Start learning real time projects from experts

Study any topic, anytime. explore hundreds of real time projects for the lowest price ever!

1.5k+

Happy

Students

3+

Experienced

Instructors

Top courses

These are the most popular courses among listen courses learners worldwide

Intermediate

PAN CARD Tampering Detection

5

(88 Reviews)

Compare

In this project, we will detect tampering of PAN card using computer vision. This project will help the different organizations in detecting wether the ID, the PANcard provided to them by their employees or customers or anyone is original or not.

Rp 1299

Rp 4999

00:25:20 Hours

Last updated Sun, 16-Jul-2023

PAN CARD Tampering Detection
6 Lessons 00:25:20 Hours English
In this project, we will detect tampering of PAN card using computer vision. This project will help the different organizations in detecting wether the ID, the PANcard provided to them by their employees or customers or anyone is original or not.
Outcomes:
  • Learn to create Machine Learning Algorithms in Python
Remove from cart Add to cart

Intermediate

DOG Breed Prediction

4

(87 Reviews)

Compare

In this project, we will see how to use keras and tensorflow to build, train, and test a convolutional neural networks capable of identifying the breed of a dog in a given image. This is a supervisied learning problem, specifically a multiclass classification problem.

Rp 1299

Rp 4999

00:53:35 Hours

Last updated Fri, 28-Apr-2023

DOG Breed Prediction
6 Lessons 00:53:35 Hours English
In this project, we will see how to use keras and tensorflow to build, train, and test a convolutional neural networks capable of identifying the breed of a dog in a given image. This is a supervisied learning problem, specifically a multiclass classification problem.
Outcomes:
  • Understanding the problem statement.
  • Understanding different libraries and their respective uses.
  • In depth exploratory data analysis of each feature.
  • Data cleansing and preparation.
  • Creating custom functions for machine learning models.
  • In depth explanation of data imputation and filling missing data.
  • Defining an approach to solve ML classification problems.
  • Training and testing the model using cross validation.
Remove from cart Add to cart

Intermediate

Image Watermarking

4

(86 Reviews)

Compare

In this project, we will see how we can add a watermark to an image. Adding a watermark works as copyright for your image so that no one else can illegally use your image or document. We will use an open CV for this project to add a logo as well as text as a watermark on different images.

Rp 1299

Rp 4999

00:13:30 Hours

Last updated Fri, 28-Apr-2023

Image Watermarking
5 Lessons 00:13:30 Hours English
In this project, we will see how we can add a watermark to an image. Adding a watermark works as copyright for your image so that no one else can illegally use your image or document. We will use an open CV for this project to add a logo as well as text as a watermark on different images.
Outcomes:
  • Understanding the problem statement.
  • Understanding different libraries and their respective uses.
  • In depth exploratory data analysis of each feature. Data cleansing and preparation.
  • Creating custom functions for machine learning models.
  • In depth explanation of data imputation and filling missing data.
  • Defining an approach to solve ML classification problems.
  • Data preperation for Machine Learning model.
  • Training and testing the model using cross validation.
Remove from cart Add to cart

Intermediate

Traffic Sign Classification

4

(38 Reviews)

Compare

In this project, we will use a convolution neural network to build, train and test a sign classification model. We will build this model using tensor flow and keras. It is a multiclass classification problem. This model can be used for smarter cars. After this project, you'll be able to create a multiclass classification model using Deep Learning.

Rp 1299

Rp 4999

00:28:15 Hours

Last updated Fri, 28-Apr-2023

Traffic Sign Classification
6 Lessons 00:28:15 Hours English
In this project, we will use a convolution neural network to build, train and test a sign classification model. We will build this model using tensor flow and keras. It is a multiclass classification problem. This model can be used for smarter cars. After this project, you'll be able to create a multiclass classification model using Deep Learning.
Outcomes:
  • Understanding the problem statement.
  • Understanding different libraries and their respective uses.
  • In depth exploratory data analysis of each feature. Data cleansing and preparation.
  • Creating custom functions for models.
  • In depth explanation of data imputation and filling missing data.
  • Defining an approach to solve deep learning classification problems.
  • Training and testing the model using cross validation
Remove from cart Add to cart

Intermediate

Covid-19 Analysis

5

(82 Reviews)

Compare

In this project we will analyze the COVID-19 cases using Data Science.

