This Advanced master’s degree will equip you with the knowledge and skills necessary to boost your career as a Software Developer”

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. 

Economy of Scale

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 Advanced master’s degree inMBA in Artificial Intelligence in Computer Programming is made up of 30 specialized modules, which will provide students with a comprehensive understanding of this subject. The syllabus will cover topics such as Data Mining, Algorithmics, Intelligent Systems and Machine Learning, enabling graduates to incorporate the most advanced technological tools into their computer programming projects to improve the efficiency of their models. In addition, the syllabus will include cutting-edge modules such as Neural Networks, Deep Computer Vision, Bio-inspired Computing or Software Architecture.  

You will have at your disposal a wide range of didactic tools to stimulate your learning, including explanatory videos or interactive summaries” 

Syllabus

The Advanced master’s degree in MBA in Artificial Intelligence in Computer Programming at TECH Global University is an intense program that prepares students to face challenges and business decisions, both nationally and internationally. Its content is designed to promote the development of managerial skills that enable more rigorous decision-making in uncertain environments.   

Throughout 3,600 hours of study, students will analyze a multitude of practical cases through individual work, achieving high quality learning that can be applied to their daily practice. It is, therefore, an authentic immersion in real business situations. 
This program deals in depth with the main areas of the Artificial Intelligence and is designed for managers to understand business management from a strategic, international and innovative perspective.   

A plan designed for students, focused on their professional development, which prepares them to achieve excellence in the field of in Artificial Intelligence in Computer Programming. A program that understands your needs and those of your company through innovative content based on the latest trends, and supported by the best educational methodology and an exceptional faculty, which will provide you with the competencies to solve critical situations in a creative and efficient way. 

This program is developed over 2 years and is divided into 30 modules: 

Module 1 Leadership, Ethics and Social Responsibility in Companies
Module 2 Strategic Managementand Executive Management 
Module 3 People and Talent Management
Module 4 Economic and Financial Management
Module 5 Operations and Logistics Management
Module 6 Information Systems Management
Module 7 Commercial Management, Strategic Marketing and Corporate Communications
Module 8 Market Research, Advertising and Commercial Management
Module 9 Innovation and Project Management
Module 10 Executive Management
Module 11 Fundamentals of Artificial Intelligence
Module 12 Data Types and Life Cycle
Module 13 Data in Artificial Intelligence 
Module 14 Data Mining: Selection, Pre-Processing and Transformation 
Module 15 Algorithm and Complexity in Artificial Intelligence  
Module 16 Intelligent Systems
Module 17 Machine Learning and Data Mining
Module 18 Neural Networks, the Basis of Deep Learning 
Module 19 Deep Neural Networks Training 
Module 20 Model Customization and training with TensorFlow 
Module 21 Deep Computer Vision with Convolutional Neural Networks 
Module 22 Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention
Module 23 Autoencoders, GANs, and Diffusion Models 
Module 24 Bio-Inspired Computing
Module 25 Artificial Intelligence: Strategies and Applications
Module 26 Software Development Productivity Improvement with AI
Module 27 Software Architecture for QA Testing
Module 28 Website Projects with AI
Module 29 Mobile Applications with AI
Module 30 AI for QA Testing

Where, When and How is it Taught?

TECH offers the possibility of developing this Advanced master’s degree in MBA in Artificial Intelligence in Computer Programming completely online. During the 2 years of the program, students will be able to access all the contents in this program at any time, which will allow them to manage their own study time. 

Module 1. Leadership, Ethics and Social Responsibility in Companies

1.1. Globalization and Governance 

1.1.1. Governance and Corporate Governance 
1.1.2. The Fundamentals of Corporate Governance in Companies 
1.1.3. The Role of the Board of Directors in the Corporate Governance Framework 

1.2. Leadership 

1.2.1. Leadership A Conceptual Approach 
1.2.2. Leadership in Companies 
1.2.3. The Importance of Leaders in Business Management 

1.3. Cross Cultural Management 

1.3.1. Cross Cultural Management Concept
1.3.2. Contributions to Knowledge of National Cultures 
1.3.3. Diversity Management 

1.4. Management and Leadership Development 

1.4.1. Concept of Management Development 
1.4.2. Concept of Leadership 
1.4.3. Leadership Theories 
1.4.4. Leadership Styles 
1.4.5. Intelligence in Leadership 
1.4.6. The Challenges of Today's Leader 

1.5. Business Ethics 

1.5.1. Ethics and Morality 
1.5.2. Business Ethics 
1.5.3. Leadership and Ethics in Companies 

1.6. Sustainability 

1.6.1. Sustainability and Sustainable Development 
1.6.2. The 2030 Agenda 
1.6.3. Sustainable Companies 

1.7. Corporate Social Responsibility 

1.7.1. International Dimensions of Corporate Social Responsibility 
1.7.2. Implementing Corporate Social Responsibility 
1.7.3. The Impact and Measurement of Corporate Social Responsibility 

1.8. Responsible Management Systems and Tools 

1.8.1. CSR: Corporate Social Responsibility 
1.8.2. Essential Aspects for Implementing a Responsible Management Strategy 
1.8.3. Steps for the Implementation of a Corporate Social Responsibility Management System 
1.8.4. CSR Tools and Standards 

1.9. Multinationals and Human Rights 

1.9.1. Globalization, Multinational Companies and Human Rights 
1.9.2. Multinational Companies vs. International Law 
1.9.3. Legal Instruments for Multinationals in the Area of Human Rights 

1.10. Legal Environment and Corporate Governance 

1.10.1. International Rules on Importation and Exportation 
1.10.2. Intellectual and Industrial Property 
1.10.3. International Labor Law

Module 2. Strategic Management and Executive Management 

2.1. Organizational Analysis and Design 

2.1.1. Conceptual Framework
2.1.2. Key Elements in Organizational Design
2.1.3. Basic Organizational Models
2.1.4. Organizational Design: Typologies 

2.2. Corporate Strategy 

2.2.1. Competitive Corporate Strategy
2.2.2. Types of Growth Strategies
2.2.3. Conceptual Framework 

2.3. Strategic Planning and Strategy Formulation 

2.3.1. Conceptual Framework
2.3.2. Elements of Strategic Planning
2.3.3. Strategy Formulation: Strategic Planning Process 

2.4. Strategic Thinking 

2.4.1. The Company as a System
2.4.2. Organization Concept

2.5. Financial Diagnosis 

2.5.1. Concept of Financial Diagnosis
2.5.2. Stages of Financial Diagnosis
2.5.3. Assessment Methods for Financial Diagnosis 

2.6. Planning and Strategy 

2.6.1. The Plan from a Strategy
2.6.2. Strategic Positioning
2.6.3. Strategy in Companies 

2.7. Strategy Models and Patterns 

2.7.1. Conceptual Framework
2.7.2. Strategic Models
2.7.3. Strategic Patterns: The Five P’s of Strategy 

2.8. Competitive Strategy 

2.8.1. The Competitive Advantage
2.8.2. Choosing a Competitive Strategy
2.8.3. Strategies Based on the Strategic Clock Model
2.8.4. Types of Strategies According to the Industrial Sector Life Cycle 

2.9. Strategic Management 

2.9.1. The Concept of Strategy
2.9.2. The Process of Strategic Management
2.9.3. Approaches in Strategic Management 

2.10. Strategy Implementation 

2.10.1. Indicator Systems and Process Approach
2.10.2. Strategic Map
2.10.3. Strategic Alignment 

2.11. Executive Management 

2.11.1. Conceptual Framework of Executive Management
2.11.2. Executive Management The Role of the Board of Directors and Corporate Management Tools 

2.12. Strategic Communication 

2.12.1. Interpersonal Communication 
2.12.2. Communication Skills and Influence 
2.12.3. Internal Communication 
2.12.4. Barriers to Business Communication

Module 3. People and Talent Management 

3.1. Organizational Behavior 

3.1.1. Organizational Behavior Conceptual Framework
3.1.2. Main Factors of Organizational Behavior 

3.2. People in Organizations 

3.2.1. Quality of Work Life and Psychological Well-Being
3.2.2. Work Teams and Meeting Management
3.2.3. Coaching and Team Management
3.2.4. Managing Equality and Diversity 

3.3. Strategic People Management 

3.3.1. Strategic Human Resources Management
3.3.2. Strategic People Management 

3.4. Evolution of Resources An Integrated Vision 

3.4.1. The Importance of HR
3.4.2. A New Environment for People Management and Leadership
3.4.3. Strategic HR Management

3.5. Selection, Group Dynamics and HR Recruitment 

3.5.1. Approach to Recruitment and Selection
3.5.2. Recruitment
3.5.3. The Selection Process 

3.6. Human Resources Management by Competencies 

3.6.1. Analysis of the Potential
3.6.2. Remuneration Policy
3.6.3. Career/Succession Planning 

3.7. Performance Evaluation and Compliance Management 

3.7.1. Performance Management
3.7.2. Performance Management: Objectives and Process 

3.8. Training Management 

3.8.1. Learning Theories
3.8.2. Talent Detection and Retention
3.8.3. Gamification and Talent Management
3.8.4. Training and Professional Obsolescence 

3.9. Talent Management 

3.9.1. Keys for Positive Management
3.9.2. Conceptual Origin of Talent and its Implication in the Company
3.9.3. Map of Talent in the Organization 
3.9.4. Cost and Added Value 

3.10. Innovation in Talent and People Management 

3.10.1. Strategic Talent Management Models
3.10.2. Identification, Training and Development of Talent
3.10.3. Loyalty and Retention 
3.10.4. Proactivity and Innovation 

3.11. Motivation 

3.11.1. The Nature of Motivation 
3.11.2. Expectations Theory
3.11.3. Needs Theory
3.11.4. Motivation and Financial Compensation 

