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)

• Recommendation for ML Enthusiasts: Machine Learning A-Z™: Hands-On Python & R In Data Science