## Future of Artificial Intelligence; Its Implementations and Limits Artificial Intelligence (AI) in computer science uses mechanical and computational processes to echo almost all aspects of human intelligence. AI can perform multiple functions: sensory interaction with the environment and the ability to make decisions about events that haven’t happened yet without any human assistance whatsoever. Targeted advertising and virtual agents that recognize your behaviour patterns are many standards in today’s online undertakings. Artificial Intelligence is used by business enterprises in data analysis algorithms which have the highest advantage of analyzing the Big Data, and it also involves customer engaging techniques. Apart from IBM, which developed some of the earliest functions of AI, Google and Facebook are also using AI for the analytic purpose of the massive amount of data they receive.

## Building and evaluating Naive Bayes classifier with WEKA This is a follow-up post from previous where we were calculating Naive Bayes prediction on the given data set. This time I want to demonstrate how all this can be implemented using the WEKA application. I highly recommend visiting their website and getting the latest release. WEKA is are a compelling machine learning software written in Java. You can find plenty of tutorials on youtube on how to get started with WEKA. So I won’t get into details. I’m sure you’ll be able to follow anyway.

## Simple explanation of Naive Bayes classifier Probably you’ve heard about Naive Bayes classifier and likely used in some GUI based classifiers like WEKA package. This is a number one algorithm used to see the initial results of classification. Sometimes surprisingly it outperforms the other models with speed, accuracy and simplicity. Lets see how this algorithm looks and what does it do. As you may know algorithm works on Bayes theorem of probability which allows to predict the class of unknown data set. Hoe you are comfortable with probability math – at least some basics.

## Linear regression with multiple features Single feature linear regression is simple. All you need is to find a function that fits training data best. It is also easy to plot data and learning curve. But in reality regression analysis is based on multiple features. So in most cases, we cannot imagine the multidimensional space where data could be plotted. We need to rely on the methods we use. You have to feel comfortable with linear algebra where matrices and vectors are used. If previously we had one feature (temperature) now we need to introduce more of them. So we need to expand hypothesis to accept more features. From now and later on instead of output y we are going to use h(x) notation: As you can see with more variables (features), we also end up with more parameters θ that has to be learned. Before we move let’s find suitable data that we could use for building machine learning algorithm. The data set Again we are going to use data set college cengage. This time we select health data set with several variables. The data (X1, X2, X3, X4, X5) are by city. X1 = death rate per 1000 residents X2 = doctor availability per…

## Linear regression – learning algorithm with Python In this post, we are going to demystify the learning algorithm of linear regression. We are going to analyze the simplest univariate case with single feature X wherein the previous example was temperature and output was cricket chirps/sec. Let’s use the same data with crickets to build learning algorithm and see if it produces a similar hypothesis as in excel. As you may already know from this example, we need to find linear equation parameters θ0 and θ1, to fit line most optimally on the given data set: y = θ0 + θ1 x x here is a feature (temperature) and y – output value (chirps/sec). So how we are going to find parameters θ0 and θ1? The whole point of the learning algorithm is doing this iteratively. We need to find optimal θ0, θ1 parameter values, so that approximation line error from the plotted training set is minimal. By doing successive corrections to randomly selected parameters we can find an optimal solution. From statistics, you probably know the Least Mean Square (LMS) algorithm. It uses gradient-based method of steepest descent.

## Simplest machine learning algorithm – linear regression with excel Some may say that linear regression is more statistical problem. And this is truth at some level. But when problem is solved from machine learning perspective, things gets easier especially when moving towards more complex problems. First of all lets understand few important terms. We can start with regression. When speaking of linear regression we try to find best fitting line through given points. In other words we need to find optimal linear equation to fit given data points. This is a supervised learning problem when we have set of data pairs that can be plot on x-y axis. I understand, theory is boring thing, even for me, so lets move to practical example and learn by solving some problem. In order to work with some examples we need sample data. There are many data sources available on internet. For instance great source is college cengage that have several sets with data pairs meant for linear regression problems. For our example we are going to use Cricket Chirps Vs. Temperature data where each data point consists of chirps/sec and temperature in degrees Fahrenheit. You can send data in three formats: excel, mtp and ascii. Lets download excel data where we can…

## Overview of machine learning algorithms A few years ago, machine learning caught my attention, and since then, my interest in this field keeps growing. Every day we see more and more intelligent solutions surrounding us. You probably noticed that shopping sites adapt to our interests and suggests targeted offers; another example is spam email filters; if we mark emails as spam, they keep disappearing from our lives. Another area is robotics, where they learn how to navigate independently and perform various tasks. Machine learning algorithms cover autonomous flying robots, helicopters, quads, handwriting recognition, computer vision, data mining, and multiple fields like markets, biomedicine, biology – all this.  Here are a few reasons why machine learning is significant and sometimes necessary: Data mining. Sometimes it isn’t possible to understand the nature data and relations between them so that machine learning algorithms can extract these hidden relations. Adaptation. It is hard to design a flexible algorithm that could adapt to a changing environment. Machine learning algorithms can be used to improve themselves according to changing data. Scale. There can be much knowledge in data sets that sometimes aren’t completely understood by a human designer. Machine learning algorithms can learn from this knowledge without human interaction. Complexity. The…