STEPZINDIA

Machine Learning

WHAT WILL I LEARN?

  1. Preliminaries of Statistics
  2. Graphical Techniques/ Data Visualization
  3. Data Mining – Supervised Learning
    1. Regression
      1. Simple Linear Regression
      2. Multilinear Regression
      3. Logistic Regression
    2. Decision Trees
    3. Random Forest – Ensemble Model
    4. K-Nearest Neighbour (KNN)
    5. Support Vector Machine (SVM)
    6. Neural Networks
    7. Naïve Bayes Classifier
  4. Data Mining – Unsupervised Learning
    1. Clustering
      1. Hierarchical Clustering
      2. K-Means Clustering
      3. K-Medoids
      4. CLARA
      5. DBSCAN
    2. Dimension Reduction
    3. Principal Component Analysis
  1. Advanced Regression Analysis
    1. Poisson Regression
    2. Negative Binomial Regression
    3. Lasso & Ridge Regression
    4. Zero Inflated Regression
    5. Multinomial Regression
  2. Text Mining
    1. Word Cloud
    2. Dendrogram
    3. Extracting reviews
  3. Natural Language Processing
    1. Latent Dirichlet Allocation (LDA)
    2. Emotion Mining- Lexicons
  4. Survival Analysis