With this 100% online Professional master’s degree, you will understand how AI can transform technical and fundamental analysis, optimizing investment decisions with a precision that defies human intuition” 

The use of Artificial Intelligence (AI) in finance has intensified with the development of advanced Machine Learning algorithms, which optimize investment strategies and risk analysis. Financial institutions are adopting AI to automate operations, detect fraud in real time and personalize investment recommendations for their clients.

This is how this Professional master’s degree was created, which will provide a solid understanding of how to apply advanced Artificial Intelligence techniques for the technical analysis of the markets. Therefore, professionals will be able to use modern tools for the visualization and automation of technical indicators, as well as implement sophisticated models, such as convolutional neural networks for the recognition of financial patterns.

Likewise, experts will become familiar with Machine Learning and Deep Learning techniques, as well as Natural Language Processing (NLP) to analyze financial statements and other relevant documents. Methodologies for risk and credit assessment, ESG sustainability analysis and financial fraud detection will also be addressed.

Finally, the processing of large volumes of financial data will be covered, handling and analyzing Big Data with advanced tools, such as Hadoop and Spark. In addition, the integration, cleansing and visualization of data, as well as security and privacy in the handling of financial information will be explored. At the same time, algorithmic trading strategies will be analyzed, including the design and optimization of automated and automated systems. 

In this way, TECH has created a detailed, fully online university program, which provides graduates with access to educational materials through any electronic device with an Internet connection. This eliminates the need to travel to a physical location and adapt to a specific schedule. Additionally, it integrates the revolutionary Relearning methodology, which is based on the repetition of essential concepts to improve the understanding of the content.

You will be able to handle and analyze large volumes of financial data, design effective algorithmic trading strategies, and address complex ethical and regulatory issues” 

This Professional master’s degree in Artificial Intelligence in Stock Exchanges and Financial Markets contains the most complete and up-to-date program on the market. The most important features include: 

• The development of case studies presented by experts in Artificial Intelligence focused on  Stock Exchanges and Financial Markets
• The graphic, schematic, and practical contents with which they are created, provide 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

You will delve into advanced methods such as reinforcement learning for algorithmic trading and time series modeling with LSTM, thanks to an extensive library of innovative multimedia resources” 

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 course. For this purpose, students will be assisted by an innovative interactive video system created by renowned and experienced experts.

You will have the ability to perform accurate and efficient analysis in an environment of increasing complexity and dynamics in the financial markets, through the best teaching materials, at the forefront of technology and education"

You will address ethics and regulation in the use of AI in finance, preparing you to face ethical and regulatory challenges, as well as to develop technologies responsibly in the financial sector"

This academic program will offer comprehensive content, designed to address the complexities of the modern financial environment through the advanced use of AI technologies.  As such, experts will delve into the technical and fundamental analysis of financial markets, applying Machine Learning and Deep Learning tools to optimize investment decisions and trading strategies. You will also cover techniques for processing and visualizing large volumes of data, as well as the development and implementation of high-frequency algorithmic systems. 

You will focus on critical aspects, such as ethics and regulation in the use of AI in finance, preparing you to manage ethical and regulatory challenges, hand in hand with the best online university in the world, according to Forbes: TECH” 

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 Its 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 Indexes
2.7.3. Data Mining

2.8. 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

2.10. Regulatory Framework

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Regulatory 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. Result Analysis

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. Hidden Layer
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 Graphs 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 TensorFlow Graphs

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 Graphs

10.4.1. Functions with TensorFlow
10.4.2. Use of Graphs for Model Training
10.4.3. Grap Optimization with TensorFlow Operations

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 TensorFlow Tools for Data Manipulation

10.6. The tfdata API

10.6.1. Using the tf.data API for Data Processing
10.6.2. Construction of Data Streams with tf.data
10.6.3. Using the tf.data API for Model Training

10.7. The TFRecord Format

10.7.1. Using the TFRecord API 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. ResNet Architecture

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 Recurrent Neural Networks (RNN) 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 Transformers Models for Natural Language Processing
12.6.2. Application of Transformers Models for Vision
12.6.3. Advantages of Transformers Models

12.7. Transformers for Vision

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

12.8. Hugging Face’s Transformers Library

12.8.1. Using Hugging Face's Transformers Library
12.8.2. Hugging Face’s Transformers Library Application
12.8.3. Advantages of Hugging Face’s Transformers Library

12.9. Other Transformers Libraries. Comparison

12.9.1. Comparison Between Different Transformers Libraries
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. Noise Suppression of Automatic Encoders

13.5.1. Filter Application
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 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 Healthcare 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. Technical Analysis of Financial Markets with AI

