Machine_learning_a Z

Learning to create Machine Learning Algorithms
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Machine Learning A-Z

A step towards Data Science and Machine Learning

Contains the code and implementation of the following topics and techniques:

  1. Data Preprocessing

    • Importing the dataset
    • Dealing with missing data
    • Splitting the data into test set and training set
    • Feature Scalling
  2. Regression

    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Linear Regression
    • Support Vector Regression (SVR)
    • Decision Tree Regression
    • Random Forest Regression
  3. Classification

    • Logistic Regression
    • K-Nearest Neighbors (K-NN)
    • Support Vector Machine (SVM)
    • Kernel SVM
    • Naive Bayes
    • Decision Tree Classifiers
    • Random Forest Classifiers
  4. Clustering

    • K-Means Clustering
    • Hierarchical Clustering
  5. Association Rule Learning

    • Apriori
  6. Deep Learning

    • Artifial Neural Networks (ANN)
    • Convolutional Neural Networks (CNN)
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