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    Introduction to Machine Learning
    Published on: 25th May 2018
    Posted By: Amit Kumar

    This articles provides you with fundamentals of Machine Learning by explaining supervised and unsupervised learning along with the various tasks that are performed using machine learning algorithms.

    What is Machine Learning?

    Machine Learning is the study and design of algorithms that can learn from and make predictions on data. This is achieved by building a model from the sample data, which is then used to make data-driven predictions.

    More formally, Machine learning is described by Tom Mitchell as: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E"

    Example: playing checkers.

    • E = the experience of playing many games of checkers
    • T = the task of playing checkers.
    • P = the probability that the program will win the next game.

    Machine learning can be classified as Supervised or Unsupervised. Supervised learning is the type of learning that takes place when the training instances are labelled with the correct result, which gives feedback about how learning is progressing. In unsupervised learning, the goal is harder because there are no pre-determined categorizations.

    Supervised Learning

    In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output. It is a task of inferring a function from a labelled training data. Below diagram depicts supervised learning model -

    Supervised learning problems are categorized into Regression and Classification problems as described in following sections.

    • Regression - In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. It includes modelling and analysing several variables which consist of one dependent variable and one or more independent variables. It helps understand how does the dependent variable varies when one of the independent variable is varied, keeping all other variables fixed.

      Example: Given the data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.

      Here are some of the popular Regression techniques:

      1. Linear Regression - Linear regression is used to try and fit the data into a straight line. It models the linear relationship between a dependent variable and one or more independent variables. Linear regression can be used to forecast/predict the dependent variable based on the observed data set if the relation between the variables is known to be almost linear.
      2. Locally Weighted Regression - Linear regression tries to fit a straight line to the data model, which is not a good fit in cases where the relationship is not linear or the data is too noisy. In such cases we use LWR. LWR removes the problem of linear regression by assigning weights to the training data. Weights are bigger for the data points closer to the data we are trying to predict. Since, LWR requires the entire data set every time (due to changes in weights), it is computationally expensive.
      3. Logistic Regression - Unlike linear regression where the output is a continuous function, in logistic regression the output can have only a limited number of discrete values. It is used when the dependent variable is of binary or discrete nature.
      4. Non-linear Regression - Nonlinear equations can take multiple forms. If the dependent variable cannot be modelled as a linear function of the independent variables, we use nonlinear regression to find a best fit model. The resulting model could be exponential, logarithmic, trigonometric etc.
    • Classification - In a classification problem, we are trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories on the basis of training data set. An algorithm which implements classification is known as a classifier.

      Example: Predicting whether the house "sells for more or less than the asking price", we are classifying the houses based on price into two discrete categories.

      Here are some of the popular Classification techniques:

      1. Decision Tree Classifier - This methodology uses a decision tree as the predictive model. It is used in cases where all the features have a finite discrete domain and there is a single target feature. The tree is created using the sample data where each internal node splits into 2 or more sub trees according to the discrete function of the input attribute value.
      2. Naive Bayes Classifier - Naive Bayes classifier is a family of classifiers that work on the assumption that the value of a particular feature is independent of the value of any other feature (hence naive). The model assigns class labels to the data, represented as vectors of feature values. It is based on the Bayes theorem and hence are probabilistic in nature. This classification technique is used mostly in text classification (spam/not spam or sports, politics or entertainment etc.).
      3. Random Forests Classifier - This model is an extension of decision tree classifier. Many classification trees are grown to classify a new object from an input vector. Each tree then gives a classification, and we say the tree votes for that class. The forest chooses the class which has the maximum number of votes.
      4. Hidden Markov Model Classifier - It is a statistical model of a process consisting of two random variables, say A and B, which change their state sequentially. One of the two variables, A is termed as hidden variable as its state cannot be observed directly. The state of "A" changes with Markov property, i.e. the state change probability only depends on its current state and does not change in time. The variable B is called as the observed variable since its state can be directly observed. B does not follow the Markov property, but its state probability statically depends on the current state of A.
      5. Multi-layer Perceptron - A multilayer perceptron is a biologically inspired feed-forward network that can be trained to represent a nonlinear mapping between input and output data. It consists of multiple layers, each containing multiple artificial neuron units and can be used for classification and regression tasks in a supervised learning approach.
      6. K-nearest Neighbours - In k-NN classification, the object is classified by a majority vote of its neighbours. The object is assigned to the class which is most common among its k nearest neighbours. Weights are generally assigned to the neighbours while using this algorithm.
    Unsupervised Learning

    Unsupervised learning, on the other hand, allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we do not necessarily know the effect of the variables. Following diagram depicts unsupervised learning model -

    We can derive this structure by clustering the data based on relationships among the variables in the data. With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you. It is not just about clustering. For example, associative memory is unsupervised learning.