3.12. Employer Branding 

3.12.1. Employer Branding in HR
3.12.2. Personal Branding for HR Professionals 

3.13. Developing High Performance Teams

3.13.1. High Performance Teams: Self-Managed Teams 
3.13.2. Methodologies for the Management of High Performance Self-Managed Teams

3.14. Management Skills Development 

3.14.1. What are Manager Competencies? 
3.14.2. Elements of Competencies 
3.14.3. Knowledge 
3.14.4. Management Skills 
3.14.5. Attitudes and Values in Managers 
3.14.6. Managerial Skills 

3.15. Time Management 

3.15.1. Benefits
3.15.2. What Can be the Causes of Poor Time Management? 
3.15.3. Time 
3.15.4. Time Illusions 
3.15.5. Attention and Memory
3.15.6. State of Mind
3.15.7. Time Management
3.15.8. Being Proactive
3.15.9. Be Clear About the Objective
3.15.10. Order 
3.15.11. Planning 

3.16. Change Management 

3.16.1. Change Management
3.16.2. Type of Change Management Processes
3.16.3. Stages or Phases in the Change Management Process 

Module 4.  Economic and Financial Management 

4.1. Economic Environment 

4.1.1. Macroeconomic Environment and the National Financial System 
4.1.2. Financial Institutions 
4.1.3. Financial Markets 
4.1.4. Financial Assets 
4.1.5. Other Financial Sector Entities 

4.2. Company Financing 

4.2.1. Sources of Financing 
4.2.2. Types of Financing Costs 

4.3. Executive Accounting 

4.3.1. Basic Concepts  
4.3.2. The Company's Assets  
4.3.3. The Company's Liabilities  
4.3.4. The Company's Net Worth  
4.3.5. The Income Statement  

4.4. From General Accounting to Cost Accounting 

4.4.1. Elements of Cost Calculation 
4.4.2. Expenses in General Accounting and Cost Accounting 
4.4.3. Costs Classification 

4.5. Information Systems and Business Intelligence 

4.5.1. Fundamentals and Classification 
4.5.2. Cost Allocation Phases and Methods 
4.5.3. Choice of Cost Center and Impact 

4.6. Budget and Management Control 

4.6.1. The Budget Model  
4.6.2. The Capital Budget 
4.6.3. The Operating Budget  
4.6.5. Treasury Budget  
4.6.6. Budget Monitoring  

4.7. Treasury Management 

4.7.1. Accounting Working Capital and Necessary Working Capital 
4.7.2. Calculation of Operating Requirements of Funds 
4.7.3. Credit Management 

4.8. Corporate Tax Responsibility 

4.8.1. Basic Tax Concepts  
4.8.2. Corporate Income Tax  
4.8.3. Value Added Tax  
4.8.4. Other Taxes Related to Commercial with the Mercantile Activity  
4.8.5. The Company as a Facilitator of the Work of the of the State  

Module 5. Operations and Logistics Management 

5.1. Operations Direction and Management

5.1.1. The Role of Operations 
5.1.2. The Impact of Operations on the Management of Companies.  
5.1.3. Introduction to Operations Strategy 
5.1.4. Operations Management

5.2. Industrial Organization and Logistics 

5.2.1. Industrial Organization Department
5.2.2. Logistics Department

5.3. Structure and Types of Production (MTS, MTO, ATO, ETO, etc)  

5.3.1. Production System 
5.3.2. Production Strategy  
5.3.3. Inventory Management System
5.3.4. Production Indicators 

5.4. Structure and Types of Procurement  

5.4.1. Function of Procurement 
5.4.2. Procurement Management
5.4.3. Types of Purchases 
5.4.4. Efficient Purchasing Management of a Company 
5.4.5. Stages of the Purchase Decision Process 

5.5. Economic Control of Purchasing 

5.5.1. Economic Influence of Purchases
5.5.2. Cost Centers 
5.5.3. Budget
5.5.4. Budgeting vs. Actual Expenditure
5.5.5. Budgetary Control Tools

5.6. Warehouse Operations Control 

5.6.1. Inventory Control
5.6.2. Location Systems
5.6.3. Stock Management Techniques
5.6.4. Storage Systems

5.7. Strategic Purchasing Management

5.7.1. Business Strategy
5.7.2. Strategic Planning
5.7.3. Purchasing Strategies

5.8. Typologies of the Supply Chain (SCM) 

5.8.1. Supply Chain
5.8.2. Benefits of Supply Chain Management
5.8.3. Logistical Management in the Supply Chain

5.9. Supply Chain Management 

5.9.1. The Concept of Management of the Supply Chain (SCM)
5.9.2. Supply Chain Costs and Efficiency
5.9.3. Demand Patterns
5.9.4. Operations Strategy and Change

5.10. Interactions Between the SCM and All Other Departments

5.10.1. Interaction of the Supply Chain 
5.10.2. Interaction of the Supply Chain. Integration by Parts
5.10.3. Supply Chain Integration Problems 
5.10.4. Supply Chain

5.11. Logistics Costs 

5.11.1. Logistics Costs
5.11.2. Problems with Logistics Costs
5.11.3. Optimizing Logistic Costs  

5.12. Profitability and Efficiency of Logistics Chains: KPIS 

5.12.1. Logistics Chain
5.12.2. Profitability and Efficiency of the Logistics Chain
5.12.3. Indicators of Profitability and Efficiency of the Supply Chain

5.13. Process Management

5.13.1. Process Management 
5.13.2. Process-Based Approach: Process Mapping 
5.13.3. Improvements in Process Management 

5.14. Distribution and Transportation and Logistics

5.14.1. Distribution in the Supply Chain 
5.14.2. Transportation Logistics 
5.14.3. Geographic Information Systems as a Support to Logistics 

5.15. Logistics and Customers

5.15.1. Demand Analysis 
5.15.2. Demand and Sales Forecast 
5.15.3. Sales and Operations Planning
5.15.4. Participatory Planning, Forecasting and Replenishment Planning (CPFR) 

5.16. International Logistics 

5.16.1. Export and Import Processes 
5.16.2. Customs 
5.16.3. Methods and Means of International Payment
5.16.4. International Logistics Platforms

5.17. Outsourcing of Operations

5.17.1. Operations Management and Outsourcing 
5.17.2. Outsourcing Implementation in Logistics Environments 

5.18. Competitiveness in Operations

5.18.1. Operations Management 
5.18.2. Operational Competitiveness 
5.18.3. Operations Strategy and Competitive Advantages 

5.19. Quality Management

5.19.1. Internal and External Customers
5.19.2. Quality Costs
5.19.3. Ongoing Improvement and the Deming Philosophy

Module 6. Information Systems Management

6.1. Technological Environment

6.1.1. Technology and Globalization 
6.1.2. Economic Environment and Technology 
6.1.3. Technological Environment and its Impact on Companies 

6.2. Information Systems and Technologies in the Enterprise 

6.2.1. The Evolution of the IT Model
6.2.2. Organization and IT Departments
6.2.3. Information Technology and Economic Environment

6.3. Corporate Strategy and Technology Strategy 

6.3.1. Creating Value for Customers and Shareholders
6.3.2. Strategic IS/IT Decisions
6.3.3. Corporate Strategy Vs. Technology and Digital Strategy

6.4. Information Systems Management

6.4.1. Corporate Governance of Technology and Information Systems 
6.4.2. Management of Information Systems in Companies 
6.4.3. Expert Managers in Information Systems: Roles and Functions 

6.5. Information Technology Strategic Planning

6.5.1. Information Systems and Corporate Strategy
6.5.2. Strategic Planning of Information Systems  
6.5.3. Phases of Information Systems Strategic Planning 

6.6. Information Systems for Decision-Making

6.6.1. Business Intelligence
6.6.2. Data Warehouse
6.6.3. BSC or Balanced Scorecard

6.7. Exploring the Information

6.7.1. SQL: Relational Databases.Basic Concepts
6.7.2. Networks and Communications
6.7.3. Operational System: Standardized Data Models 
6.7.4. Strategic System: OLAP, Multidimensional Model and Graphical Dashboards. 
6. 7.5. Strategic DB Analysis and Report Composition

6.8. Enterprise Business Intelligence

6.8.1. The World of Data
6.8.2. Relevant Concepts
6.8.3. Main Characteristics
6.8.4. Solutions in Today's Market
6.8.5. Overall Architecture of a BI Solution
6.8.6. Cybersecurity in BI and Data Science

6.9. New Business Concept  

6.9.1. Why BI
6.9.2. Obtaining Information
6.9.3. BI in the Different Departments of the Company
6.9.4. Reasons to Invest in BI

6.10. BI Tools and Solutions 

6.10.1. How to Choose the Best Tool?
6.10.2. Microsoft Power BI, MicroStrategy y Tableau
6.10.3. SAP BI, SAS BI and Qlikview
6.10.4. Prometheus

6.11. BI Project Planning and Management  

6.11.1. First Steps to Define a BI Project
6.11.2. BI Solution for the Company
6.11.3. Requirements and Objectives 

6.12. Corporate Management Applications 

6.12.1. Information Systems and Corporate Management 
6.12.2. Applications for Corporate Management 
6.12.3. Enterprise Resource Planning or ERP Systems 

6.13. Digital Transformation

6.13.1. Conceptual Framework of Digital Transformation 
6.13.2. Digital Transformation; Key Elements, Benefits and Drawbacks
6.13.3. Digital Transformation in Companies 

6.14. Technology and Trends

6.14.1. Main Trends in the Field of Technology that are Changing Business Models 
6.14.2. Analysis of the Main Emerging Technologies 

6.15. IT Outsourcing

6.15.1. Conceptual Framework of Outsourcing 
6.15.2. IT Outsourcing and its Impact on the Business 
6.15.3. Keys to Implement Corporate IT Outsourcing Projects

Module 7. Commercial Management, Strategic Marketing and Corporate Communication

7.1. Commercial Management

7.1.1. Conceptual Framework of Commercial Management
7.1.2. Business Strategy and Planning
7.1.3. The Role of Sales Managers

