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

  1. Machine learning
  2. Deep learning
  3. Transformers
  4. Generation of synthetic data
  5. Hyperparameters in artificial intelligence models

  1. Linear regression
  2. Non-linear regression and support vector machines (SVM)
  3. Decision trees, random forests
  4. Fuse logic and gradient down
  5. Recommendation systems

  1. Preparation of the working environment: Anaconda, Visual Studio Code and Python
  2. Input dataset and data preprocessing
  3. TensorHub, TensorFlow and Keras
  4. Image processing
  5. Generation of artificial intelligence models

  1. Introduction
  2. Self - Service solutions
  3. Data processing techniques
  4. Data quality management
  5. Types of data problem

  1. Data cleaning with Excel
  2. DATASET
  3. Functions. Part I
  4. Functions. Part II
  5. Functions. Part III

  1. Instructions for installing talend data preparation free desktop
  2. Data Cleansing with Talend Data Preparation
  3. Basic cleansing functions
  4. Data normalization
  5. Data enrichment

  1. Registration instructions
  2. Data cleansing with trifacta
  3. Basic cleansing functions
  4. Data normalization
  5. Data enrichment

  1. Introduction
  2. Simple, multiple and logistic linear regression (I)
  3. Simple, multiple and logistic linear regression (II)
  4. Support vector machines (SVM)
  5. Decision trees

  1. KNN (k-nearest neighbours)
  2. Naive Bayes
  3. Evaluation of supervised models
  4. Example exercise
  5. Proposed exercise

  1. Introduction to clustering: purconsider and metrics
  2. K-means clustering
  3. Hierarchical clustering, other techniques and examples
  4. Principal component analysis (PCA)
  5. PCA example exercise

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

  1. Introduction
  2. Review: Artificial neural network (ANN)
  3. Review: ANN exercises
  4. Convolutional Neural Networks (CNN)
  5. CNN Exercises

  1. Natural language processing (I)
  2. Recurrent neural networks (RNN) (I)
  3. Recurrent neural networks (RNN) (II)
  4. Natural language processing (II)
  5. RNN Exercise

  1. Boltzmann Machines (BM)
  2. Restricted Boltzmann Machines (RBM)
  3. Recommender systems
  4. Recommender systems: metrics
  5. RBM exercise

  1. Self-organising maps (SOM)
  2. SOM exercises
  3. Autoencoders (AE)
  4. AE exercises
  5. Proposed exercise

  1. Introduction to Power BI
  2. Different types of Power BI
  3. First simple report
  4. Power Query: Data Sources
  5. Data Transformation

  1. Data Modeling
  2. Starting with DAX (I)
  3. Starting with DAX (II)
  4. Getting proficient in DAX (I)
  5. Getting proficient in DAX (II)

  1. Table and Matrix
  2. Trends
  3. How to properly filter your data
  4. Bookmarks
  5. Drill through

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

  1. Linear regression
  2. Logistic regression
  3. Basic Neural Network
  4. Clustering
  5. Principal Component Analysis (PCA)

  1. Deep learning
  2. Optimization
  3. Convolutional Neural Network
  4. Recurrent Neural Network
  5. Natural Language Processing (NLP)

  1. Creating tables and Reports
  2. Transformation and filtering data
  3. Data visualization
  4. Relation between data tables
  5. Dashboard

  1. Object detection in images
  2. Object classification in images
  3. Facial recognition
  4. Word detection
  5. Business Intelligence application

  1. The fourth industrial revolution
  2. Digital transformation in companies
  3. Fundaments and key points
  4. Benefits
  5. Enabling technologies

  1. Big data
  2. Cloud computing
  3. Blockchain
  4. Artificial intelligence
  5. Augmented and virtual 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. Business change management

  1. Introduction
  2. Key elements in AI project management
  3. AI project characteristics
  4. Introduction to the main agile and ideation methodologies
  5. Methodology integration

  1. Introduction
  2. Phase I: Empathize
  3. Phase II: Define
  4. Phase III: Devise
  5. Phase IV: Prototype

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

  1. Introduction
  2. Project ideation
  3. Project implementation
  4. Advise on implementing methodologies
  5. Summary and conclusions

  1. Financial sector
  2. Retail sector
  3. Industrial sector
  4. Agricultural sector
  5. Health sector

  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
  3. Featured start-ups
  4. Future of the AI ecosystem
  5. Starting an AI company

  1. Ethics. General remarks
  2. Bias examples
  3. Global initiatives
  4. Public Institutions and regulations
  5. AI in the SDGs