Visão Geral

Curso Machine Learning and Predictive Analytics, Este novas oportunidades de avaliar o desempenho e identificar como podem capitalizar seus ativos de informação e-los para avaliar o desempenho e desempenho e Os principais princípios e abordagens da aprendizagem da máquina devem ser desenvolvidos com problemas com problemas de relacionamento e de eles.

Materiais

Conteúdo Programatico

Introduction to predictive analytics

• Definindo análise preditiva - introdução
  
• Business Relevance of PA - Business intelligence and applications
• Relevance of pattern recognition, classification, optimisation
• Predictive analytics and big data
• Case study: A business application using predictive analytics approaches

Predictive analytics in business - applications

1. Sources of data and value of knowledge
2. Identify a wide range of applications for predictive analytics:
3. Marketing and recommender systems, fraud detection, business process analytics, credit risk modelling, web analytics and others
4. Social media and human behaviour analytics
5. Case study: Email targeting - which message will a customer answer?

Analytics models and techniques

1. Introduction to analytics modelling
   
2. Types of analytics models:
3. Predictive models
4. Survival models
5. Descriptive models
6. Define pattern recognition, inferring data and data visualisation
7. Briefing learning and regression approaches
8. Comparison of approaches - use and goals

Introduction to machine learning

1. Introduction: Basic principles
   
2. Basic notions of learning
3. Introduction to learning problems (classification, clustering and reinforcement) and literature
4. Identifying different learning approaches - supervised, unsupervised and reinforcement
5. Case study on different types of learning

Machine learning for predictive analytics 

1. Review of types of problems
2. Machine Learning techniques:
3. Decision tree learning
4. Artificial neural networks
5. Clustering
6. Naive Bayes classifier
7. k-nearest neighbours
8. Genetic algorithms
9. Case study on problem - a "suitable" predictive modelling technique

Regression techniques for predictive analytics

1. Review of types of problems (application)
2. Linear regression models
3. Survival or duration analysis (time to event analysis)
4. Ensemble learning and random forest
5. Case study on problem - a "suitable" predictive modelling technique

Advanced topics and Software tools

1. Analytics in the context of big data
2. Predictive analytics as art and science
3. Software tools; the R project and Python
4. Trends and challenges in predictive analytics - where are we going?