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

In this post, we will understand the difference between linear regression and logistic regression.Linear RegressionIt helps predict the variable that is continuous, and is a dependent variable.This is done using a given set of independent variables.It extrapolates a line to find the value of dependent variable.Least square methods are used to estimate the accuracy.The best fit line is found, that helps predict the output.It is generally a continuous value.The relation between the dependent variable and independent variable has to be linear.The independent variables may have collinearity between them.It is considered a machine learning problem, i.e an applied statistics problem.Logistic RegressionIt ... Read More

To shift the Y-axis ticks from left to right, we can perform the following steps −Create a figure using the figure() method.Using the above figure method, create the axis of the plot, using add_subplot(xyz), where x is row, y is column, and z is index.To shift the Y-axis ticks from left to right, use ax.yaxis.tick_right() where ax is axis created using add_subplot(xyz) method.Now plot the line using plot() method, with given x and y points, where x and y points can be created using np.array() method.Set up x and y labels, e.g., X-axis and Y-axis , using xlabel and ylabel ... Read More

TensorFlow Hub is a repository that contains trained machine learning models. These models are ready to be fine-tuned and deployed anywhere. The trained models such as BERT and Faster R-CNN can be reused with a few lines of code. It is an open-repository, which means it can be used and modified by anyone.The tfhub.dev repository contains many pre-trained models. Some of them include text embeddings, image classification models, TF.js/TFLite models and so on.It can be installed using the below code:!pip install --upgrade tensorflow_hubIt can be imported into the working environment as shown in the below code:import tensorflow_hub as hub Read More

A sequential model can be built using Keras Sequential API that is used to work with plain stack of layers. Here every layer has exactly one input tensor and one output tensor.A pre-trained model can be used as the base model on the specific dataset. This saves the time and resources of having to train the model again on the specific dataset.A pre-trained model is a saved network which would be previously trained on a large dataset. This large dataset would be a large-scale image-classification task. A pre-trained model can be used as it is or it can be customized ... Read More

To establish a simple relationship between the observations of a given joint distribution of a variable, we can create the plot for the regression model using Seaborn.To fit the dataset using the regression model, we have to first import the necessary libraries in Python.We will create plots for each regression model, (a) Linear Regression, (b) Polynomial Regression, and (c) Logistic Regression.In this example, we will use the wine quality dataset which can be accessed from here, https://archive.ics.uci.edu/ml/datasets/wine+qualityExampleimport matplotlib.pyplot as plt import seaborn as sns from scipy.stats import pearsonr sns.set(style="dark", color_codes=True) #import the dataset wine_quality = pd.read_csv('winequality-red.csv', delimiter=';') #Plotting ... Read More