In this course, you will learn about the fundamental concepts of Machine Learning, such as supervised and unsupervised learning, feature engineering, and model evaluation. Additionally, you will gain practical experience with popular Machine Learning tools and libraries, including scikit-learn and TensorFlow.

• Regression

  

  • Linear regression
  • Multiple linear regression
  • Polynomial Regression
  • Lasso and Ridge Regression
  • Support vector regression
  • Ensamble Regression

  

• Model validation

  • K-fold cross-validation
  • R-square and Mean square error calculation and their importance

• Classification models

  • Logistic regression
  • Support vector machine classification
  • K-nearest neighbour classification
  • Ensamble classifier

• Dimensionality Reduction

  

  • PCA-Principal Component Analysis
  • Linear Discriminant Analysis (LDA)
  • Kernal PCA

  

• Clustering

  • K-mean clustering
  • Hierarchial clustering