You will be able to design customized and intuitive user experiences through this 100% online university degree"

Computational Intelligence serves institutions to improve productivity in software development. Its tools have the ability to handle unstructured data, learn from past experiences and adapt to changes in dynamic environments. In addition, AI can predict potential application problems before they happen, allowing professionals to take preventative measures to avoid costly problems in the future. In this context, the most prestigious international IT companies are looking to actively incorporate Software Architecture specialists for QA Testing.

For this reason, TECH implements an innovative program for programmers to get the most out of optimization and performance management in AI tools. Designed by world-class experts, the curriculum will delve into programming algorithms to develop products with intelligent systems. The syllabus will also delve into the essential extensions for Visual Studio Code, today's most widely used source code editor. Moreover, the teaching materials will address the integration of AI in database management to detect possible failures and create unittests This is a university degree that has a diversity of audiovisual content in multiple formats and a network of real simulations to bring the development of the program closer to the reality of IT practice.

In order to achieve the proposed learning objectives, this program is taught through an online teaching methodology. In this way, professionals will be able to perfectly combine their work with their studies. In addition, you will enjoy a first-class teaching staff and multimedia academic materials of great pedagogical rigor such as master classes, interactive summaries or practical exercises. The only requirement for accessing the Virtual Campus is that students have an electronic device with Internet access, and can even use their cell phone.

You will gain a holistic perspective on how Machine Learning impacts and improves every stage of software development"

This Professional master’s degree in Artificial Intelligence in Programming contains the most complete and up-to-date program on the market. Its most notable features are:

• The development of practical cases presented by experts in Artificial Intelligence in programming
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning 
• Its special emphasis on innovative methodologies  
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments 
• Content that is accessible from any fixed or portable device with an Internet connection 

Are you looking to apply Transformational Models for natural language processing to your practice? Achieve it thanks to this innovative program"

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.

You will delve into the testing lifecycle, from the creation of test cases to the detection of bugs"

Relearning will enable you to learn with less effort and more performance, involving you more in your professional specialization"

This Professional master’s degree will provide graduates with a holistic approach, which will give them a significant advantage in IT development by equipping them with specific skills. To achieve this, the course will range from development environment preparation to software optimization and AI implementation in real projects. The syllabus will delve into aspects such as no-code design of interfaces, use of ChatGPT to optimize code or the application of Machine Learning in QA Testing.  In this way, the graduates will implement innovative solutions in an effective way in various applications such as web and mobile projects. 

Update your knowledge about Artificial Intelligence in Programming through innovative multimedia content"

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence 

1.1.1. When Do We Start Talking About Artificial Intelligence?
1.1.2. References in Film 
1.1.3. Importance of Artificial Intelligence 
1.1.4. Technologies that Enable and Support Artificial Intelligence 

1.2. Artificial Intelligence in Games 

1.2.1. Game Theory 
1.2.2. Minimax and Alpha-Beta Pruning 
1.2.3. Simulation: Monte Carlo 

1.3. Neural Networks 

1.3.1. Biological Fundamentals 
1.3.2. Computational Model 
1.3.3. Supervised and Unsupervised Neural Networks 
1.3.4. Simple Perceptron 
1.3.5. Multilayer Perceptron

1.4. Genetic Algorithms 

1.4.1. History 
1.4.2. Biological Basis 
1.4.3. Problem Coding 
1.4.4. Generation of the Initial Population 
1.4.5. Main Algorithm and Genetic Operators 
1.4.6. Evaluation of Individuals: Fitness 

1.5. Thesauri, Vocabularies, Taxonomies 

1.5.1. Vocabulary 
1.5.2. Taxonomy 
1.5.3. Thesauri 
1.5.4. Ontologies 
1.5.5. Knowledge Representation: Semantic Web 

1.6. Semantic Web 

1.6.1. Specifications RDF, RDFS and OWL 
1.6.2. Inference/ Reasoning 
1.6.3. Linked Data 

1.7. Expert systems and DSS 

1.7.1. Expert Systems 
1.7.2. Decision Support Systems 

1.8. Chatbots and Virtual Assistants

1.8.1. Types of Assistants: Voice and Text Assistants
1.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialog Flow 
1.8.3. Integrations: Web, Slack, Whatsapp, Facebook 
1.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant

1.9. AI Implementation Strategy 

1.10. Future of Artificial Intelligence

1.10.1. Understand How to Detect Emotions Using Algorithms
1.10.2. Creating a Personality: Language, Expressions and Content
1.10.3. Trends of Artificial Intelligence
1.10.4. Reflections

Module 2. Data Types and Data Life Cycle 

2.1. Statistics

2.1.1. Statistics: Descriptive Statistics, Statistical Inferences
2.1.2. Population, Sample, Individual
2.1.3. Variables: Definition, Measurement Scales

