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MOOC Machine Learning y Big Data para la Bioinformática. 5ª Edición
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MODULE 3: Data Science and Machine Learning
3.1. Data science and machine learning: what, why and how?
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3.1. Data science and machine learning: what, why and how?
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◄ Module 3 discussion forum
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Module 1 discussion forum
1.1. Bioinformatics: what, why and how?
1.2. Bioinformatics: applications in biosciences and biohealth
Module 1 Questionnaire
Module 2 discussion forum
2.1. The problem: How to obtain and prepare the data?
2.2. Pre-processing and exploratory analysis
2.3. Functional annotation analysis
Module 2 questionnaire
Module 3 discussion forum
3.2. Supervised Learning
3.3. Unsupervised Learning
Module 3 questionnaire
Module 4 discussion forum
4.1. Regression: What, what for and how?
4.2. Classical Regression Methods
4.3. Machine Learning methods for regression
Module 4 Questionnaire
Module 5 discussion forum
5.1. Classification: What, what for and how?
5.2. Standard methods in classification
5.3. Advanced classification methods
Module 5 Questionnaire
Module 6 discussion forum
6.1. Clustering and Association Rules: What, what for and how?
6.2. Clustering
6.3. Association Rules
Module 6 Questionnaire
Module 7 discussion forum
7.1. Introduction to Big Data
7.2. Introduction to Spark using Python
7.3. Machine Learning in Spark: MLib
7.4. Classification, Regression, Clustering and Association Rules in Spark
Module 7 Questionnaire
Module 8 discussion forum
8.1. How to use KNIME? - Workflows
8.2. How to solve a problem with KNIME?
8.3. Supervised Learning: Regression and Classification in KNIME
Datasets example
Example data flows
How to install KNIME
Module 8 Questionnaire
Final Questionnaire
Presentation and Welcome
Course guide
Announcements and updates
Canteen
Información sobre Certificado
Information Certificate
3.2. Supervised Learning ►
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