SUMMARY

1. Apps - Bibliometric and Scientometric - Python
2. Apps - Machine Learning - Python (Lesson Included)
3. Apps - Multicriteria Decision Aids - Java/Python (Lesson Included)
4. Apps - Operations Research - Java/Python (Lesson Included)
5. Data Science - Association Rules - Python (Lesson Included)
6. Data Science - Classification Algorithms - Python (Lesson Included)
7. Data Science - Regression Algorithms - Python (Lesson Included)
8. Data Science - Clustering Algorithms - Python (Lesson Included)
9. Data Science - Decision Trees - Python (Lesson Included)
10. Data Science - Recommender Systems - Python (Lesson Included)
11. Data Science - Natural Language Processing - Python (Lesson Included)
12. Data Science - Neural Networks - Python (Lesson Included)
13. Data Science - Reinforcement Learning - Python (Lesson Included)
14. Data Science - Deep Reinforcement Learning - Python (Lesson Included)
15. Forecasting - Python
16. Multivariate Data Analysis - R and SPSS (Lesson Included)
17. Others - Python

Apps - Bibliometric and Scientometric

• pyBibX: A Bibliometric and Scientometric Python Library Powered with Artificial Intelligence Tools

• pyAutoSummarizer: A Extrative and Abstractive Summarization Library Powered with Artificial Intelligence

Apps - Machine Learning

• pyRecommenderSystem: A Recommender System Library. Recommender Systems Lesson

Apps - Multicriteria Decision Aids

• pyDecision: A python library for MCDA methods. Lesson Included. AHP Lesson. Lesson Included (slides in pt-br). ELECTRE I Lesson ; ELECTRE I_v Lesson ; ELECTRE I_s Lesson ; ELECTRE II Lesson ; ELECTRE III Lesson ; ELECTRE IV Lesson and ELECTRE Tri Lesson. Lesson Included. PROMETHEE I Lesson ; PROMETHEE II Lesson ; PROMETHEE III Lesson ; PROMETHEE IV Lesson and PROMETHEE V Lesson

• pyMissingAHP - A Method to Infer AHP Missing Pairwise Comparisons. Lesson Included. AHP Lesson

• 3MO-AHP - A Multiobjective Inconsistency Reduction Technique for AHP and Fuzzy-AHP Methods. The following objectives can be used: minimum inconsistency (fMI), the total number of adjusted pairwise comparisons (fNC), original rank preservation (fKT), minimum average weights adjustment (fWA) and finally, minimum L1 matrix norm between the original PCM and the adjusted PCM (fLM). Lesson Included. AHP Lesson

• Ranking-Trees (Inference for ELECTRE II, III and IV Parameters and Inference for PROMETHEE I, II, III, IV Parameters) : A python implementation of the Ranking-Trees Algorithm. The Ranking-Trees can elicitate (infer) all or any combination of the following parameters, ELECTRE II ( Colab Demo ) - criteria weights, concordance parameters and discordance parameters, ELECTRE III ( Colab Demo ) - criteria weights, indifference threshold, preference threshold and veto threshold, ELECTRE IV ( Colab Demo ) - indifference threshold, preference threshold and veto threshold, PROMETHEE I ( Colab Demo ) - P, Q, S and F parameters, PROMETHEE II ( Colab Demo ) - P, Q, S and F parameters, f) PROMETHEE III ( Colab Demo ) - P, Q, S, F and lambda parameters, PROMETHEE IV ( Colab Demo ) - P, Q, S and F parameters. Lesson Included (slides in pt-br). ELECTRE I Lesson ; ELECTRE I_v Lesson ; ELECTRE I_s Lesson ; ELECTRE II Lesson ; ELECTRE III Lesson ; ELECTRE IV Lesson and ELECTRE Tri Lesson

• ELECTRE-Tree (Inference for ELECTRE Tri-B Parameters) : A python implementation of the ELECTRE-Tree Algorithm. The ELECTRE-Tree can elicitate (infer) all or any combination of the following ELECTRE Tri-B parameters: criteria weights, indifference threshold, preference threshold, veto threshold, and the lambda cut level ( Colab Demo ). Lesson Included (slides in pt-br). ELECTRE I Lesson ; ELECTRE I_v Lesson ; ELECTRE I_s Lesson ; ELECTRE II Lesson ; ELECTRE III Lesson ; ELECTRE IV Lesson and ELECTRE Tri Lesson