2.2. Types of Data Statistics

2.2.1. According to Type

2.2.1.1. Quantitative: Continuous Data and Discrete Data
2.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data 

2.2.2. According to their Shape 

2.2.2.1. Numeric
2.2.2.2. Text: 
2.2.2.3. Logical

2.2.3. According to its Source

2.2.3.1. Primary
2.2.3.2. Secondary

2.3. Life Cycle of Data

2.3.1. Stages of the Cycle
2.3.2. Milestones of the Cycle
2.3.3. FAIR Principles

2.4. Initial Stages of the Cycle

2.4.1. Definition of Goals
2.4.2. Determination of Resource Requirements
2.4.3. Gantt Chart
2.4.4. Data Structure

2.5. Data Collection

2.5.1. Methodology of Data Collection
2.5.2. Data Collection Tools
2.5.3. Data Collection Channels

2.6. Data Cleaning

2.6.1. Phases of Data Cleansing
2.6.2. Data Quality
2.6.3. Data Manipulation (with R)

2.7. Data Analysis, Interpretation and Evaluation of Results

2.7.1. Statistical Measures
2.7.2. Relationship Indices
2.7.3. Data Mining

2.8. Data Warehouse (Datawarehouse)

2.8.1. Elements that Comprise it
2.8.2. Design
2.8.3. Aspects to Consider

2.9. Data Availability

2.9.1. Access
2.9.2. Uses
2.9.3. Security/Safety

2.10. Regulatory Aspects 

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Normative Aspects

Module 3. Data in Artificial Intelligence 

3.1. Data Science 

3.1.1. Data Science 
3.1.2. Advanced Tools for Data Scientists 

3.2. Data, Information and Knowledge 

3.2.1. Data, Information and Knowledge
3.2.2. Types of Data 
3.2.3. Data Sources 

3.3. From Data to Information

3.3.1. Data Analysis 
3.3.2. Types of Analysis 
3.3.3. Extraction of Information from a Dataset 

3.4. Extraction of Information Through Visualization 

3.4.1. Visualization as an Analysis Tool 
3.4.2. Visualization Methods
3.4.3. Visualization of a Data Set 

3.5. Data Quality 

3.5.1. Quality Data 
3.5.2. Data Cleaning
3.5.3. Basic Data Pre-Processing 

3.6. Dataset 

3.6.1. Dataset Enrichment 
3.6.2. The Curse of Dimensionality 
3.6.3. Modification of Our Data Set 

3.7. Unbalance

3.7.1. Classes of Unbalance 
3.7.2. Unbalance Mitigation Techniques 
3.7.3. Balancing a Dataset 

3.8. Unsupervised Models

3.8.1. Unsupervised Model 
3.8.2. Methods 
3.8.3. Classification with Unsupervised Models 

3.9. Supervised Models 

3.9.1. Supervised Model 
3.9.2. Methods 
3.9.3. Classification with Supervised Models 

3.10. Tools and Good Practices 

3.10.1. Good Practices for Data Scientists 
3.10.2. The Best Model
3.10.3. Useful Tools 

Module 4. Data Mining: Selection, Pre-Processing and Transformation 

4.1. Statistical Inference 

4.1.1. Descriptive Statistics vs. Statistical Inference 
4.1.2. Parametric Procedures 
4.1.3. Non-Parametric Procedures 

4.2. Exploratory Analysis 

4.2.1. Descriptive Analysis
4.2.2. Visualization 
4.2.3. Data Preparation 

4.3. Data Preparation 

4.3.1. Integration and Data Cleaning
4.3.2. Normalization of Data 
4.3.3. Transforming Attributes

4.4. Missing Values 

4.4.1. Treatment of Missing Values 
4.4.2. Maximum Likelihood Imputation Methods 
4.4.3. Missing Value Imputation Using Machine Learning 

4.5. Noise in the Data

4.5.1. Noise Classes and Attributes 
4.5.2. Noise Filtering
4.5.3. The Effect of Noise 

4.6. The Curse of Dimensionality 

4.6.1. Oversampling 
4.6.2. Undersampling 
4.6.3. Multidimensional Data Reduction 

4.7. From Continuous to Discrete Attributes 

4.7.1. Continuous Data Vs. Discreet Data 
4.7.2. Discretization Process 

4.8. The Data

4.8.1. Data Selection
4.8.2. Prospects and Selection Criteria 
4.8.3. Selection Methods

4.9. Instance Selection 

4.9.1. Methods for Instance Selection 
4.9.2. Prototype Selection 
4.9.3. Advanced Methods for Instance Selection 

4.10. Data Pre-Processing in Big Data Environments 

Module 5. Algorithm and Complexity in Artificial Intelligence 

5.1. Introduction to Algorithm Design Strategies 

5.1.1. Recursion 
5.1.2. Divide and Conquer 
5.1.3. Other Strategies 

5.2. Efficiency and Analysis of Algorithms 

5.2.1. Efficiency Measures 
5.2.2. Measuring the Size of the Input 
5.2.3. Measuring Execution Time 
5.2.4. Worst, Best and Average Case 
5.2.5. Asymptotic Notation 
5.2.6. Mathematical Analysis Criteria for Non-Recursive Algorithms 
5.2.7. Mathematical Analysis of Recursive Algorithms 
5.2.8. Empirical Analysis of Algorithms 

5.3. Sorting Algorithms 

5.3.1. Concept of Sorting 
5.3.2. Bubble Sorting 
5.3.3. Sorting by Selection 
5.3.4. Sorting by Insertion 
5.3.5. Merge Sort 
5.3.6. Quick Sort 

5.4. Algorithms with Trees 

5.4.1. Tree Concept 
5.4.2. Binary Trees 
5.4.3. Tree Paths 
5.4.4. Representing Expressions 
5.4.5. Ordered Binary Trees 
5.4.6. Balanced Binary Trees 

5.5. Algorithms Using Heaps 

5.5.1. Heaps 
5.5.2. The Heapsort Algorithm 
5.5.3. Priority Queues 

5.6. Graph Algorithms 

5.6.1. Representation 
5.6.2. Traversal in Width 
5.6.3. Depth Travel 
5.6.4. Topological Sorting 

5.7. Greedy Algorithms 

5.7.1. Greedy Strategy 
5.7.2. Elements of the Greedy Strategy 
5.7.3. Currency Exchange 
5.7.4. Traveler’s Problem 
5.7.5. Backpack Problem 

5.8. Minimal Path Finding 

5.8.1. The Minimum Path Problem 
5.8.2. Negative Arcs and Cycles 
5.8.3. Dijkstra's Algorithm 

5.9. Greedy Algorithms on Graphs 

5.9.1. The Minimum Covering Tree 
5.9.2. Prim's Algorithm 
5.9.3. Kruskal’s Algorithm 
5.9.4. Complexity Analysis 

5.10. Backtracking 

5.10.1. Backtracking 
5.10.2. Alternative Techniques 

Module 6. Intelligent Systems 

6.1. Agent Theory 

6.1.1. Concept History 
6.1.2. Agent Definition 
6.1.3. Agents in Artificial Intelligence 
6.1.4. Agents in Software Engineering 

6.2. Agent Architectures 

6.2.1. The Reasoning Process of an Agent 
6.2.2. Reactive Agents 
6.2.3. Deductive Agents 
6.2.4. Hybrid Agents 
6.2.5. Comparison 

6.3. Information and Knowledge 

6.3.1. Difference between Data, Information and Knowledge 
6.3.2. Data Quality Assessment 
6.3.3. Data Collection Methods 
6.3.4. Information Acquisition Methods 
6.3.5. Knowledge Acquisition Methods 

6.4. Knowledge Representation 

6.4.1. The Importance of Knowledge Representation 
6.4.2. Definition of Knowledge Representation According to Roles 
6.4.3. Knowledge Representation Features 

6.5. Ontologies 

6.5.1. Introduction to Metadata 
6.5.2. Philosophical Concept of Ontology 
6.5.3. Computing Concept of Ontology 
6.5.4. Domain Ontologies and Higher-Level Ontologies 
6.5.5. How to Build an Ontology? 

