Posts by Jonathan Hari Napitupulu
- Advanced Optimization
- Classification
- Collaborative filtering
- Collaborative filtering algorithm
- Deciding What to Do Next (Revisited)
- Deciding what to Try Next
- Decision Boundary
- Diagnosing bias vs. variance
- Evaluating a hyphotesis
- Examples & Intuition l
- Examples & Intuition ll
- hypothesis representation
- Introduction
- Learning Curves
- Model Representation l
- Model Representation ll
- Model selection and training/validation/test sets
- Multi-class Classification
- multiclass classification
- Neurons & the brain
- Non-linear hypothesis
- Note from Intro to Data Science
- Regularization and Bias/Variance
- Regularized Linear Regression
- Regularized Logistic Regression
- Simplified cost function and gradient descent
- The problem of overfitting
- diamonds-analysis
- eda
- eda-fb
- explore-many-variables
- exploring-two-variables
- us-candidates-contributors-area
- Titanic
- A/B Testing Multiple Metrics
- A/B Testing Single Metric
- A/B Testing Sanity Check
- Duration of Experiment
- Size of Experiment
- Population of Experiment
- Subject of Experiment
- Variability of Metrics
- High to Low Level Metrics A/B Testing
- Difficult Metrics in A/B Testing
- A/B Testing Metrics
- Policy and Ethics in A/B Testing
- A/B Testing Overview
- Frequentist vs. Bayesian Approach
- Inference and Diagnostics for MLR
- Multiple Linear Regression
- Conditions and Inference of Linear Regression
- Introduction to Linear Regression
- Comparing categorical proportions and Chi-Square
- Hypothesis Testing and Confidence Interval for Categorical Variable
- t-distribution and ANOVA
- Paired data and Bootstrapping
- Decision Hypothesis Testing and Confidence Interval
- Hypothesis Testing
- Confidence Interval
- Introduction to Statistical Distributions
- Probability
- Intro to Inferential Statistics
- Exploratory Data Analysis
- Design Study
- Research Methods
- Visualization for Big Data
- Design Principles of Visualization
- Visualization for Multi-Dimensional Data
- Graph and Visualization
- Statistics and Exploratory Data Analysis
- Why Data Science?
- Using MapReduce and Design Pattern
- Hadoop and Big Data
- Interaction and Animation with D3.js
- Narrative Structures of Data Journalism
- Design and Principles of Data Visualization
- Dimple Basics
- D3 Basics
- Fundamentals of Data Visualization
- validation with scikit-learn
- evaluation with scikit-learn
- PCA with scikit-learn
- Feature Selection with scikit-learn
- Text Learning with scikit-learn
- Feature Scaling with scikit-learn
- K-Means with scikit-learn
- Outliers with scikit-learn
- Regression with scikit-learn
- Datasets and Question
- Random Forest with scikit-learn
- Decision Trees with scikit-learn
- Support Vector Machine with scikit-learn
- Wrangling with OpenStreetMap Data
- Naive Bayes
- Wrangling with Various Data Formats
- Bayesian Learning
- First Kaggle Competition