7.2. Marketing 

7.2.1. The Concept of Marketing
7.2.2. Basic Elements of Marketing
7.2.3. Marketing Activities of the Company

7.3. Strategic Marketing Management

7.3.1. The Concept of Strategic Marketing
7.3.2. Concept of Strategic Marketing Planning
7.3.3. Stages in the Process of Strategic Marketing Planning

7.4. Digital Marketing and E-Commerce

7.4.1. Digital Marketing and E-commerce Objectives  
7.4.2. Digital Marketing and Media Used 
7.4.3. E-Commerce General Context 
7.4.4. Categories of E-commerce 
7.4.5. Advantages and Disadvantages of E-commerce Versus Traditional Commerce

7.5. Managing Digital Business

7.5.1. Competitive Strategy in the Face of the Growing Digitalization of the Media 
7.5.2. Design and Creation of a Digital Marketing Plan 
7.5.3. ROI Analysis in a Digital Marketing Plan 

7.6. Digital Marketing to Reinforce the Brand

7.6.1. Online Strategies to Improve Your Brand's Reputation
7.6.2. Branded Content and Storytelling

7.7. Digital Marketing Strategy

7.7.1. Defining the Digital Marketing Strategy 
7.7.2. Digital Marketing Strategy Tools 

7.8. Digital Marketing to Attract and Retain Customers 

7.8.1. Loyalty and Engagement Strategies Through the Internet
7.8.2. Visitor Relationship Management
7.8.3. Hypersegmentation

7.9. Managing Digital Campaigns

7.9.1. What is a Digital Advertising Campaign?
7.9.2. Steps to Launch an Online Marketing Campaign
7.9.3. Mistakes in Digital Advertising Campaigns

7.10. Online Marketing Plan

7.10.1. What is an Online Marketing Plan?
7.10.2. Steps to Create an Online Marketing Plan
7.10.3. Advantages of Having an Online Marketing Plan

7.11. Blended Marketing

7.11.1. What is Blended Marketing?
7.11.2. Differences Between Online and Offline Marketing
7.11.3. Aspects to be Taken into Account in the Blended Marketing Strategy 
7.11.4. Characteristics of a Blended Marketing Strategy
7.11.5. Recommendations in Blended Marketing
7.11.6. Benefits of Blended Marketing

7.12. Sales Strategy 

7.12.1. Sales Strategy 
7.12.2. Sales Methods

7.13. Corporate Communication 

7.13.1  Concept
7.13.2  The Importance of Communication in the Organization
7.13.3  Type of Communication in the Organization
7.13.4  Functions of Communication in the Organization
7.13.5  Components of Communication
7.13.6 Communication Problems
7.13.7 Communication Scenarios

7.14. Corporate Communication Strategy 

7.14.1. Motivational Programs, Social Action, Participation and Training with HR 
7.14.2. Internal Communication Tools and Supports
7.14.3. Internal Communication Plan

7.15. Digital Communication and Reputation

7.15.1. Online Reputation 
7.15.2. How to Measure Digital Reputation? 
7.15.3. Online Reputation Tools 
7.15.4. Online Reputation Report 
7.15.5. Online Branding

Module 8. Market Research, Advertising and Commercial Management

8.1. Market Research 

8.1.1. Marketing Research: Historical Origin  
8.1.2. Analysis and Evolution of the Conceptual Framework of Marketing Research  
8.1.3. Key Elements and Value Contribution of Market Research  

8.2. Quantitative Research Methods and Techniques 

8.2.1. Sample Size  
8.2.2. Sampling  
8.2.3. Types of Quantitative Techniques   

8.3. Qualitative Research Methods and Techniques 

8.3.1. Types of Qualitative Research 
8.3.2. Qualitative Research Techniques 

8.4. Market Segmentation 

8.4.1. Market Segmentation Concept  
8.4.2. Utility and Segmentation Requirements  
8.4.3. Consumer Market Segmentation  
8.4.4. Industrial Market Segmentation  
8.4.5. Segmentation Strategies  
8.4.6. Segmentation Based on Marketing - Mix Criteria  
8.4.7. Market Segmentation Methodology 

8.5. Research Project Management 

8.5.1. Market Research as a Process 
8.5.2. Planning Stages in Market Research 
8.5.3. Stages of Market Research Implementation 
8.5.4. Managing a Research Project  

8.6. International Market Research  

8.6.1. International Market Research 
8.6.2. International Market Research Process 
8.6.3. The Importance of Secondary Sources in International Market Research 

8.7. Feasibility Studies    

8.7.1. Concept and Usefulness 
8.7.2. Outline of a Feasibility Study 
8.7.3. Development of a Feasibility Study 

8.8. Publicity 

8.8.1. Historical Background of Advertising  
8.8.2. Conceptual Framework of Advertising; Principles, Concept of Briefing and Positioning  
8.8.3. Advertising Agencies, Media Agencies and Advertising Professionals  
8.8.4. Importance of Advertising in Business 
8.8.5. Advertising Trends and Challenges  

8.9. Developing the Marketing Plan  

8.9.1. Marketing Plan Concept 
8.9.2. Situation Analysis and Diagnosis 
8.9.3. Strategic Marketing Decisions 
8.9.4. Operational Marketing Decisions 

8.10. Promotion and Merchandising Strategies 

8.10.1. Integrated Marketing Communication 
8.10.2. Advertising Communication Plan 
8.10.3. Merchandising as a Communication Technique 

8.11. Media Planning  

8.11.1. Origin and Evolution of Media Planning   
8.11.2. Media  
8.11.3. Media Plan  

8.12. Fundamentals of Commercial Management  

8.12.1. The Role of Commercial Management 
8.12.2. Systems of Analysis of the Company/Market Commercial Competitive Situation 
8.12.3. Commercial Planning Systems of the Company 
8.12.4. Main Competitive Strategies 

8.13. Commercial Negotiation 

8.13.1. Commercial Negotiation   
8.13.2. Psychological Issues in Negotiation 
8.13.3. Main Negotiation Methods 
8.13.4. The Negotiation Process 

8.14. Decision-Making in Commercial Management 

8.14.1. Commercial Strategy and Competitive Strategy 
8.14.2. Decision Making Models 
8.14.3. Decision-Making Analytics and Tools 
8.14.4. Human Behavior in Decision Making 

8.15. Leadership and Management of the Sales Network 

8.15.1. Sales Management. Sales Management 
8.15.2. Networks Serving Commercial Activity 
8.15.3. Salesperson Recruitment and Training Policies 
8.15.4. Remuneration Systems for Own and External Commercial Networks 
8.15.5. Management of the Commercial Process. Control and Assistance to the Work of the Sales Representatives Based on the Information

8.16. Implementing the Commercial Function 

8.16.1. Recruitment of Own Sales Representatives and Sales Agents 
8.16.2. Controlling Commercial Activity  
8.16.3. The Code of Ethics of Sales Personnel 
8.16.4. Compliance with Legislation 
8.16.5. Generally Accepted Standards of Business Conduct 

8.17. Key Account Management 

8.17.1. Concept of Key Account Management   
8.17.2. The Key Account Manager 
8.17.3. Key Account Management Strategy  

8.18. Financial and Budgetary Management  

8.18.1. The Break-Even Point 
8.18.2. The Sales Budget. Control of Management and of the Annual Sales Plan 
8.18.3. Financial Impact of Strategic Sales Decisions 
8.18.4. Cycle Management, Turnover, Profitability and Liquidity
8.18.5. Income Statement

Module 9. Innovation and Project Management

9.1. Innovation

9.1.1. Introduction to Innovation 
9.1.2. Innovation in the Entrepreneurial Ecosystem 
9.1.3. Instruments and Tools for the Business Innovation Process

9.2. Innovation Strategy

9.2.1. Strategic Intelligence and Innovation 
9.2.2. Innovation from Strategy 

9.3. Project Management for Startups

9.3.1. Startup Concept
9.3.2. Lean Startup Philosophy
9.3.3. Stages of Startup Development
9.3.4. The Role of a Project Manager in a Startup

9.4. Business Model Design and Validation

9.4.1. Conceptual Framework of a Business Model 
9.4.2. Business Model Design and Validation 

9.5. Project Management

9.5.1. Project Management: Identification of Opportunities to Develop Corporate Innovation Projects
9.5.2. Main stages or Phases in the Direction and Management of Innovation Projects

9.6. Project Change Management: Training Management 

9.6.1. Concept of Change Management
9.6.2. The Change Management Process
9.6.3. Change Implementation

9.7. Project Communication Management

9.7.1. Project Communications Management
9.7.2. Key Concepts for Project Communications Management
9.7.3. Emerging Trends
9.7.4. Adaptations to Equipment
9.7.5. Planning Communications Management
9.7.6. Manage Communications
9.7.7. Monitoring Communications

9.8. Traditional and Innovative Methodologies

9.8.1. Innovative Methodologies
9.8.2. Basic Principles of Scrum
9.8.3. Differences between the Main Aspects of Scrum and Traditional Methodologies

9.9. Creation of a Startup

9.9.1. Creation of a Startup 
9.9.2. Organization and Culture
9.9.3. Top Ten Reasons Why Startups Fail 
9.9.4. Legal Aspects

9.10. Project Risk Management Planning

9.10.1. Risk Planning
9.10.2. Elements for Creating a Risk Management Plan
9.10.3. Tools for Creating a Risk Management Plan
9.10.4. Content of the Risk Management Plan

Module 10. Executive Management

10.1. General Management

10.1.1. The Concept of General Management  
10.1.2. The General Manager's Action 
10.1.3. The CEO and their Responsibilities 
10.1.4. Transforming the Work of Management 