16.1. Analysis and Visualization of Technical Indicators with Plotly and Dash

16.1.1. Implementation of Interactive Charts with Plotly
16.1.2. Advanced Visualization of Time Series with Matplotlib
16.1.3. Creating Real-Time Dynamic Dashboards with Dash

16.2. Optimization and Automation of Technical Indicators with Scikit-learn

16.2.1. Automation of Indicators with Scikit-learn
16.2.2. Optimization of Technical Indicators
16.2.3. Creating Personalized Indicators with Keras

16.3. Financial Pattern Recognition with CNN

16.3.1. Using CNN in TensorFlow to Identify Patterns in Charts
16.3.2. Improving Recognition Models with Transfer Learning Techniques
16.3.3. Validation of Recognition Models in Real-Time Markets

16.4. Quantitative Trading Strategies with QuantConnect

16.4.1. Building Algorithmic Trading Systems with QuantConnect
16.4.2. Backtesting Strategies with QuantConnect
16.4.3. Integrating Machine Learning into Trading Strategies with QuantConnect

16.5. Algorithmic Trading with Reinforcement Learning Using TensorFlow

16.5.1. Reinforcement Learning for Trading
16.5.2. Creating Trading Agents with TensorFlow Reinforcement Learning
16.5.3. Simulating and Tuning Agents in OpenAI Gym

16.6. Time Series Modeling with LSTM in Keras for Price Forecasting

16.6.1. Applying LSTM to Price Forecasting
16.6.2. Implementing LSTM Models in Keras for Financial Time Series
16.6.3. Optimization and Parameter Fitting in Time Series Models

16.7. Application of Explainable Artificial Intelligence (XAI) in Finance

16.7.1. Applicability of XAI in Finances
16.7.2. Applying LIME to Trading Models
16.7.3. Using SHAP for Feature Contribution Analysis in AI Decisions

16.8. High-Frequency Trading (HFT) Optimized with Machine Learning Models

16.8.1. Developing ML Models for HFT
16.8.2. Implementing HFT Strategies with TensorFlow
16.8.3. Simulation and Evaluation of HFT in Controlled Environments

16.9. Volatility Analysis Using Machine Learning

16.9.1. Applying Intelligent Models to Predict Volatility
16.9.2. Implementing Volatility Models with PyTorch
16.9.3. Integrating Volatility Analysis into Portfolio Risk Management

16.10. Portfolio Optimization with Genetic Algorithms

16.10.1. Fundamentals of Genetic Algorithms for Investment Optimization in Markets
16.10.2. Implementing Genetic Algorithms for Portfolio Selection
16.10.3. Evaluation of Portfolio Optimization Strategies

Module 17. Fundamental Analysis of Financial Markets with AI

17.1. Predictive Financial Performance Modeling with Scikit-Learn

17.1.1. Linear and Logistic Regression for Financial Forecasting with Scikit-Learn
17.1.2. Using Neural Networks with TensorFlow to Forecast Revenues and Earnings
17.1.3. Validating Predictive Models with Cross-Validation Using Scikit-Learn

17.2. Valuation of Companies with Deep Learning

17.2.1. Automating the Discounted Cash Flows (DCF) Model with TensorFlow 
17.2.2. Advanced Valuation Models Using PyTorch
17.2.3. Integration and Analysis of Multiple Valuation Models with Pandas

17.3. Analysis of Financial Statements with NLP Using ChatGPT

17.3.1. Extracting Key Information from Annual Reports with ChatGPT
17.3.2. Sentiment Analysis of Analyst Reports and Financial News with ChatGPT
17.3.3. Implementing NLP Models with Chat GPT for Interpreting Financial Texts 

17.4. Risk and Credit Analysis with Machine Learning

17.4.1. Credit Scoring Models Using SVM and Decision Trees in Scikit-Learn
17.4.2. Credit Risk Analysis in Corporations and Bonds with TensorFlow
17.4.3. Visualization of Risk Data with Tableau

17.5. Credit Analysis with Scikit-Learn

17.5.1. Implementing Credit Scoring Models
17.5.2. Credit Risk Analysis with RandomForest in Scikit-Learn
17.5.3. Advanced Visualization of Credit Scoring Results with Tableau

17.6. ESG Sustainability Assessment with Data Mining Techniques

17.6.1. ESG Data Mining Methods
17.6.2. ESG Impact Modeling with Regression Techniques
17.6.3. Applications of ESG Analysis in Investment Decisions

17.7. Sector Benchmarking with Artificial Intelligence Using TensorFlow and Power BI

17.7.1. Comparative Analysis of Companies Using AI
17.7.2. Predictive Modeling of Sector Performance with TensorFlow
17.7.3. Implementing Industry Dashboards with Power BI

17.8. Portfolio Management with AI Optimization

17.8.1. Portfolio Optimization
17.8.2. Use of Machine Learning Techniques for Portfolio Optimization with Scikit-Optimize 
17.8.3. Implementing and Evaluating the Effectiveness of Algorithms in Portfolio Management

