Recognizing HandWritten Digits in Scikit Learn
Last Updated :
19 Sep, 2024
Scikit learn is one of the most widely used machine learning libraries in the machine learning community the reason behind that is the ease of code and availability of approximately all functionalities which a machine learning developer will need to build a machine learning model. In this article, we will learn how can we use sklearn to train an MLP model on the handwritten digits dataset. Some of the other benefits are:
- It provides classification, regression, and clustering algorithms such as the SVM algorithm, random forests, gradient boosting, and k-means.
- It is also designed to operate with Python's scientific and numerical libraries NumPy and SciPy. Scikit-learn is a NumFOCUS project that has financial support.
Importing Libraries and Dataset
Let us begin by importing the model's required libraries and loading the dataset digits.
Python
# importing the hand written digit dataset
from sklearn import datasets
# digit contain the dataset
digits = datasets.load_digits()
# dir function use to display the attributes of the dataset
dir(digits)
Output:
['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']
Function to print a set of image
- digits.image is a three-dimensional array. The first dimension indexes images, and we can see that there are 1797 in total.
- The following two dimensions relate to each image's pixels' x and y coordinates.
- Each image is 8x8 = 64 pixels in size. In other terms, this array may be represented in 3D as a stack of 8x8 pixel images.
Python
# outputting the picture value as a series of numbers
print(digits.images[0])
Output:
[[ 0. 0. 5. 13. 9. 1. 0. 0.]
[ 0. 0. 13. 15. 10. 15. 5. 0.]
[ 0. 3. 15. 2. 0. 11. 8. 0.]
[ 0. 4. 12. 0. 0. 8. 8. 0.]
[ 0. 5. 8. 0. 0. 9. 8. 0.]
[ 0. 4. 11. 0. 1. 12. 7. 0.]
[ 0. 2. 14. 5. 10. 12. 0. 0.]
[ 0. 0. 6. 13. 10. 0. 0. 0.]]
The original digits had much higher resolution, and the resolution was reduced when preparing the dataset for scikit-learn to allow training a machine learning system to recognize these digits easier and faster. Because at such a low resolution, even a human would struggle to recognize some of the digits The low quality of the input photos will also limit our neural network in these settings. Is the neural network capable of doing at least as well as an individual? It would already be an accomplishment!
Python
# importing the matplotlib libraries pyplot function
import matplotlib.pyplot as plt
# defining the function plot_multi
def plot_multi(i):
nplots = 16
fig = plt.figure(figsize=(15, 15))
for j in range(nplots):
plt.subplot(4, 4, j+1)
plt.imshow(digits.images[i+j], cmap='binary')
plt.title(digits.target[i+j])
plt.axis('off')
# printing the each digits in the dataset.
plt.show()
plot_multi(0)
Output:
A batch of 16 Hand Written DigitsTraining Neural network with the dataset
A neural network is a set of algorithms that attempts to recognize underlying relationships in a batch of data using a technique similar to how the human brain works. In this context, neural networks are systems of neurons that might be organic or artificial in nature.
- an input layer consisting of 64 nodes, one for each pixel in the input pictures They simply send their input value to the neurons of the following layer.
- This is a dense neural network, meaning each node in each layer is linked to all nodes in the preceding and following levels.
The input layer expects a one-dimensional array, whereas the image datasets are two-dimensional. As a result, flattening all images process takes place:
Python
# converting the 2 dimensional array to one dimensional array
y = digits.target
x = digits.images.reshape((len(digits.images), -1))
# gives the shape of the data
x.shape
Output:
(1797, 64)
The 8x8 images' two dimensions have been merged into a single dimension by composing the rows of 8 pixels one after the other. The first image, which we discussed before, is now represented as a 1-D array having 8x8 = 64 slots.
