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This article was written by Mohammad Sajid. Statistical cluster analysis is an Exploratory Data Analysis Technique which groups heterogeneous objects(M.D.) into homogeneous groups. We will learn the basics of cluster analysis with mathematical way. Cluster Analysis can be done by two methods:Hierarchical cluster analysis.Non-Hierarchical cluster analysis. Hierarchical Cluster Analysis(HCA):In HCA, the observation vector(cases) are grouped together on the basis of their mutual distance.An HCA is usually visualised through a hierarchical tree called dendrogram tree. This hierarchical tree is a nested set of partitions represented by a tree diagram.Characteristics of HCA:Sectioning a tree at a particular level produces a partition into ‘g’ disjoint groups.If 2 groups are chosen from different partitions then either the groups are disjoint or 1 group is totally contained within the other.A numerical value is associated with each partition of the tree where branches join together. This value is a measure of distance or dissimilarity between two merged clusters.Different distance measures give rise to different hierarchical clusters structure.There are two types of approaches for HCA: Agglomerative HCADivisive HCAAgglomerative HCA: Operates by successive merges of cases.Begin with clusters, each containing single cases.At each stage merge the 2 most similar group to form a new cluster, thus reducing the number of the cluster by n.Continue till(eventually as similarity decreases) all subgroups are fused to form one single cluster.Divisive HCA: The divisive method operates by the successive splitting of groups.Initially starts with a single group(i.e. one single cluster).Group is divided into 2 types: 1) The objects in one subgroup are as far as possible from the objects in the other group. 2) Continue till there are ‘n’ groups, each with a single cluster. To read the rest of the article, click here. See More

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This article was written by Bob Hayes.Data science requires the effective application of skills in a variety of machine learning areas and techniques. A recent survey by Kaggle, however, revealed that a limited number of data professionals possess competency in advanced machine learning skills. About half of data professionals said they were competent in supervised machine learning (49%) and logistic regression (53%). Deep learning techniques were among the ML skills with the lowest competency rates: Neural Networks – GAN (7%); NN – RNNs (15%) and NN – CNNs (26%).A majority of enterprises (80%) have some form of artificial intelligence (machine learning, deep learning) in production today. Additionally, about a third of enterprises are planning on expanding their AI efforts over the next 36 months. But who will lead these data science projects? Who will do the work? Some researchers suggest there is a lack of AI talent needed to fill those roles. Tencent estimates there are only 300,000 AI researchers and practitioners worldwide. ElementAI estimates there are 22,000 PhD-level researchers working in AI.Kaggle conducted a survey in August 2017 of over 16,000 data professionals (2017 State of Data Science and Machine Learning). The survey asked respondents about their competence across a variety of AI-related approaches and techniques. Looking at different AI skills will give us a more detailed look into the specific AI skills that are driving this talent gap.Competency in Machine Learning AreasAll respondents (employed or not) were were given a list of 13 machine learning areas and asked to indicate in which areas they consider themselves competent. The top 10 machine learning areas in which data professionals are competent were: Supervised Machine Learning (49%) Unsupervised Learning (26%) Time Series (25%) Natural Language Processing (19%) Outlier detection (16%) Computer Vision (15%) Recommendation Engines (14%) Survival Analysis (8%) Reinforcement Learning (6%) Adversarial Learning (4%)Competency in Machine Learning TechniquesThe survey included a question for all data professionals, employed or not, regarding their competency in 13 machine learning techniques (In which areas of machine learning do you consider yourself competent? (Select all that apply).) The top 10 machine learning techniques in which data pros are competent were : Logistic Regression (54%) Decision Trees – Random Forests (43%) Support Vector Machines (32%) Decision Trees – Gradient Boosted Machines (31%) Bayesian Techniques (27%) Neural Networks – CNNs (26%) Ensemble Methods (22%) Gradient Boosting (17%) Neural Networks – RNNs (15%) Hidden Markov Models HMMs (9%)To read the whole article, with illustrations, click here.See More