17.9. Financial Fraud Detection with AI Using TensorFlow and Keras

17.9.1. Basic Concepts and Techniques of Fraud Detection with AI
17.9.2. Constructing Neural Network Detection Models in TensorFlow
17.9.3. Practical Implementation of Fraud Detection Systems in Financial Transactions

17.10. Analysis and Modeling in Mergers and Acquisitions with AI

17.10.1. Using Predictive AI Models to Evaluate Mergers and Acquisitions
17.10.2. Simulating Post-Merger Scenarios Using Machine Learning Techniques
17.10.3. Evaluating the Financial Impact of M&A with Intelligent Models

Module 18. Large Scale Financial Data Processing

18.1. Big Data in the Financial Context

18.1.1. Key Characteristics of Big Data in Finance
18.1.2. Importance of the 5 Vs (Volume, Velocity, Variety, Veracity, Value) in Financial Data
18.1.3. Use Cases of Big Data in Risk Analysis and Compliance

18.2. Technologies for Storage and Management of Financial Big Data

18.2.1. NoSQL Database Systems for Financial Warehousing
18.2.2. Using Data Warehouses and Data Lakes in the Financial Sector
18.2.3. Comparison between On-Premises and Cloud-Based Solutions

18.3. Real-Time Processing Tools for Financial Data

18.3.1. Introduction to Tools such as Apache Kafka and Apache Storm
18.3.2. Real-Time Processing Applications for Fraud Detection
18.3.3. Benefits of Real-Time Processing in Algorithmic Trading

18.4. Integration and Data Cleaning in Finance

18.4.1. Methods and Tools for Integrating Data from Multiple Sources
18.4.2. Data Cleaning Techniques to Ensure Data Quality and Accuracy
18.4.3. Challenges in the Standardization of Financial Data

18.5. Data Mining Techniques Applied to The Financial Markets

18.5.1. Classification and Prediction Algorithms in Market Data
18.5.2. Sentiment Analysis in Social Networks for Predicting Market Movements
18.5.3. Data Mining to Identify Trading Patterns and Investor Behavior

18.6. Advanced Data Visualization for Financial Analysis

18.6.1. Visualization Tools and Software for Financial Data
18.6.2. Design of Interactive Dashboards for Market Monitoring
18.6.3. The Role of Visualization in Risk Analysis Communication

18.7. Use of Hadoop and Related Ecosystems in Finance

18.7.1. Key Components of the Hadoop Ecosystem and Their Application in Finance
18.7.2. Hadoop Use Cases for Large Transaction Volume Analysis
18.7.3. Advantages and Challenges of Integrating Hadoop into Existing Financial Infrastructures

18.8. Spark Applications in Financial Analytics

18.8.1. Spark for Real-Time and Batch Data Analytics
18.8.2. Predictive Model Building Using Spark MLlib
18.8.3. Integration of Spark with Other Big Data Tools in Finance

18.9. Data Security and Privacy in the Financial Sector

18.9.1. Data Protection Rules and Regulations (GDPR, CCPA)
18.9.2. Encryption and Access Management Strategies for Sensitive Data 
18.9.3. Impact of Data Breaches on Financial Institutions

18.10. Impact of Cloud Computing on Large-Scale Financial Analysis

18.10.1. Advantages of the Cloud for Scalability and Efficiency in Financial Analysis
18.10.2. Comparison of Cloud Providers and Their Specific Financial Services
18.10.3. Case Studies on Migration to the Cloud in Large Financial Institutions

Module 19. Algorithmic Trading Strategies

19.1. Fundamentals of Algorithmic Trading

19.1.1. Algorithmic Trading Strategies
19.1.2. Key Technologies and Platforms for the Development of Algorithmic Trading Algorithms
19.1.3. Advantages and Challenges of Automated Trading versus Manual Trading

19.2. Design of Automated Trading Systems

19.2.1. Structure and Components of an Automated Trading System
19.2.2. Algorithm Programming: from the Idea to the Implementation
19.2.3. Latency and Hardware Considerations in Trading Systems

19.3. Backtesting and Evaluation of Trading Strategies

19.3.1. Methodologies for Effective Backtesting of Algorithmic Strategies
19.3.2. Importance of Quality Historical Data in Backtesting
19.3.3. Key Performance Indicators for Evaluating Trading Strategies 

19.4. Optimizing Strategies with Machine Learning

19.4.1. Applying Supervised Learning Techniques in Strategy Improvement
19.4.2. Using Particle Swarm Optimization and Genetic Algorithms
19.4.3. Challenges of Overfitting in Trading Strategy Optimization 

