1. State of the art of artificial intelligence
  2. Philosophy of artificial intelligence
  3. Future of artificial intelligence
  4. Project development processes with artificial intelligence
  5. Data, your greatest asset

  1. Machine learning
  2. Deep learning
  3. Transformers
  4. Synthetic data generation
  5. Hyperparameters in artificial intelligence models

  1. Linear regression
  2. Non-linear Regression and Support Vector Machine (SVM)
  3. Decision trees and random forests
  4. Fuzzy logic and gradient descent
  5. Recommender systems

  1. Preparing the working environment: Anaconda, Visual Studio Code and Python
  2. Data input and processing datasets
  3. TensorHub, TensorFlow and Keras
  4. Image processing
  5. Artificial intelligence modelling

  1. Introduction
  2. Data literacy
  3. Working with data
  4. Solutions and techniques for data processing
  5. Data quality management

  1. Working with data in Excel
  2. Data set (DATASET)
  3. Data Cleasing with Excel
  4. Data Wrangling with Excel
  5. Data Blending in Excel

  1. Talend Data Preparation Desktop Installation
  2. Working with data in Talend
  3. Data Cleasing with Talend
  4. Data Wrangling with Talend
  5. Data Blending with Talend

  1. Registration in dataprep by Trifacta
  2. Working with data with Dataprep by Trifacta
  3. Data Cleasing with Trifacta
  4. Data Wrangling with Dataprep by Trifacta
  5. Data Blending with Dataprep by Trifacta

  1. Introduction
  2. Linear, multiple and logistic regression (I)
  3. Linear, multiple and logistic regression (II)
  4. Support Vector Machine (SVM)
  5. Decision trees

  1. KNN (K-Nearest Neighbors)
  2. Naive bayes
  3. Evaluation of supervised models
  4. Sample exercise
  5. Proposed exercise

  1. Introduction to clustering: purpose and metrics
  2. K-means clustering
  3. Hierarchical clustering, other techniques and examples
  4. Principal Component Analysis (PCA)
  5. Sample PCA exercise

  1. Artificial Neural Networks (ANN) (I)
  2. Artificial Neural Networks (ANN) (II)
  3. Artificial Neural Networks (ANN) (III)
  4. Sample exercise
  5. Proposed exercise

  1. Introduction and Review of Artificial Neural Networks (ANN)
  2. Convolutional Neural Networks (CNN): Introduction and Use Cases
  3. CNN: Intuition
  4. CNN: Mathematical description
  5. CNN: Programming example with Python and TensorFlow
  6. Exercise: Artificial vision with CNN

  1. Time Series Review
  2. Recurrent Neural Networks (RNN): Introduction and Use Cases
  3. RNN: Intuition
  4. RNN: Mathematical description
  5. RNN: Programming Example with Python and TensorFlow
  6. Exercise: Time Series with RNN

  1. Review of Recommender Systems
  2. Deep Bolztmann Machines (DBM): Introduction and use cases [Video].
  3. DBM: Intuition
  4. DBM: Mathematical description
  5. DBM: Programming Example with Python and TensorFlow
  6. Exercise: Recommendation System with DBM

  1. Anomaly detection
  2. Self-Organising Maps (SOM): Introduction and intuition
  3. SOM: Mathematical description
  4. AutoEncoders (AE): Introduction and intuition
  5. AE: Mathematical description
  6. Exercise: Anomaly detection with SOM and AE

  1. Introduction to Power BI
  2. Different types of Power BI: is it really free?
  3. Let's dive in: simple first report
  4. Power Query: data source
  5. Data transformation

  1. Data modelling
  2. Starting with DAX (I)
  3. Getting started with DAX (II)
  4. Mastering DAX (I)
  5. Mastering DAX (II)

  1. Table and matrix
  2. Trends
  3. How to filter your data properly
  4. Scoreboards
  5. Obtaining details

  1. Understanding Power BI Service
  2. Sharing content in Power BI Service
  3. Comparing Power BI Service and Power Report Service
  4. Integrating Python and R in Power BI Desktop
  5. Introducing Bravo for Power BI Desktop

  1. - Linear Regression.
  2. - Logistic Regression.
  3. - Neural Networks.
  4. - Clustering.
  5. Principal Component Analysis (PCA).

  1. - Deep neural networks.
  2. - Optimisation of algorithms.
  3. - Convolutional neural networks.
  4. - Recurrent neural networks.
  5. - NPL. Natural language processing.

  1. - Creation of tables and reports.
  2. - Data transformation and filtering.
  3. - Data visualisation.
  4. - Calculation. Relationships between data tables, metrics and indicators.
  5. - Dynamic and interactive control panel.

  1. - Application. Classification of objects in images.
  2. - Application. Detection of objects in images.
  3. - Application. Facial recognition.
  4. - Application. Word detection for voice assistants.
  5. - Application. Business Intelligence.

  1. Fourth industrial revolution
  2. Digital transformation in business
  3. Fundamentals and key points
  4. Benefits
  5. Enabling technologies

  1. Big Data
  2. Cloud Computing
  3. Cybersecurity
  4. Artificial intelligence
  5. Virtual and augmented reality

  1. BIM
  2. Collaborative robots
  3. Additive manufacturing
  4. Hyperconnectivity
  5. IoT

  1. Manufacturing Execution System (MES)
  2. Process integration and efficiency
  3. Use cases
  4. New methodologies: Agile, Lean Startup or Design Thinking.
  5. Change management in the company

  1. Introduction
  2. Key elements for AI project management
  3. Characteristics of AI projects
  4. Introduction to the main agile and ideation methodologies
  5. Integration of different methodologies

  1. Introduction
  2. Phase I: Empathising
  3. Phase II: Define
  4. Phase III: Devising
  5. Phase IV: Prototyping

  1. Lean start-up. Basic concepts
  2. Lean Start-up. Tools
  3. Scrum. Introduction
  4. Scrum. Roles
  5. Scrum. Ceremonies and artefacts

  1. Introduction
  2. Devising the project
  3. Implementing the project
  4. Some tips on how to implement the methodologies
  5. Summary and conclusions

  1. Finance and Insurance
  2. Retail
  3. Industry
  4. Agriculture
  5. Health

  1. Logistics and operations
  2. Marketing
  3. Sales and Customer Service
  4. Finance and Control
  5. People Analytics

  1. Current scenario of a booming sector
  2. Financing and funding
  3. Featured Startups
  4. Future of the ecosystem
  5. Starting an AI company

  1. Ethics. General notes.
  2. Examples of biases.
  3. Global initiatives.
  4. Public bodies and regulation.
  5. AI in the Sustainable Development Goals