The Master’s in Artificial Intelligence: Management and Implementation of Models offers advanced specialization for professionals seeking to lead the strategic integration of AI into their organizations, optimizing business processes and strategies. This program provides you with a deep understanding of the models and algorithms that drive innovation, covering everything from the fundamentals of Artificial Intelligence and its various types to the implementation of AI projects from start to finish. You will specialize in AI model management, exploring machine learning and deep learning algorithms to make informed strategic decisions and solve complex challenges. You’ll acquire critical skills in self-service data preparation, using essential tools such as Excel, Talend, and Dataprep by Trifacta to optimize the quality, accessibility, and governance of information—a fundamental pillar for the success of any AI initiative. You’ll delve into data mining, extracting value and hidden patterns that will drive innovation and competitive advantage across various sectors. This training will enable you to manage and implement AI models with technical and strategic proficiency, positioning you as a leader in managing advanced technology projects and optimizing processes through Artificial Intelligence, directly contributing to your organization’s growth, operational efficiency, and digital transformation.
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
Cybersecurity
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, and Design Thinking.
Change Management in the Workplace
Introduction
Key Elements for AI Project Management
Characteristics of AI Projects
Introduction to the Main Agile and Ideation Methodologies
Educa PHAROS is a training solution for businesses that centralizes learning, simplifies user management, and allows you to track progress through dashboards and reports.
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).
It is based on best practices in security, access control, learning traceability, and implementation support. Customize this text according to your commitments (SLA, compliance, etc.).
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.
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