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This article was written by Montana Low. An open source framework for configuring, building, deploying and maintaining deep learning models in Python.As Instacart has grown, we’ve learned a few things the hard way. We’re open sourcing Lore, a framework to make machine learning approachable for Engineers and maintainable for Machine Learning Researchers. Common ProblemsPerformance bottlenecks are easy to hit when you’re writing bespoke code at high levels like Python or SQL.Code Complexity grows because valuable models are the result of many iterative changes, making individual insights harder to maintain and communicate as the code evolves in an unstructured way.Repeatability suffers as data and library dependencies are constantly in flux.Information overload makes it easy to miss newly available low hanging fruit when trying to keep up with the latest papers, packages, features, bugs… it’s much worse for people just entering the field.To address these issues we’re standardizing our machine learning in Lore. At Instacart, three of our teams are using Lore for all new machine learning development, and we are currently running a dozen Lore models in production. TLDRIf you want a super quick demo that serves predictions with no context, you can clone my_app from github. Skip to the Outline if you want the full tour. Feature SpecsThe best way to understand the advantages is to launch your own deep learning project into production in 15 minutes. If you like to see feature specs before you alt-tab to your terminal and start writing code, here’s a brief overview:Models support hyper parameter search over estimators with a data pipeline. They will efficiently utilize multiple GPUs (if available) with a couple different strategies, and can be saved and distributed for horizontal scalability.Estimators from multiple packages are supported: Keras, XGBoost and SciKit Learn. They can all be subclassed with build, fit or predictoverridden to completely customize your algorithm and architecture, while still benefiting from everything else.Pipelines avoid information leaks between train and test sets, and one pipeline allows experimentation with many different estimators. A disk based pipeline is available if you exceed your machines available RAM.Transformers standardize advanced feature engineering. For example, convert an American first name to its statistical age or gender using US Census data. Extract the geographic area code from a free form phone number string. Common date, time and string operations are supported efficiently through pandas.Encoders offer robust input to your estimators, and avoid common problems with missing and long tail values. They are well tested to save you from garbage in/garbage out.IO connections are configured and pooled in a standard way across the app for popular (no)sql databases, with transaction management and read write optimizations for bulk data, rather than typical ORM single row operations. Connections share a configurable query cache, in addition to encrypted S3 buckets for distributing models and datasets.Dependency Management for each individual app in development, that can be 100% replicated to production. No manual activation, or magic env vars, or hidden files that break python for everything else. No knowledge required of venv, pyenv, pyvenv, virtualenv, virtualenvwrapper, pipenv, conda. Ain’t nobody got time for that.Tests for your models can be run in your Continuous Integration environment, allowing Continuous Deployment for code and training updates, without increased work for your infrastructure team.Workflow Support whether you prefer the command line, a python console, jupyter notebook, or IDE. Every environment gets readable logging and timing statements configured for both production and development. To read the whole article, click here.See More

This article was written by Stuart Reid. This tutorial covers regression analysis using the Python StatsModels package with Quandl integration. For motivational purposes, here is what we are working towards: a regression analysis program which receives multiple data-set names from Quandl.com, automatically downloads the data, analyses it, and plots the results in a new window. TYPES OF REGRESSION ANALYSISLinear regression analysis fits a straight line to some data in order to capture the linear relationship between that data. The regression line is constructed by optimizing the parameters of the straight line function such that the line best fits a sample of (x, y) observations where y is a variable dependent on the value of x. Regression analysis is used extensively in economics, risk management, and trading. One cool application of regression analysis is in calibrating certain stochastic process models such as the Ornstein Uhlenbeck stochastic process. Non-linear regression analysis uses a curved function, usually a polynomial, to capture the non-linear relationship between the two variables. The regression is often constructed by optimizing the parameters of a higher-order polynomial such that the line best fits a sample of (x, y) observations. In the article, Ten Misconceptions about Neural Networks in Finance and Trading, it is shown that a neural network is essentially approximating a multiple non-linear regression function between the inputs into the neural network and the outputs.The case for linear vs. non-linear regression analysis in finance remains open. The issue with linear models is that they often under-fit and may also assert assumptions on the variables and the main issue with non-linear models is that they often over-fit. Training and data-preparation techniques can be used to minimize over-fitting.A multiple linear regression analysis is a used for predicting the values of a set of dependent variables, Y, using two or more sets of independent variables e.g. X1, X2, ..., Xn. E.g. you could try to forecast share prices using one fundamental indicator like the PE ratio, or you could used multiple indicators together like the PE, DY, DE ratios, and the share's EPS. Interestingly there is almost no difference between a multiple linear regression and a perceptron (also known as an artificial neuron, the building blocks of neural networks). Both are calculated as the weighted sum of the input vector plus some constant or bias which is used to shift the function. The only difference is that the input signal into the perceptron is fed into an activation function which is often non-linear.If the objective of the multiple linear regression is to classify patterns between different classes and not regress a quantity then another approach is to make use of clustering algorithms. Clustering is particularly useful when the data contains multiple classes and more than one linear relationship. Once the data set has been partitioned further regression analysis can be performed on each class. Some useful clustering algorithms are the K-Means Clustering Algorithm and one of my favourite computational intelligence algorithms, Ant Colony Optimization.The image below shows how the K-Means clustering algorithm can be used to partition data into clusters (classes). Regression can then be performed on each class individually.Logistic Regression Analysis - linear regressions deal with continuous valued series whereas a logistic regression deals with categorical (discrete) values. Discrete values are difficult to work with because they are non differentiable so gradient-based optimization techniques don't apply.Stepwise Regression Analysis - this is the name given to the iterative construction of a multiple regression model. It works by automatic selecting statistically significant independent variables to include in the regression analysis. This is achieved either by either growing or pruning the variables included in the regression analysis.Many other regression analyses exist, and in particular, mixed models are worth mentioning here. Mixed models is is an extension to the generalized linear model in which the linear predictor contains random effects in addition to the usual fixed effects. This decision tree can be used to help determine the right components for a model. To read the whole article, with illustrations, click here. See More