10.2. Manager Functions: Organizational Culture and Approaches 

10.2.1. Manager Functions: Organizational Culture and Approaches 

10.3. Operations Management  

10.3.1. The Importance of Management 
10.3.2. Value Chain 
10.3.3. Quality Management 

10.4. Public Speaking and Spokesperson Education 

10.4.1. Interpersonal Communication 
10.4.2. Communication Skills and Influence 
10.4.3. Communication Barriers 

10.5. Personal and Organizational Communications Tools 

10.5.1. Interpersonal Communication 
10.5.2. Interpersonal Communication Tools 
10.5.3. Communication in the Organization 
10.5.4. Tools in the Organization 

10.6. Communication in Crisis Situations 

10.6.1. Crisis 
10.6.2. Phases of the Crisis 
10.6.3. Messages: Contents and Moments 

10.7. Preparation of a Crisis Plan 

10.7.1. Analysis of Possible Problems 
10.7.2. Planning 
10.7.3. Adequacy of Personnel 

10.8. Emotional Intelligence  

10.8.1. Emotional Intelligence and Communication 
10.8.2. Assertiveness, Empathy, and Active Listening 
10.8.3. Self-Esteem and Emotional Communication 

10.9. Personal Branding 

10.9.1. Strategies to Develop Personal Branding 
10.9.2. Personal Branding Laws 
10.9.3. Tools for Creating Personal Brands 

10.10. Leadership and Team Management 

10.10.1. Leadership and Leadership Styles 
10.10.2. Leader Capabilities and Challenges 
10.10.3. Managing Change Processes 
10.10.4. Managing Multicultural Teams

Module 11. Fundamentals of Artificial Intelligence

11.1. History of Artificial Intelligence 

11.1.1. When Do We Start Talking About Artificial Intelligence?  
11.1.2. References in Film 
11.1.3. Importance of Artificial Intelligence 
11.1.4. Technologies that Enable and Support Artificial Intelligence 

11.2. Artificial Intelligence in Games 

11.2.1. Game Theory 
11.2.2. Minimax and Alpha-Beta Pruning 
11.2.3. Simulation: Monte Carlo 

11.3. Neural Networks 

11.3.1. Biological Fundamentals 
11.3.2. Computational Model 
11.3.3. Supervised and Unsupervised Neural Networks 
11.3.4. Simple Perceptron 
11.3.5. Multilayer Perceptron 

11.4. Genetic Algorithms 

11.4.1. History 
11.4.2. Biological Basis 
11.4.3. Problem Coding 
11.4.4. Generation of the Initial Population 
11.4.5. Main Algorithm and Genetic Operators 
11.4.6. Evaluation of Individuals: Fitness 

11.5. Thesauri, Vocabularies, Taxonomies 

11.5.1. Vocabulary 
11.5.2. Taxonomy 
11.5.3. Thesauri 
11.5.4. Ontologies 
11.5.5. Knowledge Representation: Semantic Web 

11.6. Semantic Web 

11.6.1. Specifications RDF, RDFS and OWL 
11.6.2. Inference/ Reasoning 
11.6.3. Linked Data 

11.7. Expert Systems and DSS 

11.7.1. Expert Systems 
11.7.2. Decision Support Systems 

11.8. Chatbots and Virtual Assistants  

11.8.1. Types of Assistants: Voice and Text Assistants  
11.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialogue Flow 
11.8.3. Integrations: Web, Slack, Whatsapp, Facebook 
11.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant  

11.9. AI Implementation Strategy 
11.10. Future of Artificial Intelligence  

11.10.1. Understand How to Detect Emotions Using Algorithms  
11.10.2. Creating a Personality: Language, Expressions and Content  
11.10.3. Trends of Artificial Intelligence  
11.10.4. Reflections 

Module 12. Data Types and Life Cycle

12.1. Statistics  

12.1.1. Statistics: Descriptive Statistics, Statistical Inferences  
12.1.2. Population, Sample, Individual  
12.1.3. Variables: Definition, Measurement Scales  

12.2. Types of Data Statistics  

12.2.1. According to Type  

12.2.1.1. Quantitative: Continuous Data and Discrete Data  
12.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data 

12.2.2. According to their Shape   

12.2.2.1. Numeric  
12.2.2.2. Text:   
12.2.2.3. Logical  

12.2.3. According to its Source  

12.2.3.1. Primary  
12.2.3.2. Secondary  

12.3. Life Cycle of Data  

12.3.1. Stages of the Cycle  
12.3.2. Milestones of the Cycle  
12.3.3. FAIR Principles  

12.4. Initial Stages of the Cycle  

12.4.1. Definition of Goals  
12.4.2. Determination of Resource Requirements  
12.4.3. Gantt Chart  
12.4.4. Data Structure  

12.5. Data Collection  

12.5.1. Methodology of Data Collection  
12.5.2. Data Collection Tools  
12.5.3. Data Collection Channels  

12.6. Data Cleaning  

12.6.1. Phases of Data Cleansing  
12.6.2. Data Quality  
12.6.3. Data Manipulation (with R)  

12.7. Data Analysis, Interpretation and Result Evaluation  

12.7.1. Statistical Measures  
12.7.2. Relationship Indexes  
12.7.3. Data Mining  

12.8. Datawarehouse  

12.8.1. Elements that Comprise it  
12.8.2. Design  
12.8.3. Aspects to Consider  

12.9. Data Availability  

12.9.1. Access  
12.9.2. Uses  
12.9.3. Security  

12.10. Regulatory Framework 

12.10.1. Data Protection Law  
12.10.2. Good Practices  
12.10.3. Other Regulatory Aspects 

Module 13. Data in Artificial Intelligence

13.1. Data Science 

13.1.1. Data Science 
13.1.2. Advanced Tools for the Data Scientist 

13.2. Data, Information and Knowledge 

13.2.1. Data, Information and Knowledge  
13.2.2. Types of Data 
13.2.3. Data Sources 

13.3. From Data to Information  

13.3.1. Data Analysis 
13.3.2. Types of Analysis 
13.3.3. Extraction of Information from a Dataset 

13.4. Extraction of Information Through Visualization 

13.4.1. Visualization as an Analysis Tool 
13.4.2. Visualization Methods  
13.4.3. Visualization of a Data Set 

13.5. Data Quality 

13.5.1. Quality Data 
13.5.2. Data Cleaning  
13.5.3. Basic Data Pre-Processing 

13.6. Dataset 

13.6.1. Dataset Enrichment 
13.6.2. The Curse of Dimensionality 
13.6.3. Modification of Our Data Set 

13.7. Unbalance  

13.7.1. Classes of Unbalance 
13.7.2. Unbalance Mitigation Techniques 
13.7.3. Balancing a Dataset 

13.8. Unsupervised Models  

13.8.1. Unsupervised Model 
13.8.2. Methods 
13.8.3. Classification with Unsupervised Models 

13.9. Supervised Models 

13.9.1. Supervised Model 
13.9.2. Methods 
13.9.3. Classification with Supervised Models 

13.10. Tools and Good Practices 

13.10.1. Good Practices for Data Scientists 
13.10.2. The Best Model  
13.10.3. Useful Tools 

Module 14. Data Mining: Selection, Pre-Processing and Transformation

14.1. Statistical Inference 

14.1.1. Descriptive Statistics vs. Statistical Inference 
14.1.2. Parametric Procedures 
14.1.3. Non-Parametric Procedures 

14.2. Exploratory Analysis 

14.2.1. Descriptive Analysis  
14.2.2. Visualization 
14.2.3. Data Preparation 

14.3. Data Preparation 

14.3.1. Integration and Data Cleaning  
14.3.2. Normalization of Data 
14.3.3. Transforming Attributes  

14.4. Missing Values 

14.4.1. Treatment of Missing Values 
14.4.2. Maximum Likelihood Imputation Methods 
14.4.3. Missing Value Imputation Using Machine Learning 

14.5. Noise in the Data  

14.5.1. Noise Classes and Attributes 
14.5.2. Noise Filtering  
14.5.3. The Effect of Noise 

14.6. The Curse of Dimensionality 

14.6.1. Oversampling 
14.6.2. Undersampling 
14.6.3. Multidimensional Data Reduction 

14.7. From Continuous to Discrete Attributes 

14.7.1. Continuous Data Vs. Discreet Data 
14.7.2. Discretization Process 

14.8. The Data  

14.8.1. Data Selection  
14.8.2. Prospects and Selection Criteria 
14.8.3. Selection Methods  

14.9. Instance Selection 

14.9.1. Methods for Instance Selection 
14.9.2. Prototype Selection 
14.9.3. Advanced Methods for Instance Selection 

14.10. Data Pre-Processing in Big Data Environments 

Module 15. Algorithm and Complexity in Artificial Intelligence

15.1. Introduction to Algorithm Design Strategies 

15.1.1. Recursion 
15.1.2. Divide and Conquer 
15.1.3. Other Strategies 

15.2. Efficiency and Analysis of Algorithms 

15.2.1. Efficiency Measures 
15.2.2. Measuring the Size of the Input 
15.2.3. Measuring Execution Time 
15.2.4. Worst, Best and Average Case 
15.2.5. Asymptotic Notation 
15.2.6. Criteria for Mathematical Analysis of Non-Recursive Algorithms 
15.2.7. Mathematical Analysis of Recursive Algorithms 
15.2.8. Empirical Analysis of Algorithms 

15.3. Sorting Algorithms 

15.3.1. Concept of Sorting 
15.3.2. Bubble Sorting 
15.3.3. Sorting by Selection 
15.3.4. Sorting by Insertion 
15.3.5. Merge Sort 
15.3.6. Quick Sort 

15.4. Algorithms with Trees 

15.4.1. Tree Concept 
15.4.2. Binary Trees 
15.4.3. Tree Paths 
15.4.4. Representing Expressions 
15.4.5. Ordered Binary Trees 
15.4.6. Balanced Binary Trees 