Rp 1299

Rp 4999

00:28:10 Hours

Last updated Fri, 28-Apr-2023

Covid-19 Analysis
5 Lessons 00:28:10 Hours English
In this project we will analyze the COVID-19 cases using Data Science.
Outcomes:
  • Understanding the problem statement.
  • Understanding different libraries and their respective uses.
  • In depth exploratory data analysis of each feature. Data cleansing and preparation.
  • Creating custom functions for machine learning models.
  • In depth explanation of data imputation and filling missing data.
Remove from cart Add to cart

Intermediate

Plant Disease Prediction

5

(35 Reviews)

Compare

In this project, we will create a convolution neural network that will be able to predict whether a plant is suffering from a disease. We will use different layers and other hyperparameters for building, training, and testing this classification model. we will be using TensorFlow and Keras for this project.

Rp 1299

Rp 4999

00:10:20 Hours

Last updated Fri, 28-Apr-2023

Plant Disease Prediction
5 Lessons 00:10:20 Hours English
In this project, we will create a convolution neural network that will be able to predict whether a plant is suffering from a disease. We will use different layers and other hyperparameters for building, training, and testing this classification model. we will be using TensorFlow and Keras for this project.
Outcomes:
  • Understanding the problem statement.
  • Understanding different libraries and their respective uses.
  • In depth exploratory data analysis of each feature. Data cleansing and preparation.
  • Creating custom functions for Deep learning models.
  • In depth explanation of data imputation and filling missing data.
  • Data preperation for Deep Learning model.
  • Training and testing the model using cross validation
Remove from cart Add to cart

Intermediate

Counting & Detecting Vehicles

4

(1 Reviews)

Compare

In this project, we will be working on detecting and counting the number of vehicles in a given image or a video. We will be using open CV for image processing and a new thing, HAAR cascade, which is used for object detection. We can also create our own customized HAAR cascade classifier. You can create your own casket classifier.

Rp 1299

Rp 4999

00:14:40 Hours

Last updated Fri, 28-Apr-2023

Counting & Detecting Vehicles
4 Lessons 00:14:40 Hours English
In this project, we will be working on detecting and counting the number of vehicles in a given image or a video. We will be using open CV for image processing and a new thing, HAAR cascade, which is used for object detection. We can also create our own customized HAAR cascade classifier. You can create your own casket classifier.
Outcomes:
  • Understanding the problem statement.
  • Understanding different libraries and their respective uses.
  • In depth exploratory data analysis of each feature. Data cleansing and preparation.
  • Creating custom functions for machine learning models.
  • In depth explanation of data imputation and filling missing data.
  • Defining an approach to solve ML classification problems.
  • Data preperation for Machine Learning model.
  • Traning and testing the model using cross validation
Remove from cart Add to cart

Intermediate

Face Swapping Using Deep Learning

4

(34 Reviews)

Compare

In this project, we will be using open CV and DLIB to extract the faces of human beings from a given image. We will use a pre-trained model to extract landmarks from the faces. We will take a source image and a destination image and replace the face of the destination image with a source image.

Rp 1299

Rp 4999

00:20:55 Hours

Last updated Fri, 28-Apr-2023

Face Swapping Using Deep Learning
5 Lessons 00:20:55 Hours English
In this project, we will be using open CV and DLIB to extract the faces of human beings from a given image. We will use a pre-trained model to extract landmarks from the faces. We will take a source image and a destination image and replace the face of the destination image with a source image.
Outcomes:
  • Understanding the problem statement.
  • Understanding different libraries and their respective uses.
  • In depth exploratory data analysis of each feature. Data cleansing and preparation.
  • Creating custom functions for machine learning models.
  • In depth explanation of data imputation and filling missing data.
  • Defining an approach to solve ML classification problems.
  • Data preperation for Machine Learning model.
  • Training and testing the model using cross validation.
Remove from cart Add to cart

Intermediate

Toxic Comment Classifier using NLP

4

(34 Reviews)

Compare

In this project we will be creating a classifier model that can predict if input text is inappropriate or not. Exploring the effectiveness of multiple machine learning approaches and select the best model and tune the parameters to maximize performance.

Rp 1299

Rp 4999

00:39:54 Hours

Last updated Fri, 28-Apr-2023

Toxic Comment Classifier using NLP
7 Lessons 00:39:54 Hours English
In this project we will be creating a classifier model that can predict if input text is inappropriate or not. Exploring the effectiveness of multiple machine learning approaches and select the best model and tune the parameters to maximize performance.
Outcomes:
  • Understanding the problem statement.
  • Understanding different libraries and their respective uses.
  • In depth exploratory data analysis of each feature. Data cleansing and preparation.
  • Creating custom functions for machine learning models.
  • Defining an approach to solve ML classification problems.
  • Data preperation for Machine Learning model.
  • Training and testing the model using cross validation.
Remove from cart Add to cart

Intermediate

Medical Insurance Premium

4

(1 Reviews)

Compare

In this project, we will predict the medical insurance premium for a person using a few independent variables.

