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

To understand various components of a machine learning algorithm, we first understand the definition of machine learning given by Professor Mitchell −“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.”As we can see the above definition, the main components of any machine learning algorithm are Task(T), Performance(P), and Experience(E).Based on these three components, let’s simplify the definition of machine learning −Machine learning is a subset of Artificial Intelligence (AI) and a field ... Read More

Do you know how much data a person is creating every second? The numbers are astonishing. According to Domo, during 2020 every person created 1.7MB of data every second and it is not going to slow down in the future as well. It would not be wrong to say that we are living in the 'age of data'. One of the biggest challenges in front of businesses and organizations is to make sense of all the data. They are trying to deal with it by creating intelligent systems using the concepts and methodologies from Machine Learning (ML), one of the ... Read More

The objective of search in Artificial Intelligence (AI) is to find the path to solve different problems. The search in AI can be executed in two ways namely, Forward Reasoning and Backward Reasoning. The most basic difference between the two is that forward reasoning starts with the new data to find conclusions, whereas backward reasoning starts with a conclusion to determining the initial data. Read this article to learn more about Forward Reasoning and Backward Reasoning and how they are different from each other. What is Forward Reasoning? Forward reasoning is a process in artificial intelligence that finds all the ... Read More