15.5. Algorithms Using Heaps 

15.5.1. Heaps 
15.5.2. The Heapsort Algorithm 
15.5.3. Priority Queues 

15.6. Graph Algorithms 

15.6.1. Representation 
15.6.2. Traversal in Width 
15.6.3. Depth Travel 
15.6.4. Topological Sorting 

15.7. Greedy Algorithms 

15.7.1. Greedy Strategy 
15.7.2. Elements of the Greedy Strategy 
15.7.3. Currency Exchange 
15.7.4. Traveler’s Problem 
15.7.5. Backpack Problem 

15.8. Minimal Path Finding 

15.8.1. The Minimum Path Problem 
15.8.2. Negative Arcs and Cycles 
15.8.3. Dijkstra's Algorithm 

15.9. Greedy Algorithms on Graphs 

15.9.1. The Minimum Covering Tree 
15.9.2. Prim's Algorithm 
15.9.3. Kruskal’s Algorithm 
15.9.4. Complexity Analysis 

15.10. Backtracking 

15.10.1. Backtracking 
15.10.2. Alternative Techniques 

Module 16. Intelligent Systems

16.1. Agent Theory 

16.1.1. Concept History 
16.1.2. Agent Definition 
16.1.3. Agents in Artificial Intelligence 
16.1.4. Agents in Software Engineering 

16.2. Agent Architectures 

16.2.1. The Reasoning Process of an Agent 
16.2.2. Reactive Agents 
16.2.3. Deductive Agents 
16.2.4. Hybrid Agents 
16.2.5. Comparison 

16.3. Information and Knowledge 

16.3.1. Difference between Data, Information and Knowledge 
16.3.2. Data Quality Assessment 
16.3.3. Data Collection Methods 
16.3.4. Information Acquisition Methods 
16.3.5. Knowledge Acquisition Methods 

16.4. Knowledge Representation 

16.4.1. The Importance of Knowledge Representation 
16.4.2. Definition of Knowledge Representation According to Roles 
16.4.3. Knowledge Representation Features 

16.5. Ontologies 

16.5.1. Introduction to Metadata 
16.5.2. Philosophical Concept of Ontology 
16.5.3. Computing Concept of Ontology 
16.5.4. Domain Ontologies and Higher-Level Ontologies 
16.5.5. How to Build an Ontology? 

16.6. Ontology Languages and Ontology Creation Software 

16.6.1. Triple RDF, Turtle and N 
16.6.2. RDF Schema 
16.6.3. OWL 
16.6.4. SPARQL 
16.6.5. Introduction to Ontology Creation Tools 
16.6.6. Installing and Using Protégé 

16.7. Semantic Web 

16.7.1. Current and Future Status of the Semantic Web 
16.7.2. Semantic Web Applications 

16.8. Other Knowledge Representation Models 

16.8.1. Vocabulary 
16.8.2. Global Vision 
16.8.3. Taxonomy 
16.8.4. Thesauri 
16.8.5. Folksonomy 
16.8.6. Comparison 
16.8.7. Mind Maps 

16.9. Knowledge Representation Assessment and Integration 

16.9.1. Zero-Order Logic 
16.9.2. First-Order Logic 
16.9.3. Descriptive Logic 
16.9.4. Relationship between Different Types of Logic 
16.9.5. Prolog: Programming Based on First-Order Logic 

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

16.10.1. Concept of Reasoner 
16.10.2. Reasoner Applications 
16.10.3. Knowledge-Based Systems 
16.10.4. MYCIN: History of Expert Systems 
16.10.5. Expert Systems Elements and Architecture 
16.10.6. Creating Expert Systems 

Module 17. Machine Learning and Data Mining 

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

17.1.1. Key Concepts of Knowledge Discovery Processes 
17.1.2. Historical Perspective of Knowledge Discovery Processes 
17.1.3. Stages of the Knowledge Discovery Processes 
17.1.4. Techniques Used in Knowledge Discovery Processes 
17.1.5. Characteristics of Good Machine Learning Models 
17.1.6. Types of Machine Learning Information 
17.1.7. Basic Learning Concepts 
17.1.8. Basic Concepts of Unsupervised Learning 

17.2. Data Exploration and Pre-Processing 

17.2.1. Data Processing 
17.2.2. Data Processing in the Data Analysis Flow 
17.2.3. Types of Data 
17.2.4. Data Transformations 
17.2.5. Visualization and Exploration of Continuous Variables 
17.2.6. Visualization and Exploration of Categorical Variables 
17.2.7. Correlation Measures 
17.2.8. Most Common Graphic Representations 
17.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction 

17.3. Decision Trees 

17.3.1. ID Algorithm 
17.3.2. Algorithm C 
17.3.3. Overtraining and Pruning 
17.3.4. Result Analysis 

17.4. Evaluation of Classifiers 

17.4.1. Confusion Matrixes 
17.4.2. Numerical Evaluation Matrixes 
17.4.3. Kappa Statistic 
17.4.4. ROC Curves 

17.5. Classification Rules 

17.5.1. Rule Evaluation Measures 
17.5.2. Introduction to Graphic Representation 
17.5.3. Sequential Overlay Algorithm 

17.6. Neural Networks 

17.6.1. Basic Concepts 
17.6.2. Simple Neural Networks 
17.6.3. Backpropagation Algorithm 
17.6.4. Introduction to Recurrent Neural Networks 

17.7. Bayesian Methods 

17.7.1. Basic Probability Concepts 
17.7.2. Bayes' Theorem 
17.7.3. Naive Bayes 
17.7.4. Introduction to Bayesian Networks 

17.8. Regression and Continuous Response Models 

17.8.1. Simple Linear Regression 
17.8.2. Multiple Linear Regression 
17.8.3. Logistic Regression 
17.8.4. Regression Trees 
17.8.5. Introduction to Support Vector Machines (SVM) 
17.8.6. Goodness-of-Fit Measures 

17.9. Clustering 

17.9.1. Basic Concepts 
17.9.2. Hierarchical Clustering 
17.9.3. Probabilistic Methods 
17.9.4. EM Algorithm 
17.9.5. B-Cubed Method 
17.9.6. Implicit Methods 

17.10. Text Mining and Natural Language Processing (NLP) 

17.10.1. Basic Concepts 
17.10.2. Corpus Creation 
17.10.3. Descriptive Analysis 
17.10.4. Introduction to Feelings Analysis

Module 18. Neural Networks, the Basis of Deep Learning

18.1. Deep Learning 

18.1.1. Types of Deep Learning 
18.1.2. Applications of Deep Learning 
18.1.3. Advantages and Disadvantages of Deep Learning 

18.2. Surgery 

18.2.1. Sum 
18.2.2. Product 
18.2.3. Transfer 

18.3. Layers 

18.3.1. Input Layer 
18.3.2. Cloak 
18.3.3. Output Layer 

18.4. Union of Layers and Operations 

18.4.1. Architecture Design 
18.4.2. Connection between Layers 
18.4.3. Forward Propagation 

18.5. Construction of the First Neural Network 

18.5.1. Network Design 
18.5.2. Establish the Weights 
18.5.3. Network Training 

18.6. Trainer and Optimizer 

18.6.1. Optimizer Selection 
18.6.2. Establishment of a Loss Function 
18.6.3. Establishing a Metric 

18.7. Application of the Principles of Neural Networks 

18.7.1. Activation Functions 
18.7.2. Backward Propagation 
18.7.3. Parameter Adjustment 

18.8. From Biological to Artificial Neurons 

18.8.1. Functioning of a Biological Neuron 
18.8.2. Transfer of Knowledge to Artificial Neurons 
18.8.3. Establish Relations Between the Two 

18.9. Implementation of MLP (Multilayer Perceptron) with Keras 

18.9.1. Definition of the Network Structure 
18.9.2. Model Compilation 
18.9.3. Model Training 

18.10. Fine Tuning  Hyperparameters of Neural Networks 

18.10.1. Selection of the Activation Function 
18.10.2. Set the Learning Rate 
18.10.3. Adjustment of Weights 

Module 19. Deep Neural Networks Training

19.1. Gradient Problems 

19.1.1. Gradient Optimization Techniques 
19.1.2. Stochastic Gradients 
19.1.3. Weight Initialization Techniques 

19.2. Reuse of Pre-Trained Layers 

19.2.1. Learning Transfer Training 
19.2.2. Feature Extraction 
19.2.3. Deep Learning 

19.3. Optimizers 

19.3.1. Stochastic Gradient Descent Optimizers 
19.3.2. Optimizers Adam and RMSprop 
19.3.3. Moment Optimizers 

19.4. Programming of the Learning Rate 

19.4.1. Automatic Learning Rate Control 
19.4.2. Learning Cycles 
19.4.3. Smoothing Terms 

19.5. Overfitting 

19.5.1. Cross Validation 
19.5.2. Regularization 
19.5.3. Evaluation Metrics 

19.6. Practical Guidelines 

19.6.1. Model Design 
19.6.2. Selection of Metrics and Evaluation Parameters 
19.6.3. Hypothesis Testing 

19.7. Transfer Learning 

19.7.1. Learning Transfer Training 
19.7.2. Feature Extraction 
19.7.3. Deep Learning 

19.8. Data Augmentation 

19.8.1. Image Transformations 
19.8.2. Synthetic Data Generation 
19.8.3. Text Transformation 

19.9. Practical Application of Transfer Learning 

19.9.1. Learning Transfer Training 
19.9.2. Feature Extraction 
19.9.3. Deep Learning 

19.10. Regularization 

19.10.1. L and L 
19.10.2. Regularization by Maximum Entropy 
19.10.3. Dropout 

Module 20. Model Customization and Training with TensorFlow

20.1. TensorFlow 

20.1.1. Use of the TensorFlow Library 
20.1.2. Model Training with TensorFlow 
20.1.3. Operations with Graphs in TensorFlow 

20.2. TensorFlow and NumPy 

20.2.1. NumPy Computing Environment for TensorFlow 
20.2.2. Using NumPy Arrays with TensorFlow 
20.2.3. NumPy Operations for TensorFlow Graphs 