6.6. Ontology Languages and Ontology Creation Software 

6.6.1. Triple RDF, Turtle and N 
6.6.2. RDF Schema 
6.6.3. OWL 
6.6.4. SPARQL 
6.6.5. Introduction to Ontology Creation Tools 
6.6.6. Installing and Using Protégé 

6.7. Semantic Web 

6.7.1. Current and Future Status of the Semantic Web 
6.7.2. Semantic Web Applications 

6.8. Other Knowledge Representation Models 

6.8.1. Vocabulary 
6.8.2. Global Vision 
6.8.3. Taxonomy 
6.8.4. Thesauri 
6.8.5. Folksonomy 
6.8.6. Comparison 
6.8.7. Mind Maps 

6.9. Knowledge Representation Assessment and Integration 

6.9.1. Zero-Order Logic 
6.9.2. First-Order Logic 
6.9.3. Descriptive Logic 
6.9.4. Relationship between Different Types of Logic 
6.9.5. Prolog: Programming Based on First-Order Logic 

6.10. Semantic Reasoners, Knowledge-Based Systems and Expert Systems 

6.10.1. Concept of Reasoner 
6.10.2. Reasoner Applications 
6.10.3. Knowledge-Based Systems 
6.10.4. MYCIN: History of Expert Systems 
6.10.5. Expert Systems Elements and Architecture 
6.10.6. Creating Expert Systems 

Module 7. Machine Learning and Data Mining 

7.1. Introduction to Knowledge Discovery Processes and Basic Concepts of Machine Learning 

7.1.1. Key Concepts of Knowledge Discovery Processes 
7.1.2. Historical Perspective of Knowledge Discovery Processes 
7.1.3. Stages of the Knowledge Discovery Processes 
7.1.4. Techniques Used in Knowledge Discovery Processes 
7.1.5. Characteristics of Good Machine Learning Models 
7.1.6. Types of Machine Learning Information 
7.1.7. Basic Learning Concepts 
7.1.8. Basic Concepts of Unsupervised Learning 

7.2. Data Exploration and Pre-processing 

7.2.1. Data Processing 
7.2.2. Data Processing in the Data Analysis Flow 
7.2.3. Types of Data 
7.2.4. Data Transformations 
7.2.5. Visualization and Exploration of Continuous Variables 
7.2.6. Visualization and Exploration of Categorical Variables 
7.2.7. Correlation Measures 
7.2.8. Most Common Graphic Representations 
7.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction 

7.3. Decision Trees 

7.3.1. ID Algorithm 
7.3.2. Algorithm C 
7.3.3. Overtraining and Pruning 
7.3.4. Analysis of Results 

7.4. Evaluation of Classifiers 

7.4.1. Confusion Matrixes 
7.4.2. Numerical Evaluation Matrixes 
7.4.3. Kappa Statistic 
7.4.4. ROC Curves 

7.5. Classification Rules 

7.5.1. Rule Evaluation Measures 
7.5.2. Introduction to Graphic Representation 
7.5.3. Sequential Overlay Algorithm 

7.6. Neural Networks 

7.6.1. Basic Concepts 
7.6.2. Simple Neural Networks 
7.6.3. Backpropagation Algorithm 
7.6.4. Introduction to Recurrent Neural Networks 

7.7. Bayesian Methods 

7.7.1. Basic Probability Concepts 
7.7.2. Bayes' Theorem 
7.7.3. Naive Bayes 
7.7.4. Introduction to Bayesian Networks 

7.8. Regression and Continuous Response Models 

7.8.1. Simple Linear Regression 
7.8.2. Multiple Linear Regression 
7.8.3. Logistic Regression 
7.8.4. Regression Trees 
7.8.5. Introduction to Support Vector Machines (SVM) 
7.8.6. Goodness-of-Fit Measures 

7.9. Clustering 

7.9.1. Basic Concepts 
7.9.2. Hierarchical Clustering 
7.9.3. Probabilistic Methods 
7.9.4. EM Algorithm 
7.9.5. B-Cubed Method 
7.9.6. Implicit Methods 

7.10. Text Mining and Natural Language Processing (NLP) 

7.10.1. Basic Concepts 
7.10.2. Corpus Creation 
7.10.3. Descriptive Analysis 
7.10.4. Introduction to Feelings Analysis 

Module 8. Neural networks, the basis of Deep Learning 

8.1. Deep Learning 

8.1.1. Types of Deep Learning 
8.1.2. Applications of Deep Learning 
8.1.3. Advantages and Disadvantages of Deep Learning 

8.2. Surgery 

8.2.1. Sum 
8.2.2. Product 
8.2.3. Transfer 

8.3. Layers 

8.3.1. Input Layer 
8.3.2. Cloak 
8.3.3. Output layer 

8.4. Union of Layers and Operations 

8.4.1. Architecture Design 
8.4.2. Connection between Layers 
8.4.3. Forward Propagation 

8.5. Construction of the First Neural Network 

8.5.1. Network Design 
8.5.2. Establish the Weights 
8.5.3. Network Training 

8.6. Trainer and Optimizer 

8.6.1. Optimizer Selection 
8.6.2. Establishment of a Loss Function 
8.6.3. Establishing a Metric 

8.7. Application of the Principles of Neural Networks 

8.7.1. Activation Functions 
8.7.2. Backward Propagation 
8.7.3. Parameter Adjustment 

8.8. From Biological to Artificial Neurons 

8.8.1. Functioning of a Biological Neuron 
8.8.2. Transfer of Knowledge to Artificial Neurons 
8.8.3. Establish Relations between the Two 

