The following pages and posts are tagged with

K-nearest Neighbors (KNN) Classification Model Page Train a KNN classification model with scikit-learn
Ensemble Learning, Adaboost Page Bagging, boostrap aggregation, boosting, and adaboost as a boosting method.
Anomaly Detection Page Density estimation, anomaly detection system, and multivariate gaussian distribution.
Applying Machine Learning Page Evaluating machine learning algorithms, training set, cross validation set, test set, bias, variance, learning curves and improving algorithm performance.
Cross-Validation Page Cross-Validation for Parameter Tuning, Model Selection, and Feature Selection
Datasets for Machine Learning Page A list of datasets for machine learning.
Decision Trees Page Decision Trees, ID3, Entropy, Information again and more.
Deep Learning Essential Terms Page Essential terms for understanding deep learning research papers, tutorials and textbooks.
Machine Learning Systems Design Page Spam classifier example, error analysis, skewed data, precision, recall and large data sets.
Dimensionality Reduction and Feature Transformation Page Dimensionality reduction and feature transformation with scikit-learn.
Dimensionality Reduction Page Motivation of dimensionality reduction, Principal Component Analysis (PCA), and applying PCA.
Machine Learning & Econometrics Page Applying Machine Learning to Econometrics and Public Policy.
Optimal Tuning Parameters Page Efficiently Searching Optimal Tuning Parameters
Evaluating a Classification Model Page ROC, AUC, confusion matrix, and metrics
F1 Score Page Evaluate classification models using F1 score.
Feature Engineering and Scaling Page Feature engineering and scaling with scikit-learn.
Game Theory Page Game theory is increasingly relevant in reinforcement learning where we have multiple agents. Understand the concept of Nash Equilibrium.
Gaussian Naive Bayes Page Gaussian naive bayes, bayesian learning, and bayesian networks
Machine Learning Introduction Page An easy introduction to machine learning
Machine Learning Overview Page Machine Learning theory and applications using Octave or Python.
IPython Introduction Page Quick IPython introduction for machine learning
Iris Dataset Page Getting started with the famous Iris dataset
Machine Learning Journal Library Page My personal list of journals I use for my research and projects where I wrote one-sentence summaries.
Clustering with KMeans Page Clustering with KMeans in scikit-learn.
Large Scale Machine Learning Page Gradient descent with large data, stochastic gradient descent, mini-batch gradient descent, map reduce, data parallelism, and online learning.
Learning Curve Page Evaluate bias and variance with a learning curve
Linear Algebra for Machine Learning Page Matrices, vectors, addition, scalar multiplication, matrix vector multiplication, matrix matrix multiplication, properties of matrix multiplication, inverse ...
Machine Learning Linear Regression Page Machine Learning introduction by Data School
Evaluating a Linear Regression Model Page Confidence in model, hypothesis testing, p-values, feature selection, train/test split
Logistic Regression Page Classification, logistic regression, advanced optimization, multi-class classification, overfitting, and regularization.
Markov Decision Processes Page Solve MDPs' equations and understand the intuition behind it leading to reinforcement learning.
Linear Regression with Multiple Variables Page Linear Regression with Multiple Variables.
Vectorization, Multinomial Naive Bayes Classifier and Evaluation Page Machine learning with text using Machine Learning with Text - Vectorization, Multinomial Naive Bayes Classifier and Evaluation
Neural Networks (Learning) Page Cost function, back propagation, forward propagation, unrolling parameters, gradient checking, and random initialization.
Neural Networks (Representation) Page Non-linear hypothesis, neurons and the brain, model representation, and multi-class classification.
Deep Convolutional Networks Page Deep convnets for image recognition
Building a Deep Neural Network Page Build a deep neural network with ReLUs and Softmax.
Intoduction to Deep Neural Networks Page Similarities to normal neural networks and supervised learning.
One Hot Encoding in Scikit-Learn Page Convert categorical data into numerical data automatically
Machine Learning Photo OCR Page Pipeline, sliding windows, artificial data synthesis, and ceiling analysis.
