The following pages and posts are tagged with

Title | Type | Excerpt |
---|---|---|

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. |

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. |