8.9. Implementation of MLP (Multilayer Perceptron) with Keras 

8.9.1. Definition of the Network Structure 
8.9.2. Model Compilation 
8.9.3. Model Training 

8.10. Fine Tuning  Hyperparameters of Neural Networks 

8.10.1. Selection of the Activation Function 
8.10.2. Set the Learning Rate 
8.10.3. Adjustment of Weights 

Module 9. Deep Neural Networks Training 

9.1. Gradient Problems 

9.1.1. Gradient Optimization Techniques 
9.1.2. Stochastic Gradients 
9.1.3. Weight Initialization Techniques 

9.2. Reuse of Pre-Trained Layers 

9.2.1. Learning Transfer Training 
9.2.2. Feature Extraction 
9.2.3. Deep Learning 

9.3. Optimizers 

9.3.1. Stochastic Gradient Descent Optimizers 
9.3.2. Optimizers Adam and RMSprop 
9.3.3. Moment Optimizers 

9.4. Learning Rate Programming 

9.4.1. Automatic Learning Rate Control 
9.4.2. Learning Cycles 
9.4.3. Smoothing Terms 

9.5. Overfitting 

9.5.1. Cross Validation 
9.5.2. Regularization 
9.5.3. Evaluation Metrics 

9.6. Practical Guidelines 

9.6.1. Model Design 
9.6.2. Selection of Metrics and Evaluation Parameters 
9.6.3. Hypothesis Testing 

9.7. Transfer Learning 

9.7.1. Learning Transfer Training 
9.7.2. Feature Extraction 
9.7.3. Deep Learning 

9.8. Data Augmentation 

9.8.1. Image Transformations 
9.8.2. Synthetic Data Generation 
9.8.3. Text Transformation 

9.9. Practical Application of Transfer Learning 

9.9.1. Learning Transfer Training 
9.9.2. Feature Extraction 
9.9.3. Deep Learning 

9.10. Regularization 

9.10.1. L and L 
9.10.2. Regularization by Maximum Entropy 
9.10.3. Dropout 

Module 10. Model Customization and Training with TensorFlow 

10.1. TensorFlow 

10.1.1. Use of the TensorFlow Library 
10.1.2. Model Training with TensorFlow 
10.1.3. Operations with Graphics in   TensorFlow 

10.2. TensorFlow and NumPy 

10.2.1. NumPy Computing Environment for TensorFlow 
10.2.2. Using NumPy Arrays with TensorFlow 
10.2.3. NumPy Operations for TensorFlowGraphics 

10.3. Model Customization and Training Algorithms 

10.3.1. Building Custom Models with TensorFlow 
10.3.2. Management of Training Parameters 
10.3.3. Use of Optimization Techniques for Training 

10.4. TensorFlow Features and Graphics 

10.4.1. Functions with  TensorFlow 
10.4.2. Use of Graphs for Model Training 
10.4.3. Graphics Optimization with TensorFlowOperations 

10.5. Loading and Preprocessing Data with TensorFlow 

10.5.1. Loading Data Sets with TensorFlow 
10.5.2. Preprocessing Data with TensorFlow 
10.5.3. Using  TensorFlowTools for Data Manipulation 

10.6. The API tfdata 

10.6.1. Using the tfdataAPI for Data Processing 
10.6.2. Construction of Data Streams with tfdata 
10.6.3. Using thetfdata API for Model Training 

10.7. The TFRecord Format 

10.7.1. Using the  TFRecordAPI for Data Serialization 
10.7.2. TFRecord File Upload with TensorFlow 
10.7.3. Using  TFRecord Files for Model Training 

10.8. Keras Preprocessing Layers 

10.8.1. Using the Keras Preprocessing API 
10.8.2. Preprocessing Pipelined Construction with Keras 
10.8.3. Using the Keras Preprocessing API for Model Training 

10.9. The TensorFlow Datasets Project 

10.9.1. Using TensorFlow Datasets  for Data Loading 
10.9.2. Preprocessing Data with TensorFlow Datasets 
10.9.3. Using  TensorFlow Datasets   for Model Training 

10.10. Building a Deep Learning App with TensorFlow 

10.10.1. Practical Application 
10.10.2. Building a Deep Learning App with TensorFlow 
10.10.3. Model Training with TensorFlow 
10.10.4. Use of the Application for the Prediction of Results 

Module 11. Deep Computer Vision with Convolutional Neural Networks 

11.1. The Visual Cortex Architecture 

11.1.1. Functions of the Visual Cortex 
11.1.2. Theories of Computational Vision 
11.1.3. Models of Image Processing 

11.2. Convolutional Layers 

11.2.1. Reuse of Weights in Convolution 
11.2.2. Convolution D 
11.2.3. Activation Functions 

11.3. Grouping Layers and Implementation of Grouping Layers with Keras 

11.3.1. Pooling and Striding 
11.3.2. Flattening 
11.3.3. Types of Pooling 

11.4. CNN Architecture 

11.4.1. VGG Architecture 
11.4.2. AlexNet Architecture 
11.4.3. Architecture ResNet 

11.5. Implementing a CNN ResNet- using Keras 

11.5.1. Weight Initialization 
11.5.2. Input Layer Definition 
11.5.3. Output Definition 

11.6. Use of Pre-trained Keras Models 

11.6.1. Characteristics of Pre-trained Models 
11.6.2. Uses of Pre-trained Models 
11.6.3. Advantages of Pre-trained Models 

11.7. Pre-trained Models for Transfer Learning 

11.7.1. Transfer Learning 
11.7.2. Transfer Learning Process 
11.7.3. Advantages of Transfer Learning 

11.8. Deep Computer Vision Classification and Localization 

11.8.1. Image Classification 
11.8.2. Localization of Objects in Images 
11.8.3. Object Detection 

11.9. Object Detection and Object Tracking 

11.9.1. Object Detection Methods 
11.9.2. Object Tracking Algorithms 
11.9.3. Tracking and Localization Techniques 

11.10. Semantic Segmentation 

11.10.1. Deep Learning for Semantic Segmentation 
11.10.2. Edge Detection 
11.10.3. Rule-based Segmentation Methods 

Module 12. Natural Language Processing (NLP) with Natural Recurrent Networks (NRN) and Attention

12.1. Text Generation Using RNN 

12.1.1. Training an RNN for Text Generation 
12.1.2. Natural Language Generation with RNN 
12.1.3. Text Generation Applications with RNN 

12.2. Training Data Set Creation 

12.2.1. Preparation of the Data for Training an RNN 
12.2.2. Storage of the Training Dataset 
12.2.3. Data Cleaning and Transformation 
12.2.4. Sentiment Analysis 

12.3. Classification of Opinions with RNN 

12.3.1. Detection of Themes in Comments 
12.3.2. Sentiment Analysis with Deep Learning Algorithms 

12.4. Encoder-decoder Network for Neural Machine Translation

12.4.1. Training an RNN for Machine Translation 
12.4.2. Use of an Encoder-decoder Network for Machine Translation 
12.4.3. Improving the Accuracy of Machine Translation with RNNs 

12.5. Attention Mechanisms 

12.5.1. Application of Care Mechanisms in RNN 
12.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models 
12.5.3. Advantages of Attention Mechanisms in Neural Networks 

