Prepare your company for the future with TECH! You will incorporate the most current AI procedures and with all the guarantees to move towards success" 

Why Study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class center for intensive managerial skills education.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success"

At TECH Global University

Innovation

The university offers an online learning model that balances the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus.

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system. 

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high...  

95% of TECH students successfully complete their studies.

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future.  

100,000+ executives prepared each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents.  

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent.

Multicultural Context

While studying at TECH, students will enjoy a unique experience. Study in a multicultural context. In a program with a global vision, through which students can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea.

TECH students represent more than 200 different nationalities.  

Learn with the best

In the classroom, TECH’s teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

Teachers representing 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique:   

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.  

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning method (postgraduate learning methodology with the best international valuation) with the Case Study. Tradition and vanguard in a difficult balance, and in the context of the most demanding educational itinerary. 

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs. And in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.   

At TECH, you will have access to the most rigorous and up-to-date case analyses in academia” 

The Professional master’s degree in Artificial Intelligence is a tailor-made program that is taught in 100% online format so you can choose the time and place that best suits your availability, schedules and interests. A program that takes place over 12 months and is intended to be a unique and stimulating experience that lays the foundation for your professional success. 

You will delve into the data as part of Artificial Intelligence, from its extraction and grouping by types, to its subsequent processing and analysis"

Syllabus

The syllabus of this Professional Master’s Degree has been designed with the aim of providing graduates with the most cutting-edge knowledge in AI. Therefore, professionals will acquire the necessary tools to develop optimization processes inspired by biological evolution.

They can then identify and implement effective solutions to complex problems with a deep grasp of AI.

This is an exclusive academic program in which students will explore the essential foundations of AI. In this way, it will integrate its use into mass-use applications, allowing them to understand how these platforms can enrich the user experience and maximize operational efficiency.

Also, to facilitate the assimilation and retention of all concepts, TECH bases all its programs on the innovative and effective methodology Relearning. Under this approach, students will strengthen their understanding by repeating key concepts throughout the program, presented in various audiovisual formats to achieve a natural and gradual acquisition of skills.

A syllabus focused on professional improvement for the achievement of work objectives that is offered through an innovative and flexible online learning system that allows graduates to combine teaching with their other tasks.

Module 1. Fundamentals of Artificial Intelligence
Module 2. Data Types and Data Life Cycle
Module 3. Data in Artificial Intelligence
Module 4. Data Mining: Selection, Pre-Processing and Transformation
Module 5. Algorithm and Complexity in Artificial Intelligence
Module 6. Intelligent Systems 
Module 7. Machine Learning and Data Mining
Module 8. Neural Networks, the Basis of Deep Learning
Module 9. Deep Neural Networks Training
Module 10. Model Customization and Training with TensorFlow
Module 11. Deep Computer Vision with Convolutional Neural Networks
Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention
Module 13. Autoencoders, GANs, and Diffusion Models 
Module 14. Bio-Inspired Computing
Module 15. Artificial Intelligence: Strategies and Applications

Where, When and How is it Taught?

TECH offers the possibility to develop this Professional Master’s Degree in Artificial Intelligence completely online. Throughout the 12 months of the educational program, you will be able to access all the contents of this program at any time, allowing you to self-manage your study time. 

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 Result Evaluation

2.7.1. Statistical Measures
2.7.2. Relationship Indexes
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. Criteria for Mathematical Analysis of 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. Programming of the learning rate 

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. Graphs 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 tf.data API 

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

10.8. Layers of Preprocessing of Keras 

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. Using the Application to Predict 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. Layers of Grouping and Implementation of Layers of Grouping 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. Segmentation methods based on rules

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 training data set 
12.2.3. Data Cleaning and Transformation 
12.2.4. Sentiment Analysis 

12.3. Rating of reviews 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 NRN 
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 TransformersModels 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 Bookstore 

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 Transformerslibrary 

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. 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 Dissemination 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

You will gain a comprehensive perspective on technological innovation in the healthcare sector of the pharmaceutical and biotechnology industry"