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Plan Szkolenia
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Data preprocessing
- Data Cleaning
- Data integration and transformation
- Data reduction
- Discretization and concept hierarchy generation
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Statistical inference
- Probability distributions, Random variables, Central limit theorem
- Sampling
- Confidence intervals
- Statistical Inference
- Hypothesis testing
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Multivariate linear regression
- Specification
- Subset selection
- Estimation
- Validation
- Prediction
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Classification methods
- Logistic regression
- Linear discriminant analysis
- K-nearest neighbours
- Naive Bayes
- Comparison of Classification methods
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Neural Networks
- Fitting neural networks
- Training neural networks issues
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Decision trees
- Regression trees
- Classification trees
- Trees Versus Linear Models
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Bagging, Random Forests, Boosting
- Bagging
- Random Forests
- Boosting
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Support Vector Machines and Flexible disct
- Maximal Margin classifier
- Support vector classifiers
- Support vector machines
- 2 and more classes SVM’s
- Relationship to logistic regression
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Principal Components Analysis
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Clustering
- K-means clustering
- K-medoids clustering
- Hierarchical clustering
- Density based clustering
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Model Assesment and Selection
- Bias, Variance and Model complexity
- In-sample prediction error
- The Bayesian approach
- Cross-validation
- Bootstrap methods
28 godzin
Opinie uczestników (4)
Forma przekazania wiedzy poprzez dokładne wyjaśnienie zagadnienia.
Katarzyna Stańczak - Mowi Poland SA
Szkolenie - Data Mining and Analysis
Bardzo dobrze przekazywana wiedza przez prowadzącego.Brak pytań bez odpowiedzi.
Karolin Papaj - Mowi Poland SA
Szkolenie - Data Mining and Analysis
I was benefit from the guidance and sharing life examples + answering all questions.
Marta Melloch - Amazon Development Center Poland Sp. z o.o.
Szkolenie - Data Mining and Analysis
I really enjoyed the all the best.
