# Corso di formazione Artificial Neural Networks, Machine Learning and Deep Thinking

bspkannmldt

## Durata

21 ore (generalmente 3 giorni pause incluse)

## Overview

La rete neurale artificiale è un modello di dati computazionale utilizzato nello sviluppo di sistemi di Artificial Intelligence (AI) in grado di eseguire attività "intelligenti". Neural Networks sono comunemente utilizzate nelle applicazioni di Machine Learning (ML), che sono esse stesse un'implementazione dell'IA. Deep Learning è un sottoinsieme di ML.

Machine Translated

## Struttura del corso

### 1. Understanding classification using nearest neighbors

• The kNN algorithm
• Calculating distance
• Choosing an appropriate k
• Preparing data for use with kNN
• Why is the kNN algorithm lazy?

### 2. Understanding naive Bayes

• Basic concepts of Bayesian methods
• Probability
• Joint probability
• Conditional probability with Bayes' theorem
• The naive Bayes algorithm
• The naive Bayes classification
• The Laplace estimator
• Using numeric features with naive Bayes

### 3. Understanding decision trees

• Divide and conquer
• The C5.0 decision tree algorithm
• Choosing the best split
• Pruning the decision tree

### 4. Understanding classification rules

• Separate and conquer
• The One Rule algorithm
• The RIPPER algorithm
• Rules from decision trees

### 5. Understanding regression

• Simple linear regression
• Ordinary least squares estimation
• Correlations
• Multiple linear regression

### 7. Understanding neural networks

• From biological to artificial neurons
• Activation functions
• Network topology
• The number of layers
• The direction of information travel
• The number of nodes in each layer
• Training neural networks with backpropagation

### 8. Understanding Support Vector Machines

• Classification with hyperplanes
• Finding the maximum margin
• The case of linearly separable data
• The case of non-linearly separable data
• Using kernels for non-linear spaces

### 9. Understanding association rules

• The Apriori algorithm for association rule learning
• Measuring rule interest – support and confidence
• Building a set of rules with the Apriori principle

### 10. Understanding clustering

• Clustering as a machine learning task
• The k-means algorithm for clustering
• Using distance to assign and update clusters
• Choosing the appropriate number of clusters

### 11. Measuring performance for classification

• Working with classification prediction data
• A closer look at confusion matrices
• Using confusion matrices to measure performance
• Beyond accuracy – other measures of performance
• The kappa statistic
• Sensitivity and specificity
• Precision and recall
• The F-measure
• ROC curves
• Estimating future performance
• The holdout method
• Cross-validation
• Bootstrap sampling

### 12. Tuning stock models for better performance

• Using caret for automated parameter tuning
• Creating a simple tuned model
• Customizing the tuning process
• Improving model performance with meta-learning
• Understanding ensembles
• Bagging
• Boosting
• Random forests
• Training random forests
• Evaluating random forest performance

### 13. Deep Learning

• Three Classes of Deep Learning
• Deep Autoencoders
• Pre-trained Deep Neural Networks
• Deep Stacking Networks

★★★★★
★★★★★

## I nostri clienti

#### is growing fast!

We are looking to expand our presence in Italy!