Artificial Intelligence Articles - Page 38 of 41

There are various applications of machine learning which are as follows −Social media services − Machine learning is an essential role in personalizing news feed to superior advertisement focusing over social media. Facebook needs machine learning to display news feed to the user based on its interests by treating items clicked earlier by that user.Facebook always takes note of the friends that it can linked with, the profiles that it can visit, interests, workplace, and on the basis of this continuous learning, a file of Facebook users are suggested for us to become friends with.The Face Recognition nature of Facebook ... Read More

Machine learning is an application of Artificial Intelligence that supports an architecture with the capability to learn and enhance from experience without being definitely programmed automatically.It can be used by search engines including Google and Bing to rank internet pages or to determine which advertisement to display to which user. It can be used by social networks including Facebook and Instagram to make a custom feed for each user or to tag the customer by the images that was uploaded.It can be used by banks to identify whether an online transaction is fraudulent and by e-commerce websites including Amazon and ... Read More

To successfully build a machine learning project, loading data properly is one of the most important as well as challenging tasks. CSV is the most common format for machine learning projects. It is a simple format which is used to store tabular data.Followings are the three most common approaches in Python with the help of which you can load CSV data for machine learning projects −Using Python Standard LibraryTo load CSV data files, Python standard library provides us with a built-in function namely csv module.ExampleIn this example we will be loading CSV data file of iris flower data set −#Importing ... Read More

In reinforcement learning methods, a trained agent interacts with a specific environment and takes actions based upon the current state of that environment.The working of reinforcement learning is as follows −First you need to prepare an agent with some specific set of strategies.Now leave the agent to observe the current state of the environment.Based on the agent's observation, select the optimal policy, and perform suitable action.Based on the action taken, the agent will get reward or penalty.Update the set of strategies used in step 1, if needed. Repeat the process from step1-4 until the agent learns and adopts the optimal ... Read More

Unsupervised learning methods, (opposite to supervised learning methods) do not require any labels or responses along with the training data. Unsupervised ML algorithms learn patterns and relationships from the given raw data. Although these methods may have lots of uncertainties in the result, we can always obtain some useful information such as all kinds of unknown patterns in the data, and the useful features for categorization.Unsupervised algorithms are called unsupervised because the machine learning model learns from data samples where the output is not known in advance.Let’s make it clearer, suppose we have, Input variables − mBut as opposed to ... Read More

Supervised learning, one of the most used methods in ML, takes both training data (also called data samples) and its associated output (also called labels or responses) during the training process. The major goal of supervised learning methods is to learn the association between input training data and their labels. For this it performs multiple training data instances.Let’s understand its working with the help of below given example −Suppose we have, Input variables − m andOutput variable − NThe mapping function from the input to output is as follows −𝑁 = 𝑓(𝑚)To learn such mapping function, we need an algorithm ... Read More

There are four learning styles in machine learning algorithms. Let’s have a look at them −Supervised LearningSupervised learning, one of the most used methods in ML, takes both training data (also called data samples) and its associated output (also called labels or responses) during the training process. The major goal of supervised learning methods is to learn the association between input training data and their labels. For this it performs multiple training data instances.Based on machine learning based tasks, we can divide supervised learning algorithms in two classes namely Classification and Regression.Unsupervised LearningUnsupervised learning methods, (opposite to supervised learning methods) ... Read More

The Python ecosystem, growing at a rapid pace day by day, became the dominant platform for machine learning. Here we will discover the most useful components of the Python ecosystem for machine learning. Let’s get started.SciPySciPy, pronounced as “Sigh Pie”, is an ecosystem of Python open-source libraries for performing Mathematical, Scientific, and Engineering computations. SciPy is comprised of the following core packages relevant to machine learning −NumPy − NumPy is a base N-dimensional array package for SciPy that allows us to efficiently work with data in arrays.Matplotlib − Matplotlib is used to create comprehensive 2-D charts and plots from data.Pandas ... Read More

From process automation to web development to AI-based projects to machine learning, Python is used everywhere, and it helps developers to be productive and confident about the software they are building. Today, because of the benefits like simplicity, consistency, extensive set of libraries, platform independence, flexibility, and a wide community support, Python has become one of the most favored programming languages among machine learning professionals.Simplicity and Consistency − Machine learning relies on complex algorithms and workflows, but it is Python’s simplicity that allows machine learning developers to build reliable applications. Python is so simple that the developers do not need ... Read More

While machine learning is rapidly evolving, it still has a long way to go. The reasons behind this are the various challenges an ML practitioner faces while developing an application. Let’s take a look at these challenges −Data collection − Data plays the most important role in developing any machine learning application. Most of the work of an ML practitioner lies in collecting good quality data. If you are a beginner and want to experiment with machine learning, you can find datasets from Kaggle or UCI ML Repository. But if you want to implement real case scenarios or need to ... Read More