Rp 1299

Rp 4999

00:32:10 Hours

Last updated Fri, 28-Apr-2023

Medical Insurance Premium
6 Lessons 00:32:10 Hours English
In this project, we will predict the medical insurance premium for a person using a few independent variables.
Outcomes:
  • Understanding the business problem.
  • Import Libraries and Datasets
  • Perform Data exploration
  • Dealing with missing values
  • Encoding categorical data
  • Understanding feature scaling importance and applying them if required.
  • Building model
Remove from cart Add to cart

Intermediate

Core Python

5

(2 Reviews)

Compare

Short Description

Rp 1299

Rp 5000

08:38:20 Hours

Last updated Sun, 21-May-2023

Core Python
157 Lessons 08:38:20 Hours English
Short Description
Outcomes:
Remove from cart Add to cart

Beginner

Drip Content Course

0

(0 Reviews)

Compare

Drip Content Course

Rp 4000

01:16:21 Hours

Last updated Wed, 28-Feb-2024

Drip Content Course
6 Lessons 01:16:21 Hours English
Drip Content Course
Outcomes:
  • Outcomes 1
  • Outcomes 2
Remove from cart Add to cart

Top categories

These are the most popular courses among listen courses learners worldwide

Machine Learning Projects

25 Courses
Deep Learning Projects

6 Courses
NLP Projects

6 Courses
Website Projects

5 Courses
DJango Web Projects

4 Courses
Core Python

3 Courses
Computer Vision Projects

3 Courses
IOT Projects

2 Courses
Data Science Projects

1 Courses

Top 10 Latest courses

These are the most latest courses among listen courses learners worldwide

Beginner

Drip Content Course

0

(0 Reviews)

Compare

Drip Content Course

Rp 4000

01:16:21 Hours

Last updated Wed, 28-Feb-2024

Drip Content Course
6 Lessons 01:16:21 Hours English
Drip Content Course
Outcomes:
  • Outcomes 1
  • Outcomes 2
Remove from cart Add to cart

Intermediate

Book Genre Prediction Using NLP

0

(0 Reviews)

Compare

Book Genre Prediction Using NLP is a field of natural language processing that aims to predict the genre of a book based on its textual content

Rp 1299

Rp 4999

01:37:37 Hours

Last updated Fri, 28-Apr-2023

Book Genre Prediction Using NLP
9 Lessons 01:37:37 Hours English
Book Genre Prediction Using NLP is a field of natural language processing that aims to predict the genre of a book based on its textual content
Outcomes:
  • NLP algorithms can accurately predict the genre of a book, making it easier to categorize books for libraries, bookstores, and online marketplaces.
  • By accurately predicting the genre of a book, NLP algorithms can assist in recommending books to readers based on their interests.
  • NLP can be used to automatically tag books with relevant genres and keywords, making it easier to search and discover books.
  • Accurately predicting the genre of a book can help publishers and marketers to develop more effective marketing strategies.
  • By providing accurate and relevant genre classifications, NLP algorithms can improve the overall user experience of readers, making it easier for them to find books that match their interests.
Remove from cart Add to cart

Intermediate

petrol price prediction using Machine Learning

0

(0 Reviews)

Compare

Petrol price prediction using Machine Learning involves training a model to predict the future prices of petrol based on historical data and other relevant variables such as global crude oil prices, supply and demand, economic indicators, and geopolitical events.

Rp 1299

Rp 4999

00:32:46 Hours

Last updated Tue, 05-Sep-2023

petrol price prediction using Machine Learning
7 Lessons 00:32:46 Hours English
Petrol price prediction using Machine Learning involves training a model to predict the future prices of petrol based on historical data and other relevant variables such as global crude oil prices, supply and demand, economic indicators, and geopolitical events.
Outcomes:
  • By training machine learning models on large datasets of historical petrol prices and relevant variables such as crude oil prices, exchange rates, and supply and demand factors
  • Machine learning models can be used to make real-time predictions of petrol prices based on current data, such as weather conditions or political events that may affect petrol prices.
  • Machine learning models can be used to identify trends in petrol prices over time, such as seasonal variations or long-term trends related to global energy markets.
  • Machine learning models can be used to analyze individual consumer behavior and make personalized recommendations for when and where to purchase petrol based on factors such as past buying habits, location, and price sensitivity.
Remove from cart Add to cart

Intermediate

Used car price prediction using Machine Learning

0

(0 Reviews)

Compare

Used car price prediction using machine learning involves building a predictive model that can estimate the price of a used car based on various features such as the car's age, mileage, model, make, and condition.

