AI TECHNIQUES AND TOOLS THROUGH PYTHON. SUPERVISED LEARNING: CLASSIFICATION METHODS, ENSEMBLE LEARNING AND NEURAL NETWORKS

By F. M. Asensio

Most of the supervised learning techniques for classification are developed throughout this book from a methodological point of view and from a practical point of view with applications through Python software. The following techniques are covered in depth: Nearest Neighbour (kNN), Support Vector Machine (SVM), Naive Bayes, Ensemble Methods, Bagging, Boosting, Voting, Stacking, Blending, Random Forest, Neural Networks, Multilayer Perceptron, Radial Basis Networks, Hopfield Networks, LSTM Networks, RNN Recurrent Networks, GRU Networks and Neural Networks for Time Series Prediction.

• Language: English
• Publisher: Lulu.com
• Release date: Jul 13, 2025
• ISBN: 9781326288181

Book preview

AI TECHNIQUES AND TOOLS THROUGH PYTHON.

SUPERVISED LEARNING: CLASSIFICATION METHODS, ENSEMBLE LEARNING AND NEURAL NETWORKS

ASENSIO F. M.

© 2025, Author: Asensio F. M.

ISBN: 978-1-326-28818-1

Título: AI Techniques and Tools Through Python. Supervised Learning: Classification Methods, Ensemble Learning and Neural Networks

