Step into the future of technology with our Data Mining, Machine Learning and Deep Learning course. In a time where data is the new oil, mastering these skills opens up a world of opportunities. As industries increasingly rely on data-driven insights, the demand for professionals adept in supervised and unsupervised learning, as well as deep learning, is skyrocketing. This course empowers you with the ability to extract meaningful patterns from vast datasets, drive innovation, and create smarter solutions. Delivered online, it offers you the flexibility to learn at your own pace, ensuring you stay ahead in a competitive job market. Whether you’re looking to enhance your current role or pivot to a new career, this course is your gateway to becoming a sought-after expert in an ever-evolving field. Join us to transform data into intelligence and shape the future.
Data mining, machine learning and deep learning (Inglés)
Desarrollo de IA
40 h
Presentación
Objetivos
- Understand fundamental concepts of supervised learning techniques.
- Learn to implement regression and classification models effectively.
- Explore unsupervised learning methods for data clustering.
- Grasp the basics of neural networks in deep learning contexts.
- Develop skills to evaluate model performance accurately.
- Acquire knowledge of advanced deep learning architectures.
- Apply machine learning solutions to real-world data problems.
Índice de contenidos
UNIT 1. SUPERVISED LEARNING (I)
1. Introduction
2. Simple, multiple and logistic linear regression (I)
3. Simple, multiple and logistic linear regression (II)
4. Support vector machines (SVM)
5. Decision trees
UNIT 2. SUPERVISED LEARNING (II)
1. KNN (k-nearest neighbors)
2. Naive Bayes
3. Evaluation of supervised models
4. Example exercise
5. Proposed exercise
UNIT 3. UNSUPERVISED LEARNING
1. Introduction to clustering: purconsider and metrics
2. K-means clustering
3. Hierarchical clustering, other techniques and examples
4. Principal component analysis (PCA)
5. PCA example exercise
UNIT 4. DEEP LEARNING
1. Artificial Neural Networks (ANN) (I)
2. Artificial Neural Networks (ANN) (II)
3. Artificial Neural Networks (ANN) (III)
4. Example exercise
5. Proposed exercise