Rp 1299

Rp 4999

00:25:03 Hours

Last updated Fri, 28-Apr-2023

Used car price prediction using Machine Learning
5 Lessons 00:25:03 Hours English
Used car price prediction using machine learning involves building a predictive model that can estimate the price of a used car based on various features such as the car's age, mileage, model, make, and condition.
Outcomes:
  • Machine learning models can analyze large amounts of data and identify patterns that human analysts may miss. This can lead to more accurate predictions of used car prices, helping dealerships, buyers, and sellers to make better pricing decisions.
  • the use of machine learning, the process of analyzing used car prices can be automated, saving time and resources. Dealerships, buyers, and sellers can quickly obtain accurate pricing information without the need for extensive manual research.
  • Machine learning models can provide detailed insights into how prices are calculated, which can increase transparency in the used car market. This can help build trust between buyers and sellers and make the market more efficient.
  • y accurately predicting used car prices, dealerships can provide a better customer experience by offering fair prices and avoiding the need for lengthy negotiations.
Remove from cart Add to cart

Intermediate

Flight Fare Detection Using Auto SK Learn

0

(0 Reviews)

Compare

Flight fare detection using Auto SK Learn is a machine learning technique that automates the process of developing predictive models for flight ticket prices.

Rp 1299

Rp 4999

00:44:59 Hours

Last updated Fri, 28-Apr-2023

Flight Fare Detection Using Auto SK Learn
9 Lessons 00:44:59 Hours English
Flight fare detection using Auto SK Learn is a machine learning technique that automates the process of developing predictive models for flight ticket prices.
Outcomes:
  • AutoSKLearn uses advanced machine learning algorithms and techniques to build highly accurate predictive models for flight ticket prices.
  • Accurate predictions of flight fares can help airlines and travel agencies optimize their revenue management strategies.
  • AutoSKLearn automates the process of model selection and hyperparameter tuning, reducing the time and effort required to develop a predictive model. This can save airline companies and travel agencies significant time and cost in the long run.
  • AutoSKLearn can be used to build predictive models for large datasets, making it scalable for airlines and travel agencies with large amounts of data.
Remove from cart Add to cart

Intermediate

Video Game Sales Prediction using Machine Learning

0

(0 Reviews)

Compare

Video game sales prediction using machine learning is the process of using algorithms and statistical models to forecast the sales of video games.

Rp 1299

Rp 4999

01:04:50 Hours

Last updated Fri, 28-Apr-2023

Video Game Sales Prediction using Machine Learning
6 Lessons 01:04:50 Hours English
Video game sales prediction using machine learning is the process of using algorithms and statistical models to forecast the sales of video games.
Outcomes:
  • One of the primary outcomes of using machine learning for video game sales prediction is the ability to make accurate sales forecasts. By analyzing historical data and identifying patterns and trends, machine learning models can predict sales for upcoming games with a high degree of accuracy.
  • Machine learning models can also help identify which marketing strategies are most effective for different types of video games. By analyzing historical data, machine learning algorithms can identify which marketing channels, messages, and tactics are most likely to drive sales for different types of games.
  • Video game sales prediction using machine learning can also lead to enhanced revenue for game developers and publishers. By accurately predicting sales for upcoming games, developers can better plan production schedules and allocate resources to maximize revenue.
  • Using machine learning for video game sales prediction can also help reduce the risk associated with developing and publishing new games. By accurately predicting sales, game developers and publishers can make more informed decisions about which games to invest in and which ones to pass on.
Remove from cart Add to cart

Intermediate

Human Activity Recognition with Smartphone using machine learning

0

(0 Reviews)

Compare

Human Activity Recognition with Smartphone using machine learning refers to the process of identifying and classifying different human activities based on the data collected from sensors in a smartphone.