12.6. Transformer Models 

12.6.1. Using TransformerModels for Natural Language Processing 
12.6.2. Application of Transformer Models for Vision 
12.6.3. Advantages of Transformer Models 

12.7. Transformers for Vision 

12.7.1. Use of Transformer Models for Vision 
12.7.2. Image Data Preprocessing 
12.7.3. Training a Transformers Model for Vision 

12.8. Hugging Face’s TransformersLibrary 

12.8.1. Using the Hugging Face’s TransformersLibrary 
12.8.2. Hugging Face’s TransformersLibrary App 
12.8.3. Advantages of  Hugging Face’s Transformers Library 

12.9. Other Transformers Libraries. Comparison 

12.9.1. Comparison between different TransformersLibraries 
12.9.2. Use of the other Transformers Libraries 
12.9.3. Advantages of the other Transformers Libraries 

12.10. Development of an NLP Application with RNN and Attention. Practical Application 

12.10.1. Development of a Natural Language Processing Application with RNN and Attention
12.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application 
12.10.3. Evaluation of the Practical Application 

Module 13. Autoencoders, GANs, and Diffusion Models 

13.1. Representation of Efficient Data 

13.1.1. Dimensionality Reduction 
13.1.2. Deep Learning 
13.1.3. Compact Representations 

13.2. PCA Realization with an Incomplete Linear Automatic Encoder

13.2.1. Training Process 
13.2.2. Implementation in Python 
13.2.3. Use of Test Data 

13.3. Stacked Automatic Encoders 

13.3.1. Deep Neural Networks 
13.3.2. Construction of Coding Architectures 
13.3.3. Use of Regularization 

13.4. Convolutional Autoencoders 

13.4.1. Design of Convolutional Models 
13.4.2. Convolutional Model Training 
13.4.3. Results Evaluation 

13.5. Automatic Encoder Denoising 

13.5.1. Application of Filters 
13.5.2. Design of Coding Models 
13.5.3. Use of Regularization Techniques 

13.6. Sparse Automatic Encoders 

13.6.1. Increasing Coding Efficiency 
13.6.2. Minimizing the Number of Parameters 
13.6.3. Using Regularization Techniques 

13.7. Variational Automatic Encoders 

13.7.1. Use of Variational Optimization 
13.7.2. Unsupervised Deep Learning 
13.7.3. Deep Latent Representations 

13.8. Generation of Fashion MNIST Images 

13.8.1. Pattern Recognition 
13.8.2. Image Generation 
13.8.3. Deep Neural Networks Training 

13.9. Generative Adversarial Networks and Diffusion Models 

13.9.1. Content Generation from Images 
13.9.2. Modeling of Data Distributions 
13.9.3. Use of Adversarial Networks 

13.10. Implementation of the Models 

13.10.1. Practical Application 
13.10.2. Implementation of the Models 
13.10.3. Use of Real Data 
13.10.4. Results Evaluation 

Module 14. Bio-Inspired Computing

14.1. Introduction to Bio-Inspired Computing 

14.1.1. Introduction to Bio-Inspired Computing 

14.2. Social Adaptation Algorithms 

14.2.1. Bio-Inspired Computation Based on Ant Colonies 
14.2.2. Variants of Ant Colony Algorithms 
14.2.3. Particle Cloud Computing 

14.3. Genetic Algorithms 

14.3.1. General Structure 
14.3.2. Implementations of the Major Operators 

14.4. Space Exploration-Exploitation Strategies for Genetic Algorithms 

14.4.1. CHC Algorithm 
14.4.2. Multimodal Problems 

14.5. Evolutionary Computing Models (I) 

14.5.1. Evolutionary Strategies 
14.5.2. Evolutionary Programming 
14.5.3. Algorithms Based on Differential Evolution 

14.6. Evolutionary Computation Models (II) 

14.6.1. Evolutionary Models Based on Estimation of Distributions (EDA) 
14.6.2. Genetic Programming 

14.7. Evolutionary Programming Applied to Learning Problems 

14.7.1. Rules-Based Learning 
14.7.2. Evolutionary Methods in Instance Selection Problems 

14.8. Multi-Objective Problems 

14.8.1. Concept of Dominance 
14.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems 

14.9. Neural Networks (I) 

14.9.1. Introduction to Neural Networks 
14.9.2. Practical Example with Neural Networks 

14.10. Neural Networks (II) 

14.10.1. Use Cases of Neural Networks in Medical Research 
14.10.2. Use Cases of Neural Networks in Economics 
14.10.3. Use Cases of Neural Networks in Artificial Vision 

Module 15. Artificial Intelligence: Strategies and Applications

15.1. Financial Services

15.1.1. The Implications of Artificial Intelligence (AI) in Financial Services.  Opportunities and Challenges
15.1.2. Case Uses
15.1.3. Potential Risks Related to the Use of AI
15.1.4. Potential Future Developments/uses of AI

15.2. Implications of Artificial Intelligence in the Healthcare Service

15.2.1. Implications of AI in the Healthcare Sector. Opportunities and Challenges
15.2.2. Case Uses

15.3. Risks Related to the Use of AI in the Health Service

15.3.1. Potential Risks Related to the Use of AI
15.3.2. Potential Future Developments/uses of AI

15.4. Retail

15.4.1. Implications of AI in Retail. Opportunities and Challenges
15.4.2. Case Uses
15.4.3. Potential Risks Related to the Use of AI
15.4.4. Potential Future Developments/uses of AI

15.5. Industry

15.5.1. Implications of AI in Industry. Opportunities and Challenges
15.5.2. Case Uses

15.6. Potential risks related to the use of AI in industry

15.6.1. Case Uses
15.6.2. Potential Risks Related to the Use of AI
15.6.3. Potential Future Developments/uses of AI

15.7. Public Administration

15.7.1. AI implications for public administration. Opportunities and Challenges
15.7.2. Case Uses
15.7.3. Potential Risks Related to the Use of AI
15.7.4. Potential Future Developments/uses of AI

15.8. Educational

15.8.1. AI Implications for Education. Opportunities and Challenges
15.8.2. Case Uses
15.8.3. Potential Risks Related to the Use of AI
15.8.4. Potential Future Developments/uses of AI

15.9. Forestry and Agriculture

15.9.1. Implications of AI in Forestry and Agriculture. Opportunities and Challenges
15.9.2. Case Uses
15.9.3. Potential Risks Related to the Use of AI
15.9.4. Potential Future Developments/uses of AI

