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 neighbors)
  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