Master's Degree in Continuing Education in Artificial Intelligence: Management and Implementation of Models
PRESENTATION
Objectives
• Lead the conceptualization and development of AI initiatives, understanding their strategic potential and limitations. • Design strategies for implementing machine learning and deep learning algorithms, optimizing their application within the organization. • Manage data preparation and processing using tools such as Excel, Talend, and Dataprep by Trifacta, ensuring data quality for AI models. • Implement AI projects from start to finish, from design through deployment, ensuring their effective integration. • Evaluate the impact of AI solutions, measuring their performance and adjusting strategies to maximize return on investment. • Make informed decisions regarding the selection and application of AI types, aligning them with business objectives. • Integrate a comprehensive approach to data mining to extract valuable patterns and insights, driving innovation and competitive advantage. • Lead multidisciplinary teams in the execution of AI projects, fostering collaboration and efficiency.
Methodology
At Educa PHAROS, we use a highly practical methodology focused on the direct application of knowledge in the student’s professional environment, combining theoretical content with real-world cases, digital tools, and support from a team of specialized instructors.
Program
- The State of the Art in Artificial Intelligence
- Philosophy of Artificial Intelligence
- The Future of Artificial Intelligence
- Project Development Processes Using Artificial Intelligence
- Data: Your Greatest Asset
- Machine Learning
- Deep Learning
- Transformers
- Synthetic Data Generation
- Hyperparameters in Artificial Intelligence Models
- Linear Regression
- Nonlinear Regression and Support Vector Machines (SVM)
- Decision Trees and Random Forests
- Fuzzy Logic and Gradient Descent
- Recommendation Systems
- Setting Up the Development Environment: Anaconda, Visual Studio Code, and Python
- Input Dataset and Data Processing
- TensorHub, TensorFlow, and Keras
- Image Processing
- Generation of Artificial Intelligence Models
- Introduction
- Data Literacy
- Working with Data
- Data Processing Solutions and Techniques
- Data Quality Management
- Working with Data in Excel
- Dataset (DATASET)
- Data Cleaning with Excel
- Data Wrangling with Excel
- Data Blending in Excel
- Installing Talend Data Preparation Desktop
- Working with Data in Talend
- Data Cleansing with Talend
- Data Wrangling with Talend
- Data Blending with Talend
- Sign Up for Dataprep by Trifacta
- Working with Data Using Dataprep by Trifacta
- Data Cleansing with Trifacta
- Data Wrangling with Dataprep by Trifacta
- Data Blending with Dataprep by Trifacta
- Introduction
- Linear, Multiple, and Logistic Regression (I)
- Linear, Multiple, and Logistic Regression (II)
- Support Vector Machine (SVM)
- Decision Trees
- KNN (K-Nearest Neighbors)
- Naive Bayes
- Evaluation of Supervised Models
- Sample Exercise
- Suggested Exercise
- Introduction to Clustering: Purpose and Metrics
- K-means clustering
- Hierarchical clustering, other techniques, and examples
- Principal Component Analysis (PCA)
- PCA Example Exercise
- Artificial Neural Networks (ANN) (I)
- Artificial Neural Networks (ANN) (II)
- Artificial Neural Networks (ANN) (III)
- Sample Exercise
- Suggested Exercise
- Introduction
- Classification of Machine Learning Algorithms
- Examples of Machine Learning
- Differences Between Machine Learning and Deep Learning
- Types of Machine Learning Algorithms
- The Future of Machine Learning
- Introduction
- Algorithms
- Introduction
- Collaborative filtering
- Clustering
- Hybrid Recommendation Systems
- Classifiers
- Algorithms
- Components
- Learning
- Introduction
- The Process of Transitioning from DSS to IDSS
- Use Cases
- Deep Learning
- Deep Learning Environment with Python
- Machine Learning and Deep Learning
- Neural Networks
- Deep networks and shallow networks
- Single-layer and multilayer perceptrons
- Example of a perceptron
- Types of Deep Networks
- Data Input and Output
- Training a Neural Network
- Computer Graphics
- Implementation of a Deep Network
- The Direct Propagation Algorithm
- Multilayer deep neural networks
- Introduction to Power BI
- Different Types of Power BI: Is It Really Free?
- Let's dive right in: First Simple Report
- Power Query: Data Source
- Data Transformation
- Data Modeling
- Getting Started with DAX (I)
- Getting Started with DAX (II)
- Mastering the DAX (I)
- Mastering the DAX (II)
- Table and Matrix
- Trends
- How to Filter Your Data Properly
- Bookmarks
- Obtaining Details
- Understanding the Power BI Service
- Sharing Content in Power BI Service
- Comparing Power BI Service and Power Report Service
- Integrating Python and R into Power BI Desktop
- Introducing Bravo for Power BI Desktop
- - Linear Regression.
- - Logistic Regression.
- - Neural Networks.
- - Clustering.
- Principal Component Analysis (PCA).
- - Deep neural networks.
- - Algorithm optimization.
- - Convolutional neural networks.
- - Recurrent neural networks.
- - NLP. Natural Language Processing.
- - Creating tables and reports.
- - Data transformation and filtering.
- - Data visualization.
- - Calculations. Relationships between data tables, metrics, and indicators.
- - Dynamic and interactive control panel.
- - Application: Object classification in images.
- - Application: Object detection in images.
- - Application: Facial recognition.
- - Application: Word recognition for voice assistants.
- - Application. Business Intelligence.
- Fourth Industrial Revolution
- Digital Transformation in Businesses
- Fundamentals and Key Points
- Benefits
- Enabling Technologies
- Big Data
- Cloud Computing
- Blockchain
- Artificial Intelligence
- Virtual and Augmented Reality
- BIM
- Collaborative robots
- Additive Manufacturing
- Hyperconnectivity
- IoT
- Manufacturing Execution System (MES)
- Process Integration and Efficiency
- Use Cases
- New methodologies: Agile, Lean Startup, or Design Thinking
- Change Management
- Introduction
- Key Elements for AI Project Management
- Characteristics of AI Projects
- Introduction to the Main Agile and Ideation Methodologies
- Integration of Different Methodologies
- Introduction
- Phase I: Empathize
- Phase II: Define
- Phase III: Brainstorming
- Phase IV: Prototyping
- Lean Startup: Basic Concepts
- Lean Startup. Tools
- Scrum: An Introduction
- Scrum. Roles
- Scrum: Ceremonies and Artifacts
- Introduction
- Developing the Project
- Implementing the Project
- Some tips for implementing these methodologies
- Summary and Conclusions
- Finance and Insurance
- Retail
- Industry
- Agriculture
- Health
- Logistics and Operations
- Marketing
- Sales and Customer Service
- Finance and Control
- People Analytics
- The Current Landscape of a Booming Industry
- Funding and Financial Resources
- Notable Startups
- The Future of the Ecosystem
- Starting an AI Company
- Ethics. General Notes.
- Examples of biases.
- Global initiatives.
- Government Agencies and Regulation.
- AI and the Sustainable Development Goals
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It is designed for organizations that want to develop internal skills, standardize role-based training, and have traceability and learning metrics.
It includes learning paths by profile, user and permission management, progress reports, certification, and access to masterclasses or live sessions (depending on the plan).
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Educa PHAROS is a next-generation training model that places a company’s human capital at the forefront. Through a platform that adapts to each company’s corporate identity and offers a total of more than 900 courses, it provides tailored training for each organization. The unlimited flat-rate plan provides each company with the number of courses that best suits its needs, as well as the ability to determine which employees will have access.