Python
# printing the one-dimensional array's values
x[0]
Output:
array([ 0., 0., 5., 13., 9., 1., 0., 0., 0., 0., 13., 15., 10.,
15., 5., 0., 0., 3., 15., 2., 0., 11., 8., 0., 0., 4.,
12., 0., 0., 8., 8., 0., 0., 5., 8., 0., 0., 9., 8.,
0., 0., 4., 11., 0., 1., 12., 7., 0., 0., 2., 14., 5.,
10., 12., 0., 0., 0., 0., 6., 13., 10., 0., 0., 0.])
Data split for training and testing.
When machine learning algorithms are used to make predictions based on data that was not used to train the model, the train-test split process is used to measure their performance.
It is a quick and simple technique that allows you to compare the performance of algorithms for machine learning for your predictive modeling challenge.
Python
# Very first 1000 photographs and
# labels will be used in training.
x_train = x[:1000]
y_train = y[:1000]
# The leftover dataset will be utilised to
# test the network's performance later on.
x_test = x[1000:]
y_test = y[1000:]
Usage of Multi-Layer Perceptron classifier
MLP stands for multi-layer perceptron. It consists of densely connected layers that translate any input dimension to the required dimension. A multi-layer perception is a neural network with multiple layers. To build a neural network, we connect neurons so that their outputs become the inputs of other neurons.
Python
# importing the MLP classifier from sklearn
from sklearn.neural_network import MLPClassifier
# calling the MLP classifier with specific parameters
mlp = MLPClassifier(hidden_layer_sizes=(15,),
activation='logistic',
alpha=1e-4, solver='sgd',
tol=1e-4, random_state=1,
learning_rate_init=.1,
verbose=True)
Now is the time to train our MLP model on the training data.
Python
mlp.fit(x_train, y_train)
Output:
Iteration 1, loss = 2.22958289
Iteration 2, loss = 1.91207743
Iteration 3, loss = 1.62507727
Iteration 4, loss = 1.32649842
Iteration 5, loss = 1.06100535
Iteration 6, loss = 0.83995513
Iteration 7, loss = 0.67806075
Iteration 8, loss = 0.55175832
Iteration 9, loss = 0.45840445
Iteration 10, loss = 0.39149735
Iteration 11, loss = 0.33676351
Iteration 12, loss = 0.29059880
Iteration 13, loss = 0.25437208
Iteration 14, loss = 0.22838372
Iteration 15, loss = 0.20200554
Iteration 16, loss = 0.18186565
Iteration 17, loss = 0.16461183
Iteration 18, loss = 0.14990228
Iteration 19, loss = 0.13892154
Iteration 20, loss = 0.12833784
Iteration 21, loss = 0.12138920
Iteration 22, loss = 0.11407971
Iteration 23, loss = 0.10677664
Iteration 24, loss = 0.10037149
Iteration 25, loss = 0.09593187
Iteration 26, loss = 0.09250135
Iteration 27, loss = 0.08676698
Iteration 28, loss = 0.08356043
Iteration 29, loss = 0.08209789
Iteration 30, loss = 0.07649168
Iteration 31, loss = 0.07410898
Iteration 32, loss = 0.07126869
Iteration 33, loss = 0.06926956
Iteration 34, loss = 0.06578496
Iteration 35, loss = 0.06374913
Iteration 36, loss = 0.06175492
Iteration 37, loss = 0.05975664
Iteration 38, loss = 0.05764485
Iteration 39, loss = 0.05623663
Iteration 40, loss = 0.05420966
Iteration 41, loss = 0.05413911
Iteration 42, loss = 0.05256140
Iteration 43, loss = 0.05020265
Iteration 44, loss = 0.04902779
Iteration 45, loss = 0.04788382