Polynomial Regression Page Polynomial regression with scikit-learn
Boston Home Prices Prediction and Evaluation Page Exploring data with pandas, numpy and pyplot, make predictions with a scikit-learn, evaluate using R_2, k-fold cross-validation, learning curves, complexity ...
Identifying Customer Segments (Unsupervised Learning) Page Unsupervised learning application by identifying customer segments.
Smart Cab Page Train a smart cab.
Building a Student Intervention System Page Exploring data with pandas, numpy and pyplot, preprocess data through convertion of non-numerical data to numerical data, make predictions with appropriate s...
Titanic Survival Data Exploration Page Exploring data with pandas, numpy and pyplot, make predictions with a simple custom algorithm, and calculate and compare accuracy with a simple custom algori...
Recommender Systems Page Predicting movie ratings, collaborative filtering, and low rank matrix factorization.
Reinforcement Learning Page Understand the intuition behind MDPs leading to Reinforcement Learning and the Q-learning algorithm.
Machine Learning Resources Page Discover how you can become a machine learning engineer with free and paid online resources.
Support Vector Machines (SVMs) Page Machine Learning theory and applications using Octave or Python.
Support Vector Machines Page Predicting and hyperparameters tuning
Convolutional Neural Networks with TensorFlow Page Build convolutional neural networks with TensorFlow
Deep Neural Networks with TensorFlow Page Build a deep neural networks with ReLUs and Softmax.
Exploring notMNIST with TensorFlow Page Import, preprocess and visualize notMNIST.
Regularization with TensorFlow Page Prevent overfitting with dropout and regularization.
Unsupervised Learning Page K-Means algorithm, optimization objective, random initialization, and choose number of clusters.
IT Youth of the Year Award Post I am honored to receive the IT Youth Leader of the Year 2019 Award for my industry and non-profit contribution in AI at, NUS, Deep Learning Wi...
To the future of AI in and beyond Africa Post I see future deep learning researchers and applied machine intelligence experts who have the potential to make groundbreaking changes in the fields of health...
Recap of 2018 NExT++ AI Workshop Post AI in Health and Finance
Recap of 2018 PyTorch Developer Conference Post Invited by PyTorch to PyTorch's first Devcon!
NVIDIA Image Classification at NUS-NUHS-MIT Datathon Post Singapore's AI healthcare datathon with NUS, NUHS, and MIT.
NVIDIA Object Detection with DIGITS 2018 Post NUS NVIDIA DLI Workshop 2
NVIDIA Image Classification with DIGITS 2018 Post NUS NVIDIA Inaugural Workshop Review
NVIDIA Deep Learning Workshop 2018 Post NUS NVIDIA Inaugural Workshop
I am an NVIDIA Deep Learning Institute Instructor! Post I am grateful to NVIDIA for giving me the opportunity to enable people to leverage on Deep Learning.
Practical Deep Learning with PyTorch Post A course by deep learning wizard on practical deep learning with PyTorch
Deep Learning for Self-Driving Cars and Medical Diagnostics by NVIDIA Post A talk and tutorial by NVIDIA at The Hangar, NUS Enterprise
REWORK Deep Learning Summit Singapore Post Presentation on efficient scalable hyperparameter optimization.
Gloqo: Search for code for research papers on arXiv Post It's basically a google for quickly finding code for research papers.
Completed Data School's Pandas Q&A Series Post I have gone on from Andrew Ng's class on Machine Learning to applying the concepts using scikit-learn and wrangling data using Pandas through Data School's t...
Completed Data School's free Machine Learning tutorials Post I have gone on from Andrew Ng's class on Machine Learning to applying the concepts using scikit-learn and wrangling data using Pandas through Data School's t...
Completed Machine Learning by Andrew Ng, Stanford University! Post After almost a month or so, I have completed Andrew Ng's course on Machine Learning!
Machine Learning by Andrew Ng, Stanford University Post I just started on Machine Learning by Andrew Ng from Stanford University.