    Unsupervised learning problems are categorized into Clustering and Collaborative filtering problems as described in following sections.

    • Clustering - Clustering is division of observation into clusters or groups such that all observations within a cluster have some similarity between them. Unlike classification, we are not aware of the types of clusters that will be formed at the end of the clustering algorithm and hence it lies under unsupervised learning.

      Here are some of the popular Classification techniques:

      1. Canopy Clustering - It is a pre-clustering algorithm used as a pre-processing step for K-Means algorithm. It is used to speed up the clustering process on large data sets, where using another algorithm directly would be impractical.
      2. K-means Clustering - K-Means clustering is used to partition n observations into k sets, where each observation belongs to the cluster with the nearest mean. In other words, the model divides the observation into k sets such that the within-cluster sum of squares is minimized.
      3. Fuzzy K-means Clustering - Unlike K-Means clustering, where each observation belongs to exactly one cluster, in Fuzzy K-Means clustering each observation can belong to multiple clusters with varying probability. Fuzzy K-Means tries to deal with the problem where points are somewhat in between centers.
      4. Streaming K-means Clustering - Streaming K-Means Clustering is used in cases when data set is too large to fit into memory as a whole. It consists of two major steps, Streaming step and BallKMeans step. In streaming step, a single pass over the data produces as many centroids as it determines is optimal. This data is then passed through the BallKMeans step which further reduces the number of centroids to K.
      5. Spectral Clustering - The goal of spectral clustering is to cluster data that is connected but not necessarily compact or clustered within convex boundaries.
      6. Mean Shift Clustering - The mean shift algorithm is a nonparametric clustering technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. It works by treating the points as an empirical probability function where dense regions correspond to local maxima.
      7. Correlation Clustering - Correlation Clustering is applied in cases where the actual data sets is not known, but the relation between the points in the data set is known. This model does not require prior knowledge of k, i.e. number of clusters to be formed.
    • Collaborative Filtering - Collaborative Filtering (CF) is the process of making automatic predictions about the interests of a user based on his interest/disinterest similarity with other users. It is based on the assumption that if a user A has the same interest as a person B on some issue, then A is more likely to have the same interest as B on some other issue x as compared to any randomly chosen person.

      Example: CF can be used to predict which food item a user would like based on the partial list of his likes and dislikes.

      Collaborative Filtering can be classified as:

      1. User-based Collaborative Filtering - This CF technique has 2 major steps. Firstly, we look for subject/users who share the same preferences or interests as the active user. Then we use the ratings received from that set of like-minder users to predict the interest of the active user. To implement this model, neighborhood based algorithms are used generally. A subset of users are choses based on their similarity to the active users and their weighted combinations is used as the predicted rating for the active user.
      2. Item-based Collaborative Filtering - Item based CF calculates the similarity between items based on the people's rating of those items. This is achieved by firstly finding similarity between all pairs of items. Once this step is completed, the system uses the most similar items to a user's already-rated items to generate a list of recommended items.
    Distributed Machine Learning Tools and Frameworks

    Many tools and frameworks have come up to help perform ML Techniques on Big Data in a distributed environment. Some of the popular ones have been listed below.

    • Apache Mahout: Apache Mahout provides implementation for scalable and distribute machine learning algorithms. Most of these implementation run on Apache Hadoop platform.
    • R: R is a free software environment for statistical computing and graphics. It has been used extensively for implementing ML algorithms. Packages in R language are available which make it possible to run these ML algorithms in a distributed environment such as Hadoop or H20. For example, when using R with H20, R tells H2O to perform a task, and then H2O returns the result back to R, which is a tiny result, but you never actually transfer the data to R.
    • Petuum: Petuum provides both tools as well as pre implemented algorithms to perform ML at large scale. The library of distributed ML algorithms can be used at massive scale for Big Data analytics.
    • Jubatus: Jubatus is a distributed processing framework and streaming machine learning library. It provides pre implemented algorithms for Classification, Regression, Recommendation (Nearest Neighbour Search), Graph Mining, Anomaly Detection, Clustering among others.

    Thank you for reading through the tutorial. In case of any feedback/questions/concerns, you can communicate same to us through your comments and we shall get back to you as soon as possible.

    Posted By: Amit Kumar
    Published on: 25th May 2018

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