19.5. High Frequency Trading (HFT)

19.5.1. Principles and Technologies behind HFT
19.5.2. Impact of HFT on Market Liquidity and Volatility
19.5.3. Common HFT Strategies and Their Effectiveness

19.6. Order Execution Algorithms

19.6.1. Types of Execution Algorithms and Their Practical Application
19.6.2. Algorithms for Minimizing the Market Impact
19.6.3. Using Simulations to Improve Order Execution

19.7. Arbitration Strategies in Financial Markets

19.7.1. Statistical Arbitrage and Price Merger in Markets
19.7.2. Index and ETF Arbitrage
19.7.3. Technical and Legal Challenges of Arbitrage in Modern Trading

19.8. Risk Management in Algorithmic Trading

19.8.1. Risk Measures for Algorithmic Trading
19.8.2. Integrating Risk Limits and Stop-Loss in Algorithms
19.8.3. Specific Risks of Algorithmic Trading and How to Mitigate Them

19.9. Regulatory Aspects and Compliance in Algorithmic Trading

19.9.1. Global Regulations Impacting Algorithmic Trading
19.9.2. Regulatory Compliance and Reporting in an Automated Environment
19.9.3. Ethical Implications of Automated Trading

19.10. Future of Algorithmic Trading and Emerging Trends

19.10.1. Impact of Artificial Intelligence on the Future Development of Algorithmic Trading
19.10.2. New Blockchain Technologies and Their Application in Algorithmic Trading
19.10.3. Trends in Adaptability and Customization of Trading Algorithms

Module 20. Ethical and Regulatory Aspects of AI in Finance

20.1. Ethics in Artificial Intelligence Applied to Finance

20.1.1. Fundamental Ethical Principles for the Development and Use of AI in Finance
20.1.2. Case Studies on Ethical Dilemmas in Financial AI Applications 
20.1.3. Developing Ethical Codes of Conduct for Financial Technology Professionals

20.2. Global Regulations Affecting the Use of AI in Financial Markets

20.2.1. Overview of the Main International Financial Regulations on AI 
20.2.2. Comparison of AI Regulatory Policies among Different Jurisdictions
20.2.3. Implications of AI Regulation on Financial Innovation

20.3. Transparency and Explainability of AI Models in Finance

20.3.1. Importance of Transparency in AI Algorithms for User Confidence
20.3.2. Techniques and Tools to Improve the Explainability of AI Models 
20.3.3. Challenges of Implementing Interpretable Models in Complex Financial Environments 

20.4. Risk Management and Ethical Compliance in the Use of AI

20.4.1. Risk Mitigation Strategies Associated with the Deployment of AI in Finance
20.4.2. Ethics Compliance in the Development and Application of AI Technologies
20.4.3. Ethical Oversight and Audits of AI Systems in Financial Operations

20.5. Social and Economic Impact of AI in Financial Markets

20.5.1. Effects of AI on the Stability and Efficiency of Financial Markets 
20.5.2. AI and Its Impact on Employment and Professional Skills in Finance
20.5.3. Benefits and Social Risks of Large-Scale Financial Automation 

20.6. Data Privacy and Protection in AI Financial Applications

20.6.1. Data Privacy Regulations Applicable to AI Technologies in Finance
20.6.2. Personal Data Protection Techniques in AI-Based Financial Systems
20.6.3. Challenges in Managing Sensitive Data in Predictive and Analytics Models

20.7. Algorithmic Bias and Fairness in AI Financial Models

20.7.1. Identification and Mitigation of Bias in Financial AI Algorithms 
20.7.2. Strategies to Ensure Fairness in Automated Decision-Making Models
20.7.3. Impact of Algorithmic Bias on Financial Inclusion and Equity 

20.8. Challenges of Regulatory Oversight in Financial AI

20.8.1. Difficulties in the Supervision and Control of Advanced AI Technologies
20.8.2. Role of Financial Authorities in the Ongoing Supervision of AI 
20.8.3. Need for Regulatory Adaptation in the Face of Advancing AI Technology

20.9. Strategies for Responsible Development of AI Technologies in Finance

20.9.1. Best Practices for Sustainable and Responsible AI Development in the Financial Sector
20.9.2. Initiatives and Frameworks for Ethical Assessment of AI Projects in Finance
20.9.3. Collaboration between Regulators and Businesses to Encourage Responsible Practices 

20.10. Future of AI Regulation in the Financial Sector

20.10.1. Emerging Trends and Future Challenges in AI Regulation in Finance
20.10.2. Preparation of Legal Frameworks for Disruptive Innovations in Financial Technology
20.10.3. International Dialogue and Cooperation for Effective and Unified Regulation of AI in Finance

You will receive robust, up-to-date training, combining advanced theory with practical applications for you to lead at the intersection of Artificial Intelligence and finance”