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### Understand Types of Environments in Artificial Intelligence

The Environment is the surrounding world around the agent which is not part of the agent itself. It’s important to understand the nature of the environment when solving a problem using artificial intelligence. For example, program a chess bot, the environment is a chessboard and creating a room cl...

### 8 Important Evaluation Metrics for Classification Models

This post explains important evaluation metrics to check while measuring the performance of a classification model. These are Accuracy, Precision, Recall, Sensitivity, Specificity, False Positive Rate, False Negative Rate, and F1 Score. We’ll cover one by one each metric by calculating its value u...

### Understand Confusion Matrix Using Real-life Classification Example

First I’d like to explain what is the use of a  confusion matrix. Classification Problems are solved using Supervised Machine learning algorithms. In these problems, our goal is to categories an object using its features. For e.g, Identify a fruit using its taste, color and size or check out ...

### Explain Echelon Form of a Matrix

Echelon Form of a matrix is used to solve a linear equation by converting a complex matrix to a simple matrix. A matrix is in an Echelon Form if it satisfies some conditions which we’ll discuss in this post. We must know how to convert a matrix into Echelon Form and simplify our matrix for [&h...

### Working of Bagging and Boosting in Ensemble Learning

Bagging and Boosting both are Ensemble Learning techniques. Ensemble Methods are an important addition to Data Scientists toolbox. Here, weak learners combined together to become strong learners. And offers better performance than an individual one. What is an Ensemble Method The main logic behind t...

### What is the difference between Variance and Bias in Machine Learning?

From the perspective of Supervised Machine Learning, we know all models have errors. We need to minimize the error so as to make the model useful. For this, we need to minimize two major sources of error- Bias and Variance. What is Bias? The tendency of the algorithm to learn wrong details from the ...

### Decision Tree vs Random Forest in Machine Learning

Machine Learning is the sub-branch of Artificial Intelligence. It gives a system the ability to learn and become better from past experiences. Decision tree and random forest are two Supervised Machine Learning techniques. A decision tree is a simple and decision-making diagram. Certainly, for a muc...

### Decision Tree in Machine Learning with Example

Decision Tree algorithm belongs to the Supervised Machine Learning. It can use to solve Regression and Classification problems. It creates a training model which predicts the value of target variables by learning decision rules inferred from training data. What is Decision Tree? It is easy...

### Solving Regression Problems Using Neural Network

This tutorial explains solving regression problems using a neural network approach instead of using Supervised Machine Learning Algorithm. You’ll be able to solve simple regression problems at the end of this tutorial. We’ll use Jupyter Notebook to write the code and TensorFlow as our ma...

### Setup Kaggle Notebook for Ml Experiments

This hands-on guide will teach you to use the Kaggle Platform to start your own machine learning experiments. Kaggle.com is an online community of data scientists and machine learning practitioners and You can learn lots from other publically shared notebooks. We, at aitude.com, highly recommend thi...
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