15.10 Human Resources

15.10.1. Implications of AI for Human Resources Opportunities and Challenges
15.10.2. Case Uses
15.10.3. Potential Risks Related to the Use of AI
15.10.4. Potential Future Developments/uses of AI

Module 16. Improving Software Development Productivity with AI 

16.1. Prepare a Suitable Development Environment 

16.1.1. Selection of Essential Tools for AI Development
16.1.2. Configuration of the Chosen Tools 
16.1.3. Implementation of CI/CD Pipelines Adapted to AI Projects 
16.1.4. Efficient Management of Dependencies and Versions in Development Environments 

16.2. Essential AI Extensions for Visual Studio Code

16.2.1. Exploring and Selecting AI Extensions for Visual Studio Code 
16.2.2. Integration of Static and Dynamic Analysis Tools in the SDI 
16.2.3. Automation of Repetitive Tasks with Specific Extensions 
16.2.4. Customization of the Development Environment to Improve Efficiency 

16.3. No-code  Design of User Interfaces with AI Elements 

16.3.1. No-code  Design Principles and Their Application to User Interfaces 
16.3.2. Incorporation of AI Elements in the Visual Design of Interfaces 
16.3.3. Tools and Platforms for No-code Creation of Intelligent Interfaces 
16.3.4. Evaluation and Continuous Improvement of No-code Interfaces with AI 

16.4. Code Optimization using ChatGPT

16.4.1. Identifying Duplicate Code
16.4.2. Refactor 
16.4.3. Create Readable Code
16.4.4. Understanding What Code Does
16.4.5. Improving Variable and Function Names
16.4.6. Automatic Documentation Creation

16.5. Repository Management with AI

16.5.1. Automation of Version Control Processes with AI Techniques 
16.5.2. Conflict Detection and Automatic Resolution in Collaborative Environments
16.5.3. Predictive Analysis of Changes and Trends in Code Repositories 
16.5.4. Improved Organization and Categorization of Repositories using AI 

16.6. Integration of AI in Database Management

16.6.1. Query and Performance Optimization Using AI Techniques 
16.6.2. Predictive Analysis of Database Access Patterns 
16.6.3. Implementation of Recommender Systems to Optimize Database Structure 
16.6.4. Monitoring and Proactive Detection of Potential Problems in Databases 

16.7. Fault Finding and Creation of Unit Tests with AI

16.7.1. Automatic Generation of Test Cases Using AI Techniques 
16.7.2. Early Detection of Vulnerabilities and Bugs using Static Analysis with AI 
16.7.3. Improving Test Coverage by Identifying Critical Areas with AI 

16.8. Pair Programming with GitHub Copilot

16.8.1. Integration and Effective Use of GitHub Copilot in Pair ProgrammingSessions 
16.8.2. Integration Improvements in Communication and Collaboration between Developers with GitHub Copilot 
16.8.3. Integration Strategies for Making the Most of Code Hints Generated by GitHub Copilot 
16.8.4. Integration Case Studies and Best Practices in AI-assisted Pair Programming 

16.9. Automatic Translation between Programming Languages

16.9.1. Programming Language Specific Machine Translation Tools and Services 
16.9.2. Adapting Machine Translation Algorithms to Development Contexts 
16.9.3. Improving Interoperability between Different Languages by Machine Translation 
16.9.4. Assessing and Mitigating Potential Challenges and Limitations of Machine Translation

16.10. Recommended AI Tools to Improve Productivity 

16.10.1. Comparative Analysis of AI Tools for Software Development 
16.10.2. Integration of AI Tools in Workflows
16.10.3. Automation of Routine Tasks with AI Tools
16.10.4. Evaluating and Selecting Tools Based on Context and Project Requirements 

Module 17. Software Architecture with AI

17.1. Optimization and Performance Management in AI Tools

17.1.1. Performance Analysis and Profiling in AI Tools 
17.1.2. Algorithm Optimization Strategies and AI Models 
17.1.3. Implementation of Caching and Parallelization Techniques to Improve Performance 
17.1.4. Tools and Methodologies for Continuous Real-Time Performance Monitoring 

17.2. Scalability in AI Applications

17.2.1. Design of Scalable Architectures for AI Applications 
17.2.2. Implementation of Partitioning and Load Distribution Techniques 
17.2.3. Workflow and Workload Management for Scalable Systems 
17.2.4. Strategies for Horizontal and Vertical Expansion in Variable Demand Environments 

17.3. Maintainability of AI Applications

17.3.1. Design Principles to Facilitate Maintainability in AI Projects 
17.3.2. Specific Documentation Strategies for AI Models and Algorithms 
17.3.3. Implementation of Unit and Integration Tests to Facilitate Maintenance 
17.3.4. Methods for Refactoring and Continuous Improvement in Systems with AI Components

17.4. Design of Large-Scale Systems

17.4.1. Architectural Principles for the Design of Large-Scale Systems 
17.4.2. Decomposition of Complex Systems into Microservices 
17.4.3. Implementation of Specific Design Patterns for Distributed Systems 
17.4.4. Strategies for Complexity Management in Large-Scale Architectures with AI Components 

17.5. Large-Scale Data Warehousing for AI Tools

17.5.1. Selection of Scalable Data Warehousing Technologies 
17.5.2. Designing Database Schemas for Efficient Management of Large Data Volumes 
17.5.3. Partitioning and Replication Strategies in Massive Data Storage Environments 
17.5.4. Implementation of Data Management Systems to Ensure Integrity and Availability in AI Projects  

17.6. Data Structures with AI

17.6.1. Adaptation of Classical Data Structures for Use in AI Algorithms 
17.6.2. Designing and Optimizing Specific Data Structures for Machine Learning Models 
17.6.3. Integration of Efficient Data Structures in Data Intensive Systems 
17.6.4. Strategies for Real-Time Data Manipulation and Storage in AI Data Structures

17.7. Programming Algorithms for AI Products

17.7.1. Development and Implementation of Application-Specific Algorithms for AI Applications 
17.7.2. Algorithm Selection Strategies according to Problem Type and Product Requirements 
17.7.3. Adaptation of Classical Algorithms for Integration into Artificial Intelligence Systems 
17.7.4. Evaluation and Comparison of Performance between Different Algorithms in AI Development Contexts

17.8. Design Patterns for AI Development

17.8.1. Identification and Application of Common Design Patterns in Projects with AI Components 
17.8.2. Development of Specific Patterns for the Integration of Models and Algorithms into Existing Systems 
17.8.3. Pattern Implementation Strategies for Improving Reusability and Maintainability in AI Projects 
17.8.4. Case Studies and Best Practices in the Application of Design Patterns in AI Architectures 

