Machine Learning Resources

General Information

• Author: Nicholas Dominic
• Programming Language: Python 3.7.1 and R-Language 4.0.1
• Environment (IDE): Jupyter Notebook and R-Studio
• Topics: Shallow (statistical-based) Learning and Deep (neural-network-based) Learning

Frequently Ask Questions (FAQ)

• Can I copy all or some of your works?
  >> You can, by specifying author's name as condition. Or simply do GitHub's fork from my repository.
• I can't open your .ipynb file, how to resolve the issue?
  >> Copy the url (with .ipynb extension) and paste it to this website.
• May I commit a change in your code?
  >> You may. But only reliable changes will acquire my acceptance, as soon as possible.

Contents

The unmarked (active) content denotes unfinished development of codes (still on progress).
Feel free to give suggestions (through contact I attached below) about what topic should I discover more.
All references or development resources are credited within each notebook.

Linear Regression

• Data Exploration and Visualization
• Data Preprocessing Phase 1: Data Cleaning
• Data Preprocessing Phase 2: Data Integration¹
• Data Preprocessing Phase 3: Data Reduction / Compression²
• Data Preprocessing Phase 4: Data Transformation³
• Linear Least Squares
• Regularization: LASSO / Ridge / Elastic-Net
• Linear Regression
• Logistic Regression
• Polynomial Regression
• Case Study: Movie Data Analysis

Forecasting

• Time Series Data Exploration
• Time Series Data Preprocessing: Upsampling and Downsampling
• Time Series Data Engineering
• Time Series Data Visualization
• Quantitative Forecasting: Smoothing Techniques
• Quantitative Forecasting: Seasonality Techniques
• Quantitative Forecasting: Causal Techniques⁴
• Quantitative Forecasting: Auto Regressive and Moving Average Model⁵
• Forecasting Evaluation

Statistics for Data Science

• Statistical Description of Data 1: The Central Tendencies
• Statistical Description of Data 2: The Dispersion of Data
• Statistical Description of Data 3: The Visualization of Data
• Quantitative Data Attributes Proximity / Dissimilarity

Machine Learning in Python

• Supervised Learning: (Discrete) Classification using Keras Sequential API
• Supervised Learning: (Continuous) Regression using Keras
• Supervised Learning: Decision Tree and Random Forest
• Supervised Learning: Support Vector Machines Regressor and Classifier
• Unsupervised Learning: (Centroid-based) K-Means
• Unsupervised Learning: (Density-based) DBSCAN and OPTICS^6</sup
• Unsupervised Learning: Self-Organizing Map (SOM)
• Recurrent Unit: Recurrent Neural Network (RNN)⁷
• Recurrent Unit: Deep Recurrent Neural Network (DRNN)
• Recurrent Unit: Gated Recurrent Unit (GRU)
• Recurrent Unit: Long Short Term Memory (LSTM)
• Generative Model 1: Convolutional Autoencoders
• Generative Model 1: Denoising Autoencoders
• Generative Model 1: Recurrent Autoencoders
• Generative Model 1: Sparse Autoencoders
• Generative Model 1: Stacked Autoencoders
• Generative Model 1: Variational Autoencoders
• Generative Model 2: Generative Adversarial Network (GAN)

Machine Learning in R

• R-Language Introduction
• Data Loading and Visualization in R
• Fashion-MNIST Data Classification in R using Keras Sequential API
• Fashion-MNIST Data Regression in R using Keras Functional API
• MTCARS Data Hierarchical Clustering in R

Unfinished Development List
¹ Data Value Conflict / Discrepancy Detection
² Evolutionary Selection Algorithm; Wavelet Transform; Non/Parametric Methods
³ Data Discretization; Hierarchy Generation Concept
⁴ Trend Projection / Intra-Extrapolation
⁵ ARIMAX; SARIMAX
⁶ Cluster Plot for OPTICS
⁷ Low Accuracy for RNN Model to Forecast

Author Detail Information

NICHOLAS DOMINIC

• Education: Graduate Student in BINUS University Computer Science (AI stream)
• Email: [email protected] / [email protected] / [email protected]
• LinkedIn Profile: click here

Do me a favor to share my works and freely contact me for further recognition. Have a great rest of your day!