Iteration 46, loss = 0.04655532
Iteration 47, loss = 0.04586089
Iteration 48, loss = 0.04451758
Iteration 49, loss = 0.04341598
Iteration 50, loss = 0.04238096
Iteration 51, loss = 0.04162200
Iteration 52, loss = 0.04076839
Iteration 53, loss = 0.04003180
Iteration 54, loss = 0.03907774
Iteration 55, loss = 0.03815565
Iteration 56, loss = 0.03791975
Iteration 57, loss = 0.03706276
Iteration 58, loss = 0.03617874
Iteration 59, loss = 0.03593227
Iteration 60, loss = 0.03504175
Iteration 61, loss = 0.03441259
Iteration 62, loss = 0.03397449
Iteration 63, loss = 0.03326990
Iteration 64, loss = 0.03305025
Iteration 65, loss = 0.03244893
Iteration 66, loss = 0.03191504
Iteration 67, loss = 0.03132169
Iteration 68, loss = 0.03079707
Iteration 69, loss = 0.03044946
Iteration 70, loss = 0.03005546
Iteration 71, loss = 0.02960555
Iteration 72, loss = 0.02912799
Iteration 73, loss = 0.02859103
Iteration 74, loss = 0.02825959
Iteration 75, loss = 0.02788968
Iteration 76, loss = 0.02748725
Iteration 77, loss = 0.02721247
Iteration 78, loss = 0.02686225
Iteration 79, loss = 0.02635636
Iteration 80, loss = 0.02607439
Iteration 81, loss = 0.02577613
Iteration 82, loss = 0.02553642
Iteration 83, loss = 0.02518749
Iteration 84, loss = 0.02484300
Iteration 85, loss = 0.02455379
Iteration 86, loss = 0.02432480
Iteration 87, loss = 0.02398548
Iteration 88, loss = 0.02376004
Iteration 89, loss = 0.02341261
Iteration 90, loss = 0.02318255
Iteration 91, loss = 0.02296065
Iteration 92, loss = 0.02274048
Iteration 93, loss = 0.02241054
Iteration 94, loss = 0.02208181
Iteration 95, loss = 0.02190861
Iteration 96, loss = 0.02174404
Iteration 97, loss = 0.02156939
Iteration 98, loss = 0.02119768
Iteration 99, loss = 0.02101874
Iteration 100, loss = 0.02078230
Iteration 101, loss = 0.02061573
Iteration 102, loss = 0.02039802
Iteration 103, loss = 0.02017245
Iteration 104, loss = 0.01997162
Iteration 105, loss = 0.01989280
Iteration 106, loss = 0.01963828
Iteration 107, loss = 0.01941850
Iteration 108, loss = 0.01933154
Iteration 109, loss = 0.01911473
Iteration 110, loss = 0.01905371
Iteration 111, loss = 0.01876085
Iteration 112, loss = 0.01860656
Iteration 113, loss = 0.01848655
Iteration 114, loss = 0.01834844
Iteration 115, loss = 0.01818981
Iteration 116, loss = 0.01798523
Iteration 117, loss = 0.01783630
Iteration 118, loss = 0.01771441
Iteration 119, loss = 0.01749814
Iteration 120, loss = 0.01738339
Iteration 121, loss = 0.01726549
Iteration 122, loss = 0.01709638
Iteration 123, loss = 0.01698340
Iteration 124, loss = 0.01684606
Iteration 125, loss = 0.01667016
Iteration 126, loss = 0.01654172
Iteration 127, loss = 0.01641832
Iteration 128, loss = 0.01630111
Iteration 129, loss = 0.01623051
Iteration 130, loss = 0.01612736
Iteration 131, loss = 0.01590220
Iteration 132, loss = 0.01582485
Iteration 133, loss = 0.01571372
Iteration 134, loss = 0.01560349
Iteration 135, loss = 0.01557688
Iteration 136, loss = 0.01534420
Iteration 137, loss = 0.01527883
Iteration 138, loss = 0.01517545
Iteration 139, loss = 0.01503663
Iteration 140, loss = 0.01501192
Iteration 141, loss = 0.01482535
Iteration 142, loss = 0.01471388
Iteration 143, loss = 0.01463948
