Neural Nets and Deep Learning

Introduction to Neural Network Theory Neural networks are modelled after biological neural networks and attempt to allow computers to learn similarly like a human – reinforcement learning. The applicability of neural networks include pattern recognition, signal processing, anomaly detection and etc… Neural networks attempt to solve problems that would normally …

Natural Language Processing

Introduction to Natural Language Processing Wikipedia – “Natural language processing (NLP) is a field of computer science concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Challenges in natural language processing frequently involve speech recognition, natural language understanding, natural language generation (frequently from formal, machine-readable logical forms), connecting language and …

Recommender Systems

Introduction to Recommender Systems A recommender system is a subclass of information filtering system that seeks to predict the “rating” or “preference” that a user would give to an object. The two most common types of recommender systems are Content-Based and Collaborative Filtering (CF). Collaborative filtering produces recommendations based on the knowledge of users’ attitude to items, …

Unsupervised Learning: Principal Component Analysis

Introduction to Principal Component Analysis Principal Component Analysis (PCA) is an unsupervised statistical technique used to examine the interrelations among a set of variables in order to identify the underlying structure of those variables. It is also known as a general factor analysis. Factor analysis determines several orthogonal lines of …

Unsupervised Learning: K Means Clustering

Introduction to K Means Clustering K Means Clustering is an unsupervised learning algorithm that will attempt to group similar clusters together in your data. A typical clustering problem involves identifying similar physical groups, market segmentation, cluster customers based on their features, and etc… The overall objective is to divide data …

Supervised Learning: Support Vector Machines

Introduction to Support Vector Machines Support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyse data and recognise patterns, used for both classification and regression analysis. Given a set of training examples, each marked for belonging to one of the two categories, an SVM training algorithm …

Supervised Learning: Decision Trees and Random Forests

Introduction to Decision Trees and Random Forests Wikipedia – “Decision tree learning uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item’s target value (represented in the leaves). Tree models where the target variable can take a discrete set of …