17.9. Implementation of Clean Architecture

17.9.1. Fundamental Principles and Concepts of Clean Architecture
17.9.2. Adaptation of Clean Architecture to Projects with AI Components 
17.9.3. Implementation of Layers and Dependencies in Systems with Clean Architecture 
17.9.4. Benefits and Challenges of Implementing Clean Architecture  in AI Software Development 

17.10. Secure Software Development in Web Applications with AI

17.10.1. Principles of Security in Software Development with AI Components 
17.10.2. Identifying and Mitigating Potential Vulnerabilities in AI Models and Algorithms 
17.10.3. Implementation of Secure Development Practices in Web Applications with Artificial Intelligence Functionalities 
17.10.4. Strategies for the Protection of Sensitive Data and Prevention of Attacks in AI Projects 

Module 18. Web Projects with AI

18.1. Preparation of the Working Environment for Web Development with AI

18.1.1. Configuration of Web Development Environments for Projects with Artificial Intelligence 
18.1.2. Selection and Preparation of Essential Tools for AI  Web Development 
18.1.3. Integration of Specific Libraries and Frameworks for Web Projects with Artificial Intelligence 
18.1.4. Implementation of Best Practices in the Configuration of Collaborative Development Environments 

18.2. Workspace Creation for AI Projects

18.2.1. Effective Design and Organization of Workspaces for Web Projects with Artificial Intelligence Components
18.2.2. Use of Project Management and Version Control Tools in the Workspace 
18.2.3. Strategies for Efficient Collaboration and Communication in the Development Team 
18.2.4. Adaptation of the Workspace  to the Specific Needs of AI Web Projects 

18.3. Design Patterns in AI Products

18.3.1. Identification and Application of Common Design Patterns in User Interface with Artificial Intelligence Components 
18.3.2. Development of Specific Patterns to Improve User Experience in Web Projects with AI 
18.3.3. Integration of Design Patterns in the Overall Architecture of AI Web Projects 
18.3.4. Evaluation and Selection of Adequate Design Patterns according to the Project Context 

18.4. Frontend Development with AI

18.4.1. Integration of AI Models into the Presentation Layer of Web Projects
18.4.2. Development of Adaptive User Interfaces with Artificial Intelligence Elements 
18.4.3. Implementation of Natural Language Processing (NLP) Functionalities in the Frontend 
18.4.4. Strategies for Performance Optimization in Frontend Development with AI

18.5. Database Creation

18.5.1. Selection of Database Technologies for Web Projects with Artificial Intelligence 
18.5.2. Design of Database Schemas for Storing and Managing AI-Related Data 
18.5.3. Implementation of Efficient Storage Systems for Large Volumes of Data Generated by AI Models 
18.5.4. Strategies for the Security and Protection of Sensitive Data in AI Web Project Databases 

18.6. Back-End Development with AI

18.6.1. Integration of AI Services and Models in the Backend Business Logic 
18.6.2. Development of Specific APIs and Endpoints for Communication between the Frontend and AI Components 
18.6.3. Implementation of Data Processing and Decision Making Logic in the Backend with Artificial Intelligence 
18.6.4. Strategies for Scalability and Performance in the Backend Development of Web Projects with AI

18.7. Optimizing Your Web Deployment Process

18.7.1. Automating Web Project Build and Deployment Processes with AI 
18.7.2. Implementing CI/CD Pipelines Tailored to Web Applications with Artificial Intelligence Components 
18.7.3. Strategies for Efficient Release and Upgrade Management in Continuous Deployments 
18.7.4. Post-Deployment Monitoring and Analysis for Continuous Process Improvement

18.8. AI in Cloud Computing

18.8.1. Integration of Artificial Intelligence Services in Cloud Computing Platforms 
18.8.2. Development of Scalable and Distributed Solutions using Cloud Services with AI Capabilities 
18.8.3. Strategies for Efficient Resource and Cost Management in Cloud Environments with AI-enabled Web Applications 
18.8.4. Evaluation and Comparison of Cloud Service Providers for AI-enabled Web Projects

18.9. Creating an AI-enabled Project for LAMP Environments

18.9.1. Adaptation of Web Projects based on the LAMP Stack to include Artificial Intelligence Components 
18.9.2. Integration of AI-specific Libraries and Frameworks in LAMP Environments 
18.9.3. Development of AI Functionalities Complementing the Traditional LAMP Architecture
18.9.4. Strategies for Optimization and Maintenance in Web Projects with AI in LAMP Environments

18.10. Creating an AI-enabled Project for MEVN Environments

18.10.1. Integration of MEVN Stack Technologies and Tools with AI Components 
18.10.2. Development of Modern and Scalable Web Applications in MEVN Environments with AI Capabilities 
18.10.3. Implementation of Data Processing and Machine Learning functionalities in MEVN projects 
18.10.4. Strategies for Imrpoving Performance and Security Enhancement of AI-enabled Web Applications in MEVN Environments 

Module 19. AI-enabled Mobile Applications   

19.1. Preparation of Working Environment for Mobile Development with AI

19.1.1.  Configuration of Mobile Development Environments for Projects with Artificial Intelligence
19.1.2. Selection and Preparation of Specific Tools for  Mobile Application Development with AI 
19.1.3. Integration of AI Libraries and Frameworks in Mobile  Development Environments 
19.1.4. Configuration of Emulators and Real Devices for Testing Mobile Applications with AI Components 

19.2. Creating a Workspace with GitHub Copilot

19.2.1. Integration of GitHub Copilot in Mobile Development Environments 
19.2.2. Effective Use of GitHub Copilot for Code Generation in AI Projects 
19.2.3. Strategies for Developer Collaboration when using GitHub Copilot in the Workspace 
19.2.4. Best Practices and Limitations in the Use of GitHub Copilot in Mobile Application Development with AI 

19.3. Firebase Configuration

19.3.1. Initial Configuration of a Firebase Project for Mobile Development 
19.3.2. Firebase Integration in Mobile Applications with Artificial Intelligence Functionalities 
19.3.3. Use of Firebase Services as a Database, Authentication and Notifications in AI Projects 
19.3.4. Strategies for Real-Time Data and Event Management in Firebase-enabled Mobile Applications