• J-ELECTRE (ELimination and Choice Expressing REality) : Electre I, I_s, I_v, II, III, IV, TRI and TRI-ME Software. Lesson Included (slides in pt-br). ELECTRE I Lesson ; ELECTRE I_v Lesson ; ELECTRE I_s Lesson ; ELECTRE II Lesson ; ELECTRE III Lesson ; ELECTRE IV Lesson and ELECTRE Tri Lesson

• Voracious-AHP (Analytic Hierarquic Process) : A software that can remove or reduce the inconsistency from an AHP Matrix. Lesson Included. AHP Lesson

Apps - Operations Research

• J-Horizon : A Vehicle Routing Problem Software. CVRP (Capacitated VRP), MDVRP (Multiple Depot VRP), VRPTW (VRP with Time Windows), VRPB (VRP with Backhauls), VRPPD (VRP with Pickups and Deliveries), VRP with Homogeneous or Heterogeneous Fleet, TSP, mTSP and various combination of these types

• J-EOQ-SA : EOQ (Economic Order Quantity) for a single product with No Discounts, All Units or Incremental Discounts and with or without Backorders

• pyMetaheuristic : A library that implements Metaheuristics to solve single objective problems. GA Lesson

• pyMultiobjective : A library that implements Multiobjective Optimization Algorithms and Many Objectives Optimization Algorithms

• pyCombinatorial : A library to solve the TSP (Travelling Salesman Problem) using Exact Algorithms, Heuristics and Metaheuristics

• pyVRP : A python library that solves, using Genetic Algorithms, many VRP Problems ( Capacitated VRP; Multiple Depot VRP; VRP with Time Windows; VRP with Heterogeneous Fleet; VRP with Infinite Fleet; Open VRP; TSP; mTSP and various combination of these types). GA for VRP Lesson

• pyID : A python library that implements many Intermittent Demand Methods (Croston; SBA; SBJ; TSB; HES; LES and SES)

Data Science - Association Rules

• Apriori Algorithm : Apriori Algorithm - An Association Rule Learning Over Transactions Databases
• Apriori Algorithm Lesson

Data Science - Classification Algorithms

• Naive Baye Classifier : Classifier for Supervised Learning Problems
• Naive Baye Classifier Lesson
• Support Vector Machines Lesson
• Gradient Boosting Lesson

Data Science - Regression Algorithms

• Multiple Linear Regression Lesson
• Logistic Regression Lesson
• Multinomial Regression Lesson
• Support Vector Regression Lesson
• Gradient Boosting Lesson

Data Science - Clustering Algorithms

• Agglomerative Hierarchical Methods, DIANA, K-Means, DBSCAN, OPTICS Lesson

Data Science - Decision Trees

• ID3 (Iterative Dichotomiser 3) : A Decision Tree for Categorical Data with Pruning Methods
• ID3 (Iterative Dichotomiser 3) Lesson
• C4.5 : A Decision Tree for Numerical and Categorical Data that can Handle Missing Values and Pruning Methods
• C4.5 Lesson
• CART (Classification And Regression Trees) : A Decision Tree for Numerical and Categorical Data that can Handle Missing Values and Pruning Methods
• CART (Classification And Regression Trees) Lesson
• CHAID (Chi-square Automatic Interaction Detection) Lesson
• Random Forest : A Decision Tree Ensemble for Numerical and Categorical Data that can Handle Missing Values
• Random Forest Lesson
• Isolation Forest & Extended Isolation Forest Lesson ( Colab Demo - Isolation Forest ) ( Colab Demo - Extended Isolation Forest )

Data Science - Recommender Systems

• CBF (Content Based Filtering) : Content-Based Filtering using TF-IDF Matrices with Cosine Similarity
• CF Item (Collaborative Filtering - Item Based) : Collaborative Filtering Function using an Item Based Regression Approach
• CF User (Collaborative Filtering - User Based) : Collaborative Filtering Function using an User Based Regression Approach
• CF User-Item (Collaborative Filtering - User & Item Based) : Collaborative Filtering Function using an User-Item Based Regression Approach
• CF Latent Factors (Collaborative Filtering - Latent Factors) : Collaborative Filtering Function using Regression with Latent Factors Approach
• CF Nearest Neighbors (Collaborative Filtering - Nearest Neighbors) : Collaborative Filtering Function using a Nearest Neighbors Approach
• CF SVD (Collaborative Filtering - SVD) : Collaborative Filtering Function using a SVD (Singular Value Decomposition) Approach
• Recommender Systems Lesson