20.3. Model Customization and Training Algorithms 

20.3.1. Building Custom Models with TensorFlow 
20.3.2. Management of Training Parameters 
20.3.3. Use of Optimization Techniques for Training 

20.4. TensorFlow Features and Graphs 

20.4.1. Functions with TensorFlow 
20.4.2. Use of Graphs for Model Training 
20.4.3. Graphs Optimization with TensorFlow Operations 

20.5. Loading and Preprocessing Data with TensorFlow 

20.5.1. Loading Data Sets with TensorFlow 
20.5.2. Preprocessing Data with TensorFlow 
20.5.3. Using TensorFlow Tools for Data Manipulation 

20.6. The tfdata API 

20.6.1. Using the Tf.data API for Data Processing 
20.6.2. Construction of Data Streams with tf.data 
20.6.3. Using the Tf.data API for Model Training 

20.7. The TFRecord Format 

20.7.1. Using the TFRecord API for Data Serialization 
20.7.2. Loading TFRecord Files with TensorFlow 
20.7.3. Using TFRecord Files for Model Training 

20.8. Keras Preprocessing Layers 

20.8.1. Using the Keras Preprocessing API 
20.8.2. Preprocessing Pipelined Construction with Keras 
20.8.3. Using the Keras Preprocessing API for Model Training

 20.9. The TensorFlow Datasets Project 

20.9.1. Using TensorFlow Datasets for Data Loading 
20.9.2. Data Preprocessing with TensorFlow Datasets 
20.9.3. Using TensorFlow Datasets for Model Training 

20.10. Building a Deep Learning App with TensorFlow 

20.10.1. Practical Application 
20.10.2. Building a Deep Learning App with TensorFlow 
20.10.3. Model Training with TensorFlow 
20.10.4. Use of the Application for the Prediction of Results 

Module 21. Deep Computer Vision with Convolutional Neural Networks

21.1. The Visual Cortex Architecture 

21.1.1. Functions of the Visual Cortex 
21.1.2. Theories of Computational Vision 
21.1.3. Models of Image Processing 

21.2. Convolutional Layers 

21.2.1. Reuse of Weights in Convolution 
21.2.2. Convolution D 
21.2.3. Activation Functions 

21.3. Grouping Layers and Implementation of Grouping Layers with Keras 

21.3.1. Pooling and Striding 
21.3.2. Flattening 
21.3.3. Types of Pooling 

21.4. CNN Architecture 

21.4.1. VGG Architecture 
21.4.2. AlexNet Architecture 
21.4.3. ResNet Architecture 

21.5. Implementing a CNN ResNet- using Keras 

21.5.1. Weight Initialization 
21.5.2. Input Layer Definition 
21.5.3. Output Definition 

21.6. Use of Pre-Trained Keras Models 

21.6.1. Characteristics of Pre-Trained Models 
21.6.2. Uses of Pre-trained Models 
21.6.3. Advantages of Pre-trained Models 

21.7. Pre-Trained Models for Transfer Learning 

21.7.1. Transfer Learning 
21.7.2. Transfer Learning Process 
21.7.3. Advantages of Transfer Learning 

21.8. Deep Computer Vision Classification and Localization 

21.8.1. Image Classification 
21.8.2. Localization of Objects in Images 
21.8.3. Object Detection 

21.9. Object Detection and Object Tracking 

21.9.1. Object Detection Methods 
21.9.2. Object Tracking Algorithms 
21.9.3. Tracking and Localization Techniques 

21.10. Semantic Segmentation 

21.10.1. Deep Learning for Semantic Segmentation 
21.10.1. Edge Detection 
21.10.1. Rule-Based Segmentation Methods 

Module 22. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

22.1. Text Generation using RNN 

22.1.1. Training an RNN for Text Generation 
22.1.2. Natural Language Generation with RNN 
22.1.3. Text Generation Applications with RNN 

22.2. Training Data Set Creation 

22.2.1. Preparation of the Data for Training an RNN 
22.2.2. Storage of the Training Dataset 
22.2.3. Data Cleaning and Transformation 
22.2.4. Sentiment Analysis 

22.3. Classification of Opinions with RNN 

22.3.1. Detection of Themes in Comments 
22.3.2. Sentiment Analysis with Deep Learning Algorithms 

22.4. Encoder-Decoder Network for Neural Machine Translation 

22.4.1. Training an RNN for Machine Translation 
22.4.2. Use of an Encoder-Decoder Network for Machine Translation 
22.4.3. Improving the Accuracy of Machine Translation with RNNs 

22.5. Attention Mechanisms 

22.5.1. Application of Care Mechanisms in RNN 
22.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models 
22.5.3. Advantages of Attention Mechanisms in Neural Networks 

22.6. Transformer Models 

22.6.1. Using Transformers Models for Natural Language Processing 
22.6.2. Application of Transformers Models for Vision 
22.6.3. Advantages of Transformers Models 

22.7. Transformers for Vision 

22.7.1. Use of Transformers models for vision 
22.7.2. Image Data Preprocessing 
22.7.3. Training a TransformersModel for Vision 

22.8. Hugging Face’s Transformers Bookstore 

22.8.1. Using the Hugging Face Transformers Library 
22.8.2. Hugging Face´s Transformers Library Application 
22.8.3. Advantages of Hugging Face´s Transformers Library 

22.9. Other Transformers Libraries. Comparison 

22.9.1. Comparison Between Different Transformers Libraries 
22.9.2. Use of the Other Transformers Libraries 
22.9.3. Advantages of the Other Transformers Libraries 

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

22.10.1. Development of a Natural Language Processing Application with RNN and Attention. 
22.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application 
22.10.3. Evaluation of the Practical Application 

Module 23. Autoencoders, GANs, and Diffusion Models

23.1. Representation of Efficient Data 

23.1.1. Dimensionality Reduction 
23.1.2. Deep Learning 
23.1.3. Compact Representations 

23.2. PCA Realization with an Incomplete Linear Automatic Encoder 

23.2.1. Training Process 
23.2.2. Implementation in Python 
23.2.3. Use of Test Data 

23.3. Stacked Automatic Encoders 

23.3.1. Deep Neural Networks 
23.3.2. Construction of Coding Architectures 
23.3.3. Use of Regularization 

23.4. Convolutional Autoencoders 

23.4.1. Design of Convolutional Models 
23.4.2. Convolutional Model Training 
23.4.3. Results Evaluation 

23.5. Noise Suppression of Automatic Encoders 

23.5.1. Filter Application 
23.5.2. Design of Coding Models 
23.5.3. Use of Regularization Techniques 

23.6. Sparse Automatic Encoders 

23.6.1. Increasing Coding Efficiency 
23.6.2. Minimizing the Number of Parameters 
23.6.3. Using Regularization Techniques 

23.7. Variational Automatic Encoders 

23.7.1. Use of Variational Optimization 
23.7.2. Unsupervised Deep Learning 
23.7.3. Deep Latent Representations 

23.8. Generation of Fashion MNIST Images 

23.8.1. Pattern Recognition 
23.8.2. Image Generation 
23.8.3. Deep Neural Networks Training 

23.9. Generative Adversarial Networks and Diffusion Models 

23.9.1. Content Generation from Images 
23.9.2. Modeling of Data Distributions 
23.9.3. Use of Adversarial Networks 

23.10. Implementation of the Models 

23.10.1. Practical Application 
23.10.2. Implementation of the Models 
23.10.3. Use of Real Data 
23.10.4. Results Evaluation 

Module 24. Bio-Inspired Computing 

24.1. Introduction to Bio-Inspired Computing 

24.1.1. Introduction to Bio-Inspired Computing 

24.2. Social Adaptation Algorithms 

24.2.1. Bio-Inspired Computation Based on Ant Colonies 
24.2.2. Variants of Ant Colony Algorithms 
24.2.3. Particle Cloud Computing 

24.3. Genetic Algorithms 

24.3.1. General Structure 
24.3.2. Implementations of the Major Operators 

24.4. Space Exploration-Exploitation Strategies for Genetic Algorithms 

24.4.1. CHC Algorithm 
24.4.2. Multimodal Problems 

24.5. Evolutionary Computing Models (I) 

24.5.1. Evolutionary Strategies 
24.5.2. Evolutionary Programming 
24.5.3. Algorithms Based on Differential Evolution 

24.6. Evolutionary Computation Models (II) 

24.6.1. Evolutionary Models Based on Estimation of Distributions (EDA) 
24.6.2. Genetic Programming 

24.7. Evolutionary Programming Applied to Learning Problems 

24.7.1. Rules-Based Learning 
24.7.2. Evolutionary Methods in Instance Selection Problems 

24.8. Multi-Objective Problems 

24.8.1. Concept of Dominance 
24.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems 

24.9. Neural Networks (I) 

24.9.1. Introduction to Neural Networks 
24.9.2. Practical Example with Neural Networks 

24.10. Neural Networks (II) 

24.10.1. Use Cases of Neural Networks in Medical Research 
24.10.2. Use Cases of Neural Networks in Economics 
24.10.3. Use Cases of Neural Networks in Artificial Vision 

Module 25. Artificial Intelligence: Strategies and Applications 

25.1. Financial Services 

25.1.1. The Implications of Artificial Intelligence (AI) in Financial Services  Opportunities and Challenges  
25.1.2. Case Uses  
25.1.3. Potential Risks Related to the Use of AI 
25.1.4. Potential Future Developments/Uses of AI 

25.2. Implications of Artificial Intelligence in the Healthcare Service  

25.2.1. Implications of AI in the Healthcare Sector Opportunities and Challenges  
25.2.2. Case Uses 

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

25.3.1. Potential Risks Related to the Use of AI 
25.3.2. Potential Future Developments/Uses of AI  

25.4. Retail  

25.4.1. Implications of AI in the Retail. Opportunities and Challenges  
25.4.2. Case Uses  
25.4.3. Potential Risks Related to the Use of AI  
25.4.4. Potential Future Developments/Uses of AI 