Rp 1299

Rp 4999

00:40:25 Hours

Last updated Fri, 28-Apr-2023

Human Activity Recognition with Smartphone using machine learning
9 Lessons 00:40:25 Hours English
Human Activity Recognition with Smartphone using machine learning refers to the process of identifying and classifying different human activities based on the data collected from sensors in a smartphone.
Outcomes:
  • By tracking and analyzing human activity patterns, healthcare professionals can better monitor patients' physical activity levels and identify potential health issues.
  • Smartphones equipped with machine learning algorithms can help fitness enthusiasts track their activities and progress towards fitness goals.
  • Machine learning algorithms can be used to detect suspicious behavior patterns and alert security personnel to potential threats.
  • Human Activity Recognition with Smartphone using machine learning can provide personalized recommendations to users based on their activity patterns, such as suggesting activities to increase physical activity or reduce sedentary behavior.
  • Machine learning algorithms can adapt to users' activity patterns and preferences, resulting in a more personalized and intuitive user experience.
Remove from cart Add to cart

Intermediate

e-Signing of Customers based on financial data using machine Learning

0

(0 Reviews)

Compare

e-Signing of customers based on financial data using machine learning is a process where machine learning algorithms are used to analyze financial data of customers to determine their creditworthiness and eligibility for loans or other financial products.

Rp 4999

00:47:16 Hours

Last updated Fri, 28-Apr-2023

e-Signing of Customers based on financial data using machine Learning
7 Lessons 00:47:16 Hours English
e-Signing of customers based on financial data using machine learning is a process where machine learning algorithms are used to analyze financial data of customers to determine their creditworthiness and eligibility for loans or other financial products.
Outcomes:
  • E-signing reduces the time and effort required for customers to sign financial documents, as they can sign electronically from anywhere, at any time. This can lead to increased efficiency and faster processing times for financial institutions.
  • E-signatures based on machine learning algorithms can be more secure than traditional paper-based signatures, as they can be verified using biometric data such as facial recognition or fingerprint scanning. This can reduce the risk of fraud and identity theft.
  • Machine learning algorithms can analyze financial data to identify patterns and predict customer behavior, which can help financial institutions make more accurate risk assessments. This can lead to better loan decisions and more profitable lending practices.
  • E-signatures can help financial institutions comply with regulations such as the Electronic Signatures in Global and National Commerce Act (ESIGN) and the Uniform Electronic Transactions Act (UETA), which recognize electronic signatures as legally binding in the United States.
Remove from cart Add to cart

Intermediate

Sentiment Analysis Using Logistic Regression

0

(0 Reviews)

Compare

Sentiment analysis is a technique used to determine the emotional tone of a piece of text, whether it's positive, negative, or neutral.

Rp 1299

Rp 4999

00:44:57 Hours

Last updated Fri, 28-Apr-2023

Sentiment Analysis Using Logistic Regression
5 Lessons 00:44:57 Hours English
Sentiment analysis is a technique used to determine the emotional tone of a piece of text, whether it's positive, negative, or neutral.
Outcomes:
  • By analyzing the sentiment of large volumes of customer feedback
  • Sentiment analysis can help businesses identify customer issues and complaints quickly
  • Sentiment analysis can help businesses identify the features and aspects of their products or services that customers like or dislike.
Remove from cart Add to cart

Intermediate

Transaction Application With Tkinter and Sqlite web App

0

(0 Reviews)

Compare

A transaction application developed using Tkinter and SQLite is a desktop application that allows users to perform various financial transactions like depositing money, withdrawing money, transferring money, and viewing account balance.

Rp 1299

Rp 4999

02:10:25 Hours

Last updated Fri, 28-Apr-2023

Transaction Application With Tkinter and Sqlite web App
9 Lessons 02:10:25 Hours English
A transaction application developed using Tkinter and SQLite is a desktop application that allows users to perform various financial transactions like depositing money, withdrawing money, transferring money, and viewing account balance.
Outcomes:
  • The transaction application provides users with a convenient way to manage their financial transactions. Users can easily track their account balances, deposits, withdrawals, and transfers, ensuring they have a clear overview of their financial situation.
  • The application automates the financial transaction process, reducing the time required to manually process transactions. This results in increased efficiency and productivity.
  • The SQLite database used in the transaction application ensures data security, with encryption capabilities that protect users' sensitive financial information. The web app can also be secured using SSL encryption and other security measures to protect against data breaches and unauthorized access.
  • The web app version of the transaction application is scalable, allowing for the addition of new features and functionalities as the business or individual's financial needs grow.
Remove from cart Add to cart

Top instructors

They efficiently serve large number of students on our platform

Project Bank

Join now to start learning

Learn from our quality instructors!

Get started

Become a new instructor

Teach thousands of students and earn money!

Join now

Study any Project, anytime. explore hundreds of Projects and courses for the lowest price ever!

Top categories

Useful links

Help

Subscribe to our newsletter