Data Science - Natural Language Processing

• LDA (Latent Dirichlet Allocation) : Latent Dirichlet Allocation (LDA) function. Also computes the dtm, binary dtm, tf dtm and tf-idf dtm
• LDA (Latent Dirichlet Allocation) Lesson ( Colab Demo )
• Word Embedding Lesson ( Colab Demo - W2V ) ( Colab Demo - D2V )
• Extractive and Abstractive Summarization Lesson ( Colab Demo - TextRank ) ( Colab Demo - LexRank ) ( Colab Demo - LSA ) ( Colab Demo - KL-Sum ) ( Colab Demo - BART) ( Colab Demo - T5 ) ( Colab Demo - chatGPT )

Data Science - Neural Networks

• Neural Network : Pure Python Neural Network Function for Classification or Regression Problems
• Neural Network Lesson ( Colab Demo - Regression ) ( Colab Demo - Classification 1 ) ( Colab Demo - Classification 2 )
• Convolutional Neural Network Lesson ( Colab Demo - Classification 1 ) ( Colab Demo - Classification 2 VGG16 ) ( Colab Demo - Object Detection )
• Recurrent Neural Network Lesson ( Colab Demo - NLP ) ( Colab Demo - Forecasting 1 ) ( Colab Demo - Forecasting 2 )
• Tranformers Lesson ( Colab Demo - Translation ) ( Colab Demo - Voice Cloning & Text-to-Speech ) ( Colab Demo - Abstractive Summarization )
• Vision Transformers Lesson ( Colab Demo - Object Detection )
• Autoencoders Lesson ( Colab Demo - Dimension Reduction )
• Variational Autoencoders Lesson ( Colab Demo - Shapes )
• Generative Adversarial Networks Lesson ( Colab Demo - pixel2style2pixel ) ( Colab Demo - Numpy) ( Colab Demo - Deepfake) ( Colab Demo - Image Animation)
• Diffusion Models Lesson ( Colab Demo - Stable Diffusion 1 ) ( Colab Demo - Stable Diffusion 2 ) ( Colab Demo - DDPM & DDIM )

Data Science - Reinforcement Learning

• Reinforcement Learning Lesson ( Colab Demo - Q Learning - Gridworld ) ( Colab Demo - Q Learning - TicTacToe )

Data Science - Deep Reinforcement Learning

• Deep Reinforcement Learning Lesson ( Colab Demo - DQN - Gridworld ) ( Colab Demo - A2C - Gridworld )

Forecasting

• Forecasting Lessons :
  • Lesson 01 - Introduction to Forecasting
  • Lesson 02 - Time Series Decomposition
  • Lesson 03 - Holt's Method
  • Lesson 04 - Holt-Winters' Method
  • Lesson 05 - Multiple Linear Regression
  • Lesson 06 - Logistic Regression
• Moving Averages : Calculates the Centered Moving Average (Weighted, Simple or Exponential) of a Time Series
• Decomposition : Decomposition of Timeseries Using the X-11 Algorithm
• Holt Method : Calculates the Additive or Multiplicative Holt's Method for Time Series with Trend
• Holt-Winters Method : Calculates the Additive or Multiplicative Holt-Winters' Method for Time Series with Trend and Seasonality

Multivariate Data Analysis

• MVDA Lessons ( R ):

  • R Codes
  • Lesson 03 - Exploratory Factor Analysis
  • Lesson 04 - Multidimensional Scaling
  • Lesson 05 - Correspondence Analysis
  • Lesson 06 - Discriminant Analysis
  • Lesson 07 - Multiple Linear Regression
  • Lesson 08 - Logistic Regression (Binary)
  • Lesson 09 - Logistic Regression (Multinomial)
  • Lesson 10 - Confirmatory Factor Analysis
  • Lesson 11 - Canonical Correlation

• MVDA Lessons ( SPSS ) :

  • Lesson 01 - Introduction
  • Lesson 02 - Scales & Descriptive Statistics
  • Lesson 03 - Exploratory Factor Analysis
  • Lesson 04 - Multidimensional Scaling
  • Lesson 05 - Correspondence Analysis
  • Lesson 06 - Discriminant Analysis
  • Lesson 07 - Multiple Linear Regression
  • Lesson 08 - Logistic Regression (Binary)
  • Lesson 09 - Logistic Regression (Multinomial)
  • Lesson 10 - Confirmatory Factor Analysis
  • Lesson 11 - Canonical Correlation

Others

• LUDPP (LU Decomposition with Partial Pivoting) : Lower–Upper Decomposition with Partial Pivoting