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Machine Learning: Unsupervised Learning

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Unsupervised Learning deals with finding patterns in unlabeled data. It does not exactly help in predicting something, but it helps us to cluster the data on some pattern using we which we can come to some conclusion about the data and make business decisions on it. Types of Unsupervised Learning are: Clustering : It involved finding natural clusters in a dataset if they exist. The criteria of clustering can be very simple for example Gender or complex as purchase preferences. There are different types of clustering that can be utilized: K-Means Clustering, Hierarchical Clustering, Probabilistic Clustering . Data Compression : It is one of the goals that can be achieved using unsupervised learning. Since the amount of data is increasing day by day and we require more and more storage space to store that data. It can be achieved by a process called Dimensionality Reduction. Popular algorithms used for dimensionality reduction are: Principal Component Analysis (PCA), Linear Discri...
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Neural Networks

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General machine learning algorithms have achieved a great feat in predicting and classifying complex datasets but sometimes fail to do with an accuracy as good as a normal human being. To solve this issue we use Neural Networks, which are like computer models of a human brain. Perceptron: A basic unit in a neural network: Linear separator with n inputs, weights for each input, w1,...., wn. A bias input x0 and associated weight w0. Weighted sum of inputs A threshold function A neuron has a real-valued output which is a weighted sum of its inputs. If the data is linearly seperable it will converge to a hypothesis that classifies all training data correctly in a finite number of iterations. An activation function is required at each level to move the data in the forward direction. Perceptrons are very limited. They cannot represent more complex scenarios. A solution to that is multiple layers. Two layers can be used to represent any boolean network. Back-propaga...
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Machine Learning: Supervised Learning 3

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Bayesian Network Bayesian Network is a type of probabilistic graphical model that uses Bayesian inference for probability computation. Representation of casual relations makes the representation and inference efficient.  Bayesian Network It is a graphical model that efficiently encodes the joint probability distribution for a large set of variables. Arcs represent probabilistic dependence on variables. Lack of an arc represents independence. The network structure is a directed acyclic graph. Support Vector Machines It is a machine learning algorithm that is used in classification and regression. But it is mostly used in classification problems. What it basically does is, it plots each data item as a point in n-dimensional space with values of each feature being values of a particular point and then separates the classes using a hyper-plane. The line that separates the classes is determined by a subset of points know as Support Vectors. Points with a ...
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Machine Learning: Supervised Learning 2

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In the previous post, I talked about Linear Regression which predicts continuous values. But In the real world most of the time we are classifying things to distinguish them from one another. The next algorithm I am going to talk about is a classification algorithm. Naive Bayes It is a really fast classification algorithm based on the Bayes theorem. It works on the Bayes Theorem of probability. Bayes Theorem We make two assumptions when we use this algorithm. One is that each feature is independent of each other and all predictors have an equal effect on the outcome.  What this algorithm does is it first converts the data into a frequency table and calculates the conditional probability for each event. Final function that is used to give prediction is  Types of Naive Bayes Classifiers: Multinomial Naive Bayes: This is mostly used for document classification problem, i.e whether a document belongs to the category of sports, politics, technology...
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Machine Learning: Supervised Learning 1

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Supervised Learning is a technique in which we train the computer using labeled data which means that some data is already tagged in with the correct answers. We will be implementing these algorithms in python. Here are the steps you need to follow to set up your computer for machine learning. If you don't want to go for deep learning right now you can skip step 4,5,6. The first algorithm we are going to see is: Linear Regression Regression deals with predicting continuous values. It helps to estimate target values such as prices, temperature, population, etc. Linear regression is the simplest form of regression. The predictions that this model will provide in the future will all fall on the regression line that is formed during the training. Let's take an example of predicting income. So, our input data (X) will contain features (columns of information). These features can be numerical (eg. Years of Experience) or categorical (eg. Role). We...
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Machine Learning: An Introduction

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Hello guys, this is a new series in which I'll be talking about "Machine Learning" as it is one the most booming fields in the IT industry and as a student of Information Technology, I should try to share my knowledge on this topic. Here I'll be discussing all the major topics and their implementation and some theoretical concepts behind them. Also, I'll be providing links to various topics and documentations from where you can learn about them as there are already many excellent explanations scatter throughout the internet. So, without further ado, LET'S GET STARTED ... What is Machine Learning? It is an act of making computers learn something without being explicitly programmed. They learn from the data that we provide them. The data might be labeled or non-labeled. There are several steps involved in Machine Learning: Collecting Data : In this step, we collect the necessary data that will be required to build the model. The variety, de...
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Is 18:9 screen ratio worth the hype ?

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Hello guys, it's been a while since I posted something on my blog. So, Let's start with something which is trending right now in the smartphone market. As you would have guessed it from the title of this blog, I am going to talk about, is the 18:9 aspect ratio worth the hype? So, let's get started. First, the screen aspect ratio has nothing to do with the size of the screen. The length of the diagonal represents the size of the screen. The aspect ratio of the screen is the Length of the screen: Width of the Screen. Samsung A8 2018 What is wrong with the good old 16:9 screen?   -- Well quite honestly there's is nothing wrong with the 16:9 scaled displays. We can get a bezel-less design with them too. But like everything in the tech industry aspect ratio of a mobile screen has to change too. Actually, most of the video content you see on your phone or your computer is on 16:9 scaling. So, why the change?     As I said everything in the Tech Indu...
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