19.4. Concepts of Clean Architecture, DataSources, Repositories

19.4.1. Fundamental Principles of Clean Architecture in Mobile Development with AI 
19.4.2. Implementation of DataSources and Repositories Layers in Clean Architectures
19.4.3. Design and Structuring of Components in Mobile Projects with a Focus on Clean Architecture 
19.4.4. Benefits and Challenges of Implementing Clean Architecture in Mobile Applications with AI

19.5. Authentication Screen Creation

19.5.1. Design and Development of User Interfaces for Authentication Screens in Mobile Applications with AI 
19.5.2. Integration of Authentication Services with Firebase in the Login Screen
19.5.3. Use of Security and Data Protection Techniques in the Authentication Screen  
19.5.4. Personalization and Customization of the User Experience on the Authentication Screen 

19.6. Dashboardand Navigation Creation

19.6.1. Dashboard Design and Development with Artificial Intelligence Elements 
19.6.2. Implementation of Efficient Navigation Systems in Mobile Applications with AI 
19.6.3. Integration of AI Functionalities in the Dashboard to Improve User Experience

19.7. Creation of Listing Screen

19.7.1. Development of User Interfaces for AI-enabled Mobile Application Listing Displays 
19.7.2. Integration of Recommendation and Filtering Algorithms in the Listing Screen 
19.7.3. Use of Design Patterns for Effective Data Presentation in the Listing Screen 
19.7.4. Strategies for Efficient Real-Time Data Loading in the Listing Screen  

19.8. Creating Detail Screen

19.8.1. Design and Development of Detailed User Interfaces for the Presentation of Specific Information
19.8.2. Integration of AI Functionalities to Enrich the Detail Screen 
19.8.3. Implementation of Interactions and Animations in the Detail Screen 
19.8.4. Strategies for Performance Optimization in Loading and Detail Display in AI-enabled Mobile Applications  

19.9. Creating Settings Screen

19.9.1. Development of User Interfaces for Configuration and Settings in AI-enabled Mobile Applications 
19.9.2. Integration of Custom Settings Related to AI Components 
19.9.3. Implementing Customization Options and Preferences in the Configuration Screen 
19.9.4. Strategies for Usability and Clarity in the Presentation of Options in the Settings Screen 

19.10. Creating Icons, Splash  and Graphic Resources for Your App with AI     

19.10.1. Designing and Creating Attractive Icons to Represent Your AI Mobile Application 
19.10.2. Developing Splash Screens with Impressive Visual Elements 
19.10.3. Selection and Adaptation of Graphic Resources to Enhance the Aesthetics of the Mobile Application 
19.10.4. Strategies for Consistency and Visual Branding in AI Application Graphics Elements 

Module 20. AI for QA Testing 

20.1. Testing Life Cycle

20.1.1. Description and Understanding of the Testing Life Cycle in Software Development
20.1.2. Phases of the  Testing Life Cycle and Its Importance for Quality Assurance 
20.1.3. Integration of Artificial Intelligence in Different Stages of the Testing Life Cycle 
20.1.4. Strategies for Continuous Improvement of the Testing Life Cycle using AI 

20.2. Test Cases and Bug Detection

20.2.1. Effective Test Case Design and Writing in the QA Testing Context 
20.2.2. Identification of Bugs and Errors during Test Case Execution 
20.2.3. Application of Early Bug Detection Techniques using Static Analysis 
20.2.4. Use of Artificial intelligence Tools for the Automatic Identification of Bugs in Test Cases

20.3. Types of Testing

20.3.1. Exploration of Different Types of Testing in the QA Domain 
20.3.2. Unit, Integration, Functional, and Acceptance Testing: Characteristics and Applications 
20.3.3. Strategies for the Selection and Appropriate Combination of Testing Types in AI Projects 
20.3.4. Adaptation of Conventional Testing Types to Projects with Artificial Intelligence Components 

20.4. Creating a Test Plan

20.4.1. Designing and Structuring a Comprehensive Test Plan 
20.4.2. Identifying Requirements and Test Scenarios in AI Projects 
20.4.3. Strategies for Manual and Automated Test Planning 
20.4.4. Continuous Evaluation and Adjustment of the Test Plan as the Project Develops 

20.5. AI Bug Detection and Reporting

20.5.1. Implementation of Automatic Bug Detection Techniques using Machine Learning Algorithms
20.5.2. Use of Artificial Intelligence Tools for Dynamic Code Analysis in Search of Possible Errors
20.5.3. Strategies for Automatic Generation of Detailed Reports on AI-Detected Bugs 
20.5.4. Effective Collaboration between Development and QA Teams in the Management of AI-Detected Bugs

20.6. Creation of Automated Testing with AI

20.6.1. Development of Automated Test Scripts for Projects with AI Components 
20.6.2. Integration of AI-based Test Automation Tools
20.6.3. Use of Machine Learning Algorithms for Dynamic Generation of Automated Test Cases 
20.6.4. Strategies for Efficient Execution and Maintenance of Automated Test Cases in AI Projects

20.7. API Testing

20.7.1. Fundamental Concepts of API Testing and Its Importance in QA 
20.7.2. Development of Tests for API Verification in Environments with Artificial Intelligence Components 
20.7.3. Strategies for Data and Results Validation in API Testing with AI 
20.7.4. Use of Specific Tools for API Testing in Artificial Intelligence Projects

20.8. AI Tools for Web Testing

20.8.1. Exploring Artificial Intelligence Tools for Test Automation in Web Environments  
20.8.2. Integration of Element Recognition and Visual Analysis Technologies in Web Testing 
20.8.3. Strategies for Automatic Detection of Changes and Performance Problems in Web Applications using AI 
20.8.4. Evaluation of Specific Tools for Improving Efficiency in Web Testing with AI

20.9. Mobile Testing  Using AI

20.9.1. Development of Testing Strategies for Mobile Applications with Artificial Intelligence Components 
20.9.2. Integration of Specific Testing Tools for AI-based Mobile Platforms 
20.9.3. Use of Machine Learning Algorithms for the Detection of Performance Problems in Mobile Apps 
20.9.4. Strategies for the Validation of Specific Mobile Application Interfaces and Functions using AI 

20.10. QA Tools with AI

20.10.1. Exploration of QA Tools and Platforms that Incorporate Artificial Intelligence Functionalities
20.10.2. Evaluation of Tools for Efficient Test Management and Execution in AI Projects 
20.10.3. Use of Machine Learning Algorithms for Test Case Generation and Optimization 
20.10.4. Strategies for Effective Selection and Adoption of QA Tools with AI Capabilities 

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