25.5. Industry   

25.5.1. Implications of AI in Industry Opportunities and Challenges 
25.5.2. Case Uses 

25.6. Potential Risks Related to the Use of AI in Industry   

25.6.1. Case Uses 
25.6.2. Potential Risks Related to the Use of AI 
25.6.3. Potential Future Developments/Uses of AI  

25.7. Public Administration  

25.7.1. AI Implications for Public Administration Opportunities and Challenges 
25.7.2. Case Uses  
25.7.3. Potential Risks Related to the Use of AI  
25.7.4. Potential Future Developments/Uses of AI 

 25.8. Educational  

25.8.1. AI Implications for Education Opportunities and Challenges 
25.8.2. Case Uses  
25.8.3. Potential Risks Related to the Use of AI  
25.8.4. Potential Future Developments/Uses of AI 

25.9. Forestry and Agriculture  

25.9.1. Implications of AI in Forestry and Agriculture. Opportunities and Challenges  
25.9.2. Case Uses 
25.9.3. Potential Risks Related to the Use of AI 
25.9.4. Potential Future Developments/Uses of AI  

25.10. Human Resources  

25.10.1. Implications of AI for Human Resources Opportunities and Challenges 
25.10.2. Case Uses  
25.10.3. Potential Risks Related to the Use of AI  
25.10.4. Potential Future Developments/Uses of AI 

Module 26. Bio-Inspired Computing 

26.1. Preparing a Suitable Development Environment 

26.1.1. Essential Tools Selection for AI Development
26.1.2. Configuration of the Selected Tools 
26.1.3. Implementation of CI/CD Pipelines Adapted to AI Projects 
26.1.4. Efficient Management of Dependencies and Versions in Development Environments 

26.2. Essential AI Extensions for Visual Studio Code

26.2.1. Exploring and Selecting AI Extensions for Visual Studio Code 
26.2.2. Integrating Static and Dynamic Analysis Tools into the Integrated Development Environment (IDE) 
26.2.3. Automation of Repetitive Tasks with Specific Extensions 
26.2.4. Customization of the Development Environment to Improve Efficiency 

26.3. No-Code User Interface Design with Flutterflow 

26.3.1. No-Code Design Principles and their Application to User Interfaces 
26.3.2. Incorporation of AI Elements in Visual Interface Design 
26.3.3. Tools and Platforms for the No-Code Creation of Intelligent Interfaces 
26.3.4. Evaluation and Continuous Improvement of No-code Interfaces with AI 

26.4. Code Optimization Using ChatGPT 

26.4.1. Duplicate Code Detection
26.4.2. Refactor 
26.4.3. Create Readable Code
26.4.4. Understanding What Code Does
26.4.5. Improving Variable and Function Naming
26.4.6. Creating Automatic Documentation

26.5. Repository Management with AI using CHATGPT 

26.5.1. Automation of Version Control Processes with AI Techniques 
26.5.2. Conflict Detection and Automatic Resolution in Collaborative Environments
26.5.3. Predictive Analysis of Changes and Trends in Code Repositories 
26.5.4. Improvements in the Organization and Categorization of Repositories using AI 

26.6. Integration of AI in Database Management with AskYourDatabase

26.6.1. Optimization of Queries and Performance Using AI Techniques 
26.6.2. Predictive Analysis of Database Access Patterns 
26.6.3. Implementation of Recommender Systems to Optimize Database Structure 
26.6.4. Proactive Monitoring and Detection of Potential Database Problems 

26.7. Fault Detection and Creation of Unit Tests with AI ChatGPT

26.7.1. Automatic Generation of Test Cases using AI Techniques 
26.7.2. Early Detection of Vulnerabilities and Bugs using Static Analysis with AI 
26.7.3. Improving Test Coverage by Identifying Critical Areas by AI 

26.8. Pair Programming with GitHub Copilot

26.8.1. Integration and Effective Use of GitHub Copilot in Pair Programming Sessions 
26.8.2. Integration Improvements in Communication and Collaboration among Developers with GitHub Copilot 
26.8.3. Integration Strategies to Maximize the Use of GitHub Copilot-Generated Code Suggestions 
26.8.4. Integration of Case Studies and Best Practices in AI-Assisted Pair Programming 

26.9. Automatic Translation between Programming Languages ChatGPT 

26.9.1. Specific Machine Translation Tools and Services for Programming Languages 
26.9.2. Adaptation of Machine Translation Algorithms to Development Contexts 
26.9.3. Improvement of Interoperability between Different Languages by Machine Translation 
26.9.4. Assessment and Mitigation of Potential Challenges and Limitations in Machine Translation

26.10. Recommended AI Tools to Improve Productivity

26.10.1. Comparative Analysis of AI Tools for Software Development 
26.10.2. Integration of AI Tools in Workflows. 
26.10.3. Automation of Routine Tasks with AI Tools
26.10.4. Evaluation and Selection of Tools Based on Project Context and Requirements 

Module 27. Software Architecture with AI

27.1. Optimization and Performance Management in AI Tools with the help of ChatGPT 

27.1.1. Performance Analysis and Profiling in AI Tools 
27.1.2. Algorithm Optimization Strategies and AI Models 
27.1.3. Implementation of Caching and Parallelization Techniques to Improve Performance 
27.1.4. Tools and Methodologies for Continuous Real-Time Performance Monitoring 

27.2. Scalability in AI Applications Using ChatGPT

27.2.1. Scalable Architectures Design for AI Applications 
27.2.2. Implementation of Partitioning and Load Sharing Techniques 
27.2.3. Work Flow and Workload Management in Scalable Systems 
27.2.4. Strategies for Horizontal and Vertical Expansion in Variable Demand Environments 

27.3. Maintainability of AI Applications Using ChatGPT 

27.3.1. Design Principles to Facilitate Maintainability in IA Projects 
27.3.2. Specific Documentation Strategies for AI Models and Algorithms 
27.3.3. Implementation of Unit and Integration Tests to Facilitate Maintainability
27.3.4. Methods for Refactoring and Continuous Improvement in Systems with AI Components

27.4. Large-Scale System Design

27.4.1. Architectural Principles for Large-Scale System Design 
27.4.2. Decomposition of Complex Systems into Microservices 
27.4.3. Implementation of Specific Design Patterns for Distributed Systems 
27.4.4. Strategies for Complexity Management in Large-Scale Architectures with AI Components

27.5. Large-Scale Data Warehousing for AI Tools

27.5.1. Selection of Scalable Data Storage Technologies 
27.5.2. Design of Database Schemas for Efficient Handling of Large Data Volumes 
27.5.3. Partitioning and Replication Strategies in Massive Data Storage Environments 
27.5.4. Implementation of Data Management Systems to Ensure Integrity and Availability in AI Projects 

27.6. Data Structures with AI Using ChatGPT 

27.6.1. Adaptation of Classical Data Structures for Use with AI Algorithms 
27.6.2. Design and Optimization of Specific Data Structures with ChatGPT 
27.6.3. Integration of Efficient Data Structures in Data Intensive Systems 
27.6.4. Strategies for Real-Time Data Manipulation and Storage in AI Data Structures

27.7. Programming Algorithms for AI Products

27.7.1. Development and Implementation of Application-Specific Algorithms for AI Applications 
27.7.2. Algorithm Selection Strategies according to Problem Type and Product Requirements 
27.7.3. Adaptation of Classical Algorithms for Integration into AI Systems 
27.7.4. Evaluation and Performance Comparison between Different Algorithms in Development Contexts with AI

27.8. Design Patterns for AI Development 

27.8.1. Identification and Application of Common Design Patterns in Projects with AI Components 
27.8.2. Development of Specific Patterns for the Integration of Models and Algorithms into Existing Systems 
27.8.3. Strategies for the Implementation of Patterns to Improve Reusability and Maintainability in AI Projects 
27.8.4. Case Studies and Best Practices in the Application of Design Patterns in AI Architectures 

27.9. Implementation of Clean Architecture using ChatGPT

27.9.1. Fundamental Principles and Concepts of Clean Architecture
27.9.2. Adaptation of Clean Architecture to Projects with AI Components 
27.9.3. Implementation of Layers and Dependencies in Systems with Clean Architecture 
27.9.4. Benefits and Challenges of Implementing Clean Architecture in Software Development with AI 

27.10. Secure Software Development in Web Applications with DeepCode

27.10.1. Principles of Security in the Development of Software with AI Components 
27.10.2. Identification and Mitigation of Potential Vulnerabilities in AI Models and Algorithms 
27.10.3. Implementation of Secure Development Practices in Web Applications with Artificial Intelligence Functionalities 
27.10.4. Strategies for the Protection of Sensitive Data and Prevention of Attacks in AI Projects 

Module 28. Website Projects with AI

28.1. Working Environment Preparation for Web Development with AI

28.1.1. Configuration of Web Development Environments for Projects with Artificial Intelligence 
28.1.2. Selection and Preparation of Essential Tools for Web Development with AI 
28.1.3. Integration of Specific Libraries and Frameworks for Web Projects with Artificial Intelligence 
28.1.4. Implementation of Best Practices in the Configuration of Collaborative Development Environments 

28.2. Workspace Creation for AI Projects with GitHub Copilot

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

28.3. Design Patterns in GitHub Copilot Products 

28.3.1. Identification and Application of Common Design Patterns in User Interfaces with Artificial Intelligence Elements 
28.3.2. Development of Specific Patterns to Improve the User Experience in AI Web Projects 
28.3.3. Integration of Design Patterns in the Overall Architecture of Web Projects with Artificial Intelligence 
28.3.4. Evaluation and Selection of Appropriate Design Patterns According to the Project's Context 