Iteration 144, loss = 0.01454059
Iteration 145, loss = 0.01441742
Iteration 146, loss = 0.01431741
Iteration 147, loss = 0.01428414
Iteration 148, loss = 0.01416364
Iteration 149, loss = 0.01406742
Iteration 150, loss = 0.01402651
Iteration 151, loss = 0.01389720
Iteration 152, loss = 0.01381412
Iteration 153, loss = 0.01371300
Iteration 154, loss = 0.01362465
Iteration 155, loss = 0.01357048
Iteration 156, loss = 0.01348760
Iteration 157, loss = 0.01339543
Iteration 158, loss = 0.01331941
Iteration 159, loss = 0.01320812
Iteration 160, loss = 0.01315415
Iteration 161, loss = 0.01308279
Iteration 162, loss = 0.01302708
Iteration 163, loss = 0.01290042
Iteration 164, loss = 0.01289267
Iteration 165, loss = 0.01277558
Iteration 166, loss = 0.01277238
Iteration 167, loss = 0.01261308
Iteration 168, loss = 0.01260611
Iteration 169, loss = 0.01248789
Iteration 170, loss = 0.01239662
Iteration 171, loss = 0.01231743
Iteration 172, loss = 0.01227346
Iteration 173, loss = 0.01223136
Iteration 174, loss = 0.01217211
Iteration 175, loss = 0.01208682
Iteration 176, loss = 0.01204707
Iteration 177, loss = 0.01200225
Iteration 178, loss = 0.01188677
Iteration 179, loss = 0.01184993
Iteration 180, loss = 0.01175130
Iteration 181, loss = 0.01171178
Iteration 182, loss = 0.01166052
Iteration 183, loss = 0.01163843
Iteration 184, loss = 0.01154892
Iteration 185, loss = 0.01147629
Iteration 186, loss = 0.01142365
Iteration 187, loss = 0.01136608
Iteration 188, loss = 0.01128053
Iteration 189, loss = 0.01128869
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
MLPClassifier
MLPClassifier(activation='logistic', hidden_layer_sizes=(15,),
learning_rate_init=0.1, random_state=1, solver='sgd',
verbose=True)
Above shown is the loss for the last five epochs of the MLPClassifier and its respective configuration.
Python
fig, axes = plt.subplots(1, 1)
axes.plot(mlp.loss_curve_, 'o-')
axes.set_xlabel("number of iteration")
axes.set_ylabel("loss")
plt.show()
Output:
Model Evaluation
Now let's check the performance of the model using the recognition dataset or it just has memorized it. We will do this by using the leftover testing data so, that we can check whether the model has learned the actual pattern in the digit
Python
predictions = mlp.predict(x_test)
predictions[:50]
Output:
array([1, 4, 0, 5, 3, 6, 9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5,
4, 4, 9, 0, 8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 3, 0, 1, 2, 3, 4,
5, 6, 7, 8, 5, 0])
But the true labels or we can say that the ground truth labels were as shown below.
Python
Output:
array([1, 4, 0, 5, 3, 6, 9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5,
4, 4, 9, 0, 8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4,
5, 6, 7, 8, 9, 0])
So, by using the predicted labels and the ground truth labels we can find the accuracy of our model.
Python
# importing the accuracy_score from the sklearn
from sklearn.metrics import accuracy_score
# calculating the accuracy with y_test and predictions
accuracy_score(y_test, predictions)
Output:
0.9146800501882058
Get the complete notebook and dataset link here:
Notebook link : click here.
