Aprendizagem-de-Maquina-e-Mineracao-de-Dados

Discipline of Machine Learning and Data Mining at the university

Discipline ipynb

• winequality.ipynb

Self study ipynb

• data_visualizations.ipynb

  • Getting the data.
  • Line plot.
  • Axis labels and title.
  • Second line.
  • Legend.
  • Gridlines.
  • Text.
  • Size of x ticks and y ticks.

• human_bacteria_genome_comparison.ipynb

  • Comparison of two DNA sequences: (1) human; vs. (2) bacteria.

• distance_metrics_in_machine_learning.ipynb

  • Continuous or numerical variables.
    • Euclidean Distance.
    • Manhattan Distance.
    • Minkowski Distance.
    • Vector Norm.
      • Vector L1 Norm
      • Vector L2 Norm
      • Vector Lp Norm
  • Categorical variables.
    • Hamming Distance.
    • Cosine Distance & Cosine Similarity
  • Bonus.
    • CountVectorizer.

• brazilian_population_growth_visualization.ipynb

  • Data Visualization with Python.
    • Visualize Data with Python.
    • Get to know the MATPLOTLIB PYPLOT library.
    • Build line, bar, scatter and boxplot charts.
    • Manipulate data to build graphs.
  • Predicting.
    • Predicting population growth using a very simple datasus data.
    • Comparing to complex IBGE prediction through ploting.

• iris_flower_species_KNN.ipynb

  • Famous Iris Flower Species Dataset.
    • The Iris Flower Dataset involves predicting the flower species given measurements of iris flowers.
    • It is a multiclass classification problem. The number of observations for each class is balanced.
    • Solve it in 3 steps: Calculate Euclidean Distance; Get Nearest Neighbors (KNN) and Make Predictions.