28.4. Frontend Development with GitHub Copilot

28.4.1. Integration of AI Models in the Presentation Layer of Web Projects  
28.4.2. Development of Adaptive User Interfaces with Artificial Intelligence Elements 
28.4.3. Implementation of Natural Language Processing (NLP) Functionalities in Frontend Development 
28.4.4. Strategies for Performance Optimization in Frontend Development with AI

28.5. Database Creation using GitHub Copilot  

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

28.6. Back-End Development with GitHub Copilot

28.6.1. Integration of AI Services and Models in the Back-End Business Logic 
28.6.2. Development of Specific APIs and Endpoints for Communication between Front-End and AI Components 
28.6.3. Implementation of Data Processing and Decision-Making Logic in the Backend with Artificial Intelligence 
28.6.4. Strategies for Scalability and Performance in Back-End Development of Web Projects with AI 

28.7. Optimization of the Deployment Process of Your Website 

28.7.1. Automation of Web Project Build and Deployment Processes with ChatGPT  
28.7.2. Implementing CI/CD Pipelines Tailored to Web Applications with GitHub Copilot  
28.7.3. Strategies for Efficient Release and Upgrade Management in Continuous Deployments 
28.7.4. Post-Deployment Monitoring and Analysis for Continuous Process Improvement

28.8. AI in Cloud Computing

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

28.9. Creating an AI Project for LAMP Environments with the Help of ChatGPT

28.9.1. Adaptation of Web Projects Based on the LAMP Stack to Include Artificial Intelligence Components 
28.9.2. Integration of AI-specific Libraries and Frameworks in LAMP Environments 
28.9.3. Development of AI Functionalities that Complement the Traditional LAMP Architecture
28.9.4. Strategies for Optimization and Maintenance in Web Projects with AI in LAMP Environments 

28.10. Creating an AI Project for MEVN Environments Using ChatGPT 

28.10.1. Integration of MEVN Stack Technologies and Tools with Artificial Intelligence Components 
28.10.2. Development of Modern and Scalable Web Applications in MEVN Environments with AI Capabilities 
28.10.3. Implementation of Data Processing and Machine Learning functionalities in MEVN Projects 
28.10.4. Strategies for Performance and Security Enhancement of AI-Enabled Web Applications in MEVN Environments 

Module 29. Mobile Applications with AI  

29.1. Working Environment Preparation for Mobile Development with AI

29.1.1. Configuration of Mobile Development Environments for Projects with Artificial Intelligence
29.1.2. Selection and Preparation of Specific Tools for Mobile Application Development with AI 
29.1.3. Integration of AI-Libraries and Frameworks in Mobile Development Environments 
29.1.4. Configuration of Emulators and Real Devices for Testing Mobile Applications with AI Components 

29.2. Creation of a Workspace with GitHub Copilot

29.2.1. Integration of GitHub Copilot in Mobile Development Environments 
29.2.2. Effective Use of GitHub Copilot for Code Generation in AI Projects 
29.2.3. Strategies for Developer Collaboration when Using GitHub Copilot in the Workspace 
29.2.4. Best Practices and Limitations in the Use of GitHub Copilot in Mobile Application Development with AI

29.3. Firebase Configuration

29.3.1. Initial Configuration of a Firebase Project for Mobile Development 
29.3.2. Firebase Integration in Mobile Applications with Artificial Intelligence Functionality 
29.3.3. Use of Firebase Services as Database, Authentication, and Notifications in AI projects 
29.3.4. Strategies for Real-Time Data and Event Management in Firebase-Enabled Mobile Applications

29.4. Concepts of Clean Architecture, DataSources, Repositories

29.4.1. Fundamental Principles of Clean Architecture in Mobile Development with AI 
29.4.2. Implementation of DataSources and Repositories Layers with GitHub Copilot
29.4.3. Design and Structuring of Components in Mobile Projects with Github Copilot 
29.4.4. Benefits and Challenges of Implementing Clean Architecture in Mobile Applications with AI 

29.5. Creating Authentication Screen with GitHub Copilot

29.5.1. Design and Development of User Interfaces for Authentication Screens in Mobile Applications with IA 
29.5.2. Integration of Authentication Services with Firebase in the Login Screen
29.5.3. Use of Security and Data Protection Techniques in the Authentication Screen 
29.5.4. Personalization and Customization of the User Experience in the Authentication Screen 

29.6. Creating Dashboard and Navigation with GitHub Copilot

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

29.7. Listing Screen Creation using GitHub Copilot 

29.7.1. Development of User Interfaces for Listing Screens in AI-Enabled Mobile Applications 
29.7.2. Integration of Recommendation and Filtering Algorithms into the Listing Screen 
29.7.3. Use of Design Patterns for Effective Presentation of Data in the Listing Screen 
29.7.4. Strategies for Efficient Loading of Real-Time Data into the Listing Screen 

29.8. Creating Details Screen with GitHub Copilot 

29.8.1. Design and Development of Detailed User Interfaces for the Presentation of Specific Information
29.8.2. Integration of AI Functionalities to Enrich the Detailed Screen 
29.8.3. Implementation of Interactions and Animations in the Detailed Screen 
29.8.4. Strategies for Performance Optimization in Loading and Detail Display in AI-Enabled Mobile Applications 

29.9. Creating a Settings Screen with GitHub Copilot 

29.9.1. Development of User Interfaces for Configuration and Settings in AI-Enabled Mobile Applications 
29.9.2. Integration of Customized Settings Related to Artificial Intelligence Components 
29.9.3. Implementation of Customized Options and Preferences in the Settings Screen 
29.9.4. Strategies for Usability and Clarity in the Presentation of Options in the Settings Screen 

29.10. Creation of Icons, Splash and Graphic Resources for Your App with AI

29.10.1. Design and Creation of Attractive Icons to Represent the AI Mobile Application 
29.10.2. Development of Splash Screens with Impactful Visuals 
29.10.3. Selection and Adaptation of Graphic Resources to Enhance the Aesthetics of the Mobile Application 
29.10.4. Strategies for Consistency and Visual Branding in the Graphic Elements of the Application with AI 

Module 30. AI for QA Testing

30.1. Software Testing Life Cycle

30.1.1. Description and Understanding of the Testing Life Cycle in Software Development
30.1.2. Phases of the Testing Life Cycle and its Importance in Quality Assurance 
30.1.3. Integration of Artificial Intelligence in Different Stages of the Testing Life Cycle 
30.1.4. Strategies for Continuous Improvement of the Testing Life Cycle using AI 

30.2. Test Cases and Bug Detection with the Help of ChatGPT

30.2.1. Effective Test Case Design and Writing in the Context of QA Testing 
30.2.2. Identification of Bugs and Errors during Test Case Execution 
30.2.3. Application of Early Bug Detection Techniques using Static Analysis 
30.2.4. Use of Artificial Intelligence Tools for the Automatic Identification of Bugs in Test Cases

30.3. Types of Testing

30.3.1. Exploration of Different Types of Testing in the QA Environment 
30.3.2. Unit, Integration, Functional, and Acceptance Testing: Characteristics and Applications 
30.3.3. Strategies for the Selection and Appropriate Combination of Testing Types in Projects with ChatGPT 
30.3.4. Adaptation of Conventional Testing Types to Projects with ChatGPT 

30.4. Creation of a Testing Plan Using ChatGPT 

30.4.1. Design and Structure of a Comprehensive Testing Plan 
30.4.2. Identification of Requirements and Test Scenarios in AI Projects 
30.4.3. Strategies for Manual and Automated Test Planning 
30.4.4. Continuous Evaluation and Adjustment of the Testing Plan as the Project Develops

30.5. AI Bug Detection and Reporting

30.5.1. Implementation of Automatic Bug Detection Techniques using Machine Learning Algorithms
30.5.2. Use of ChatGPT for Dynamic Code Analysis to Search for Possible Bugs 
30.5.3. Strategies for Automatic Generation of Detailed Reports on Bugs Detected Using ChatGPT 
30.5.4. Effective Collaboration between Development and QA Teams in the Management of AI-Detected Bugs

30.6. Creation of Automated Testing with AI

30.6.1. Development of Automated Test Scripts for Projects Using ChatGPT 
30.6.2. Integration of AI-Based Test Automation Tools
30.6.3. Using ChatGPT for Dynamic Generation of Automated Test Cases 
30.6.4. Strategies for Efficient Execution and Maintenance of Automated Test Cases in AI Projects

30.7. API Testing

30.7.1. Fundamental Concepts of API Testing and its Importance in QA 
30.7.2. Development of Tests for the Verification of APIs in Environments Using ChatGPT 
30.7.3. Strategies for Data and Results Validation in API Testing with ChatGPT 
30.7.4. Use of Specific Tools for API Testing in Projects with Artificial Intelligence

30.8. AI Tools for Web Testing

30.8.1. Exploration of Artificial Intelligence Tools for Test Automation in Web Environments 
30.8.2. Integration of Element Recognition and Visual Analysis Technologies in Web Testing 
30.8.3. Strategies for Automatic Detection of Changes and Performance Problems in Web Applications Using ChatGPT 
30.8.4. Evaluation of Specific Tools for Improving Efficiency in Web Testing with AI

30.9. Mobile Testing Using AI

30.9.1. Development of Testing Strategies for Mobile Applications with AI Components 
30.9.2. Integration of Specific Testing Tools for AI-Based Mobile Platforms 
30.9.3. Use of ChatGPT for Detecting Performance Problems in Mobile Applications 
30.9.4. Strategies for the Validation of Interfaces and Specific Functions of Mobile Applications by AI 

30.10. QA Tools with AI

30.10.1. Exploration of QA Tools and Platforms that Incorporate Artificial Intelligence Functionality
30.10.2. Evaluation of Tools for Efficient Test Management and Test Execution in AI Projects 
30.10.3. Using ChatGPT for the Generation and Optimization of Test Cases 
30.10.4. Strategies for Effective Selection and Adoption of QA Tools with AI Capabilities 

A unique, key, and decisive educational experience to boost your professional development and make the definitive leap"