TABLE OF CONTENTS

FIRST CONCEPTS IN ARTIFICIAL INTELLIGENCE

1.1 ARTIFICIAL INTELLIGENCE

1.2 ARTIFICIAL INTELLIGENCE TECHNIQUES

1.2.1 Machine Learning (ML)

1.2.2 Artificial Neural Networks (ANN)

1.2.3 Evolutionary Algorithms

1.2.4 Fuzzy Logic

1.2.5 Natural Language Processing (NLP)

1.2.6 6. Expert Systems

1.2.7 Optimisation Algorithms

1.2.8 Computer Vision

1.2.9 Automatic Reasoning

1.2.10 Intelligent Agents

1.3 MACHINE LEARNING

1.3.1 Descriptive or unsupervised learning techniques

1.3.2 Predictive or supervised learning techniques

1.4 TOOLS FOR ARTIFICIAL INTELLIGENCE

1.4.1 TensorFlow

1.4.2 Keras

1.4.3 PyTorch

1.4.4 Scikit-learn

1.4.5 Apache Mahout

1.4.6 Microsoft Cognitive Toolkit (CNTK)

1.4.7 H2O.ai

1.4.8 RapidMiner

1.4.9 OpenCV

1.4.10 Google Cloud AI Platform

1.4.11 IBM Watson

1.4.12 Amazon SageMaker

1.4.13 DataRobot

1.4.14 Colab (Google Colaboratory)

CLASSIFICATION THROUGH THE NEAREST NEIGHBOUR kNN ALGORITHM. PROCESSING WITH PYTHON

2.1 THE kNN ALGORITHM

2.1.1 Distance metrics

2.1.2 Calculation of KNN: defining k

2.1.3 k-NN applications in machine learning

2.1.4 Advantages and disadvantages of the KNN algorithm

2.2 THE kNN ALGORITHM IN PYTHON

2.2.1 KNN with binary dependent variable

2.2.2 KNN with multiple dependent variable

CLASSIFICATION WHITH SUPPORT VECTOR MACHINE (SVM). PROCESSING THROUGH PYTHON

3.1 SUPPORT VECTOR MACHINE

3.1.1 SVM classifiers

3.1.2 Linear SVM classifiers

3.1.3 Non-linear SVM classifiers

3.1.4 Support Vector Regression SVR

3.1.5 Comparison between different classifiers

3.1.6 Applications of SVM classifiers

3.2 SUPPORT VECTOR MACHINE WITH PYTHON

3.2.1 SVM with binary dependent variable

3.2.2 SVM with multiple dependent variable

CLASSIFICATION THROUGH THE NAIVE BAYES ALGORITHM. PROCESSING THROUGH PYTHON

4.1 NAIVE BAYES ALGORITHM

4.1.1 Naive Bayes classifiers

4.1.2 Class conditional probabilities

4.1.3 A priori probabilities

4.1.4 Types of Naive Bayes classifiers

4.1.5 Advantages and disadvantages of the Naive Bayes classifier

4.1.6 Applications of the Naive Bayes classifier

4.2 NAIVE BAYES THROUGH PYTHON

4.2.1 Naive Bayes Gaussian

4.2.2 Naive Bayes Multinomial

4.2.3 Naive Bayes with multiple dependent variable

ENSEMBLE METHODS. PROCESSING THROUGH PYTHON

5.1 ENSEMBLE METHODS

5.1.1 Bagging (Bootstrap Aggregating)

5.1.2 Boosting

5.1.3 Stacking (Stacked Generalization)

5.1.4 Voting

5.1.5 Random Forest

5.1.6 Blending

5.1.7 Python and ensemble methods

5.2 BAGGING IN PYTHON

5.3 BOOSTING IN PYTHON

5.4 STACKING IN PYTHON

5.5 VOTING IN PYTHON

NEURAL NETWORK MODELS. TREATMENT WITH PYTHON

6.1 DESCRIPTION OF A NEURAL NETWORK

6.1.1 Definition

6.1.2 Output function and transfer or activation functions

6.2 NEURAL NETWORKS AND PREDICTIVE MODEL FITTING (SUPERVISED LEARNING)

6.3 LEARNING IN NEURAL NETWORKS

6.4 FUNCTIONING OF A NEURAL NETWORK

6.5 THE BACK-PROPAGATION LEARNING ALGORITHM (BACK-PROPAGATION)

6.6 TIME SERIES ANALYSIS USING NEURAL NETWORKS

6.7 NEURAL NETWORKS ES VIA PYTHON: MULTILAYER PERCEPTRON (MLP)

6.8 NEURAL NETWORKS VIA PYTHON: RADIAL BASIS NETWORK (RBF)

6.9 ADALINE NEURAL NETWORKS WITH PYTHON

6.10 HOPFIELD NEURAL NETWORKS FOR PYTHON PAtron RECOGNITION

6.11 NEURAL NETWORKS FOR TIME SERIES PREDICTION IN PYTHON

6.11.1 LSTM networks for time series forecasting

6.11.2 RNN Recurrent Networks for time series forecasting

6.11.3 GRU networks for time series forecasting

FIRST CONCEPTS IN ARTIFICIAL INTELLIGENCE

Today, the amount of data generated by both humans and machines far exceeds the ability of humans to absorb, interpret and make complex decisions based on that data. Artificial intelligence is the foundation of all machine learning and the future of all complex decision-making processes. Artificial Intelligence combines mathematical algorithms and Machine Learning, Deep Learning and Big Data techniques to extract the knowledge contained in data and present it in a comprehensible and automatic way.

Artificial intelligence (AI) is a branch of computer science that seeks to develop systems capable of performing tasks that traditionally require human intelligence, such as pattern recognition, decision-making, natural language processing and learning. Within this field, machine learning techniques play a crucial role, especially in recent developments in AI.

Among the areas of application of Artificial Intelligence are the following:

Machine Learning: Algorithms that allow machines to learn from data and improve over time without being explicitly programmed.

Natural Language Processing (NLP): Allows machines to understand, interpret and generate human language. An example is ChatGPT.

Computer Vision: Enables machines to see, identify and process visual objects and scenes.

Robotics: Creation of robots that can interact with the environment and perform physical tasks.

Artificial Neural Networks: Models inspired by the human brain for complex tasks such as speech, image and pattern recognition.

Artificial intelligence techniques are the methods and approaches used to develop systems that can perform tasks that normally require human intelligence. These techniques fall into several categories, each with its own set of approaches and algorithms. The following paragraphs describe the main AI techniques

Machine learning is a branch of AI that allows machines to learn from data without being explicitly programmed to perform specific tasks. There are several approaches within machine learning:

Supervised learning: The model learns from labelled data, i.e. data with a known response. The goal is for the model to predict the correct output for new data based on what it has learned. Examples include linear regression, classification and neural networks.

Unsupervised learning: The model finds patterns or structures in unlabelled data. It is used for tasks such as customer segmentation, clustering analysis and dimensionality reduction. Examples include the K-means algorithm and Principal Component Analysis (PCA).

Reinforcement learning: The model learns to make decisions through trial and error, receiving rewards or penalties based on the actions it takes. It is common in control tasks and games, such as learning autonomous agents. Examples include Q-learning and Deep Q Networks (DQN).

Neural networks are models inspired by the human brain, consisting of layers of neurons that process information. They are used for complex tasks such as speech recognition, image recognition and natural language processing. Neural networks are generally classified as follows:

Deep Learning: A subcategory of machine learning that uses neural networks with many layers, known as deep neural networks. They are especially effective for complex pattern recognition in large amounts of data. Examples include convolutional networks (CNNs) for computer vision and recurrent networks (RNNs) for sequence processing, such as in machine translation.

Generative Antagonistic Networks (GANs): Use two competing neural networks: one generates false data (such as images) and the other evaluates it. They are popular in the creation of content such as images and music.

Evolutionary algorithms are methods inspired by biological evolution, such as natural selection and mutation. These algorithms seek to find optimal solutions to complex problems by simulating evolutionary processes.

Genetic Algorithms (GA): They use mechanisms such as selection, crossover and mutation to evolve solutions to problems. They are used in the optimisation of processes and the search for solutions in large and complex spaces.

Evolutionary programming and genetic programming: Variants of genetic algorithms that are used for the evolution of computer programs or algorithms.

Fuzzy logic is a mathematical approach that handles uncertainty and imprecision, allowing systems to make decisions based on information that is not entirely accurate. It uses values between 0 and 1 (instead of just 0 and 1, as in classical logic) to represent degrees of truth.

It is applied in control systems, such as smart thermostats and robots that make decisions with uncertain information.

Natural language processing (NLP) is concerned with enabling machines to understand and generate human language. Techniques in NLP include:

Sentiment analysis: Determining whether a text expresses a positive, negative or neutral opinion.

Language models: Such as Transformers