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Vendors selling everyday items need to keep their stock updated so that customers don’t leave empty-handed. Maintaining the right stock levels helps avoid shortages that disappoint customers and prevents overstocking which can increase costs. In this article we’ll learn how to use Machine Learning (
6 min read
Ola Bike Ride Request Forecast using ML
From telling rickshaw-wala where to go, to tell him where to come we have grown up. Yes, we are talking about online cab and bike facility providers like OLA and Uber. If you had used this app some times then you must have paid some day less and someday more for the same journey. But have you ever t
8 min read
Waiter's Tip Prediction using Machine Learning
If you have recently visited a restaurant for a family dinner or lunch and you have tipped the waiter for his generous behavior then this project might excite you. As in this article, we will try to predict what amount of tip a person will give based on his/her visit to the restaurant using some fea
7 min read
Predict Fuel Efficiency Using Tensorflow in Python
Predicting fuel efficiency is a important task in automotive design and environmental sustainability. In this article we will build a fuel efficiency prediction model using TensorFlow one of the most popular deep learning libraries. We will use the Auto MPG dataset which contains features like engin
5 min read
Microsoft Stock Price Prediction with Machine Learning
In this article, we will implement Microsoft Stock Price Prediction with a Machine Learning technique. We will use TensorFlow, an Open-Source Python Machine Learning Framework developed by Google. TensorFlow makes it easy to implement Time Series forecasting data. Since Stock Price Prediction is one
5 min read
Share Price Forecasting Using Facebook Prophet
Time series forecast can be used in a wide variety of applications such as Budget Forecasting, Stock Market Analysis, etc. But as useful it is also challenging to forecast the correct projections, Thus can't be easily automated because of the underlying assumptions and factors. The analysts who prod
6 min read
Implementation of Movie Recommender System - Python
Recommender Systems provide personalized suggestions for items that are most relevant to each user by predicting preferences according to user's past choices. They are used in various areas like movies, music, news, search queries, etc. These recommendations are made in two ways: Collaborative filte
4 min read
How can Tensorflow be used with abalone dataset to build a sequential model?
In this article, we will learn how to build a sequential model using TensorFlow in Python to predict the age of an abalone. We may wonder what is an abalone. Answer to this question is that it is a kind of snail. Generally, the age of an Abalone is determined by the physical examination of the abalo
7 min read
Computer Vision Projects
OCR of Handwritten digits | OpenCV
OCR which stands for Optical Character Recognition is a computer vision technique used to identify the different types of handwritten digits that are used in common mathematics. To perform OCR in OpenCV we will use the KNN algorithm which detects the nearest k neighbors of a particular data point an
2 min read
Cartooning an Image using OpenCV - Python
Instead of sketching images by hand we can use OpenCV to convert a image into cartoon image. In this tutorial you'll learn how to turn any image into a cartoon. We will apply a series of steps like:Smoothing the image (like a painting)Detecting edges (like a sketch)Combining both to get a cartoon ef
2 min read
Count number of Object using Python-OpenCV
In this article, we will use image processing to count the number of Objects using OpenCV in Python.Google Colab link: https://colab.research.google.com/drive/10lVjcFhdy5LVJxtSoz18WywM92FQAOSV?usp=sharingModule neededOpenCv: OpenCv is an open-source library that is useful for computer vision applica
2 min read
Count number of Faces using Python - OpenCV
Prerequisites: Face detection using dlib and openCV In this article, we will use image processing to detect and count the number of faces. We are not supposed to get all the features of the face. Instead, the objective is to obtain the bounding box through some methods i.e. coordinates of the face i
3 min read
Text Detection and Extraction using OpenCV and OCR
Optical Character Recognition (OCR) is a technology used to extract text from images which is used in applications like document digitization, license plate recognition and automated data entry. In this article, we explore how to detect and extract text from images using OpenCV for image processing
2 min read
FaceMask Detection using TensorFlow in Python
In this article, we’ll discuss our two-phase COVID-19 face mask detector, detailing how our computer vision/deep learning pipeline will be implemented. We’ll use this Python script to train a face mask detector and review the results. Given the trained COVID-19 face mask detector, we’ll proceed to i
9 min read
Dog Breed Classification using Transfer Learning
In this tutorial, we will demonstrate how to build a dog breed classifier using transfer learning. This method allows us to use a pre-trained deep learning model and fine-tune it to classify images of different dog breeds. Why to use Transfer Learning for Dog Breed ClassificationTransfer learning is
9 min read
Flower Recognition Using Convolutional Neural Network
Convolutional Neural Network (CNN) are a type of deep learning model specifically designed for processing structured grid data such as images. In this article we will build a CNN model to classify different types of flowers from a dataset containing images of various flowers like roses, daisies, dan
6 min read
Emojify using Face Recognition with Machine Learning
In this article, we will learn how to implement a modification app that will show an emoji of expression which resembles the expression on your face. This is a fun project based on computer vision in which we use an image classification model in reality to classify different expressions of a person.
