the demand for skilled professionals in these fields is higher than ever. Throughout the course, You will develop key skills in supervised and unsupervised learning, as well as in deep learning, preparing you to tackle the challenges of analysing large volumes of data. This knowledge will It will enable the discovery of hidden patterns and the generation of valuable insights for strategic decision-making across a range of industries. By choosing this course, you’ll position yourself at the forefront of the job market, opening the door to exciting and well-paid career opportunities. With a robust theoretical approach and high-quality educational resources, you’ll be equipped to become an expert in the field of Big Data and artificial intelligence.
Data Mining, Machine Learning and Deep Learning (Big Data)
Big Data
40 hours
Introduction
Objectives
- To understand the key concepts of supervised and unsupervised learning in data analysis.
- Identify and apply supervised learning algorithms to solve specific business problems.
- To explore clustering and dimensionality reduction techniques in unsupervised learning.
- Implement deep neural networks to improve accuracy in complex tasks.
- Evaluate machine learning models using standard metrics to ensure their effectiveness.
- Use big data tools to manage and process large volumes of information.
- Develop skills to interpret results and communicate findings effectively.
Table of Contents
TEACHING UNIT 1. SUPERVISED LEARNING (I)
Introduction
Linear, multiple and logistic regression (I)
Linear, multiple and logistic regression (II)
Support Vector Machine (SVM)
Decision trees
TEACHING UNIT 2. SUPERVISED LEARNING (II)
KNN (K-Nearest Neighbours)
Naive Bayes
Evaluation of supervised models
Example exercise
Suggested exercise
TEACHING UNIT 3. UNSUPERVISED LEARNING
An Introduction to Clustering: Purpose and Metrics
K-means clustering
Hierarchical clustering, other techniques and examples
Principal component analysis (PCA)
PCA example exercise
TEACHING UNIT 4. DEEP LEARNING
Artificial Neural Networks (ANN) (I)
Artificial Neural Networks (ANN) (II)
Artificial Neural Networks (ANN) (III)
Example exercise
Suggested exercise
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