Table of Contents

Welcome to Things to Know about Machine Learning!

1. The Fundamentals of Machine Learning
   1. Data Preprocessing
      1. 6 Approaches to Deal with Imbalanced Classes
      2. How to Deal with Missing Values
      3. Handling Categorical Values
      4. Feature Scaling
   2. Training Models
      1. Batch vs. Online Learning
      2. Simple Linear Regression
      3. The Normal Equation
      4. Logistic Regression
      5. Softmax Regression
      6. L1 and L2 as Cost Function
      7. L1 and L2 as Regularization
      8. Underfitting vs. Overfitting
      9. Gradient Descent
   3. Ensemble Learning
      1. Bagging
      2. Random Forests
      3. Gradient Boosting
   4. Natural Language Processing (NLP)
      1. A General Approach to Preprocessing Text Data
   5. ML Tutorials
      1. Tutorial: Doc2Vec and t-SNE
2. Graph Machine Learning
   1. Knowledge Graph
      1. Knowledge Graph Completion
3. Deep Learning
   1. Training DNN
      1. 4 Approaches to Deal with Vanishing/Exploding Gradients Problems
      2. Batch Normalization
      3. What are Embeddings?
   2. Recurrent Neural Networks
      1. Introduction to RNN
4. Data Science
   1. Visualization
      1. Box Plot
      2. Bar Chart / Stacked Bar Chart
   2. Statistical Significance Testing
      1. What is a Hypothesis Test?
      2. Type I and Type II Errors
   3. Data Analysis Examples
      1. Analysis of Text: Tolstoy’s War and Peace