7 min read
Cat & Dog Classification using Convolutional Neural Network in Python
Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed for processing images. Unlike traditional neural networks CNNs uses convolutional layers to automatically and efficiently extract features such as edges, textures and patterns from images. This makes them hi
5 min read
Traffic Signs Recognition using CNN and Keras in Python
We always come across incidents of accidents where drivers' Overspeed or lack of vision leads to major accidents. In winter, the risk of road accidents has a 40-50% increase because of the traffic signs' lack of visibility. So here in this article, we will be implementing Traffic Sign recognition us
5 min read
Lung Cancer Detection using Convolutional Neural Network (CNN)
Computer Vision is one of the applications of deep neural networks and one such use case is in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using Convolution Neural Network which can classify normal lung tissues from cancerous tissues.The follo
7 min read
Lung Cancer Detection Using Transfer Learning
Computer Vision is one of the applications of deep neural networks that enables us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells.In this article, we will learn how to build a classifier using the Transfer Learning technique
8 min read
Pneumonia Detection using Deep Learning
In this article, we will discuss solving a medical problem i.e. Pneumonia which is a dangerous disease that may occur in one or both lungs usually caused by viruses, fungi or bacteria. We will detect this lung disease based on the x-rays we have. Chest X-rays dataset is taken from Kaggle which conta
7 min read
Detecting Covid-19 with Chest X-ray
COVID-19 pandemic is one of the biggest challenges for the healthcare system right now. It is a respiratory disease that affects our lungs and can cause lasting damage to the lungs that led to symptoms such as difficulty in breathing and in some cases pneumonia and respiratory failure. In this artic
9 min read
Skin Cancer Detection using TensorFlow
In this article, we will learn how to implement a Skin Cancer Detection model using Tensorflow. We will use a dataset that contains images for the two categories that are malignant or benign. We will use the transfer learning technique to achieve better results in less amount of training. We will us
5 min read
Age Detection using Deep Learning in OpenCV
The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'
5 min read
Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV
In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for diff
4 min read
Detecting COVID-19 From Chest X-Ray Images using CNN
A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T
5 min read
Image Segmentation Using TensorFlow
Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,
7 min read
License Plate Recognition with OpenCV and Tesseract OCR
License Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar
5 min read
Detect and Recognize Car License Plate from a video in real time
Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro
11 min read
Residual Networks (ResNet) - Deep Learning
After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble
9 min read
Natural Language Processing Projects
Twitter Sentiment Analysis using Python
This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
10 min read
Facebook Sentiment Analysis using python
This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w
6 min read
Next Sentence Prediction using BERT
Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows
4 min read
Hate Speech Detection using Deep Learning
There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat
5 min read
Image Caption Generator using Deep Learning on Flickr8K dataset
Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image
12 min read
Movie recommendation based on emotion in Python
Movies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their
4 min read
Speech Recognition in Python using Google Speech API
Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require
2 min read
Voice Assistant using python
Speech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide we’ll create a basic voice assistant using Python.Step 1: Install Required
3 min read
Human Activity Recognition - Using Deep Learning Model
Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque
6 min read
Fine-tuning BERT model for Sentiment Analysis
Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train
6 min read
Sentiment Classification Using BERT
BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec
12 min read
Sentiment Analysis with an Recurrent Neural Networks (RNN)
Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews
5 min read
Building an Autocorrector Using NLP in Python
Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N
4 min read
Python | NLP analysis of Restaurant reviews
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach
7 min read
Restaurant Review Analysis Using NLP and SQLite
Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei
9 min read
Twitter Sentiment Analysis using Python
This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
10 min read
Recommender System Project