With the developments in machine learning and deep learning, projects using Machine Learning take place in all areas of life.

Siddhardhan S, who has his own YouTube channel (https://www.youtube.com/c/Siddhardhan), teaches Machine Learning with Python and also explains his projects using machine learning. He contributes to the world of artificial intelligence by explaining the projects he has done on many subjects, from estimating house and car prices, to detecting fake news, to detecting Parkinson’s and heart disease.

Project 1: SONAR Rock vs Mine Prediction with Python | End To End Python Machine Learning Project

In this video, we are building a system in Python that can predict whether an object is either Rock or Mine with SONAR Data. For this use case, we are using the Logistic Regression Model for our prediction.

Logistic Regression: The linear regression fits a line and is used to predict a continuous value. For example, we estimate the price of a house using linear regression. However, the logistic regression fits an s-shaped function and predicts whether something is true or false. It is mainly used in binary classification problems.

Project 2: Diabetes Prediction using Machine Learning with Python | End To End Python ML Project

In this video, we are building a system that can predict whether a person has diabetes or not with the help of Machine Learning. This project is done in Python. In this project, we use the Support Vector Machine model for the prediction.

SVM (Support Vector Machines): Support vector machines, which are called SVM for short, are widely used especially in classification problems, because of their high accuracy. The goal of the SVM algorithm is to find a hyperplane that best separates the classes, taking into account variables.

Project 3. House Price Prediction using Machine Learning with Python | Machine Learning Project

Now you can build a House price prediction system using Machine Learning with Python. Boston house price prediction. This is an important Machine Learning project & use case.

Machine learning was defined in 1959 by Arthur Samuel. By definition, machine learning is the workspace that gives computers the ability to learn without being programmed openly.

Project 4. Fake News Prediction using Machine Learning with Python | Machine Learning Projects

In this video, we are building a Fake News Prediction System using Machine Learning with Python. We will be using the Logistic Regression model for prediction.

Machine learning is a data analytics technique, getting computers to learn and also act like a human. Machine learning algorithms use computational methods to “learn” by feeding data and information. And it is also known as a field of data analytics to make predictions depends on trends and insights of the data.

Project 5. Loan Status Prediction using Machine Learning with Python | Machine Learning Project

This video is about building a Loan Prediction system using Machine Learning with Python. Machine Learning Project with Python.

Machine learning algorithms use statistical tools to find meaningful connections in large amounts of data.

Project 6. Wine Quality Prediction using Machine Learning with Python | Machine Learning Project

This video is about Wine Quality prediction using Machine Learning with Python. This is one of the important Machine Learning projects.

Linear regression models the relationship between one or more observed features and the target variable. Based on the data we have, we try to derive a function that will lead us to the desired result. This function creates a line where we can search for property values and find predicted target values.

Project 7. Car Price Prediction using Machine Learning with Python | Machine Learning Projects

This video is about Car Price Prediction using Machine Learning with Python. This is a Regression Machine Learning Project. This is one of the important Machine Learning Projects.

Linear regression is a method of finding the best straight line fitting to the given data and also finding the best linear relationship between the independent and dependent variables. We use Least Squares Method to decide which line is the best fit the model.

Project 8. Gold Price Prediction using Machine Learning with Python | Machine Learning Projects

This Video is about building a Gold Price Prediction using Machine Learning with Python. For this Prediction, we have used a Random Forest Regressor.

Random Forest is a popular algorithm that operates by constructing a multitude of decision trees. And it is a more robust and more accurate algorithm than decision trees.

Project 9. Heart Disease Prediction using Machine Learning with Python | Machine Learning Projects

This video is about building a Heart Disease Prediction System using Machine Learning with Python. This is one of the important Machine Learning Projects.

Random forest is an ensemble of many decision trees; many hyperparameters between both models are shared. That means is when you’re using a random forest, you are setting up the same set of rules for all decision trees in that random forest.

Project 10. Credit Card Fraud Detection using Machine Learning in Python | Machine Learning Projects

In this video, we have built a Credit card Fraud Detection system using Machine Learning with Python. For this project, we have used the Logistic Regression model.

In regression problems, we have to use metrics designed for continuous values. Regression error metrics inform us about how much the actual values deviate from the regression line, which we estimate.

Project 11. Medical Insurance Cost Prediction using Machine Learning with Python | ML Projects

In this video, I have explained medical insurance cost prediction using Machine Learning with Python. For this project, I have used the Linear Regression model.

Some of the most common evaluation metrics for regression are:

Mean Absolute Error
Mean Squared Error
Root Mean Square Error

Project 12. Big Mart Sales Prediction using Machine Learning with Python | Machine Learning Projects

This video is about Big Mart Sales Prediction using Machine Learning with Python. In this project, XGBoost Regressor is used for Prediction.

Boosting is a concept, not a machine learning algorithm. Instead, consider it as a technique applied to an existing machine learning algorithm. However, it is most widely applied to the decision tree because it produces the best outcomes there.

Project 13. Customer Segmentation using K-Means Clustering with Python | Machine Learning Projects

This video is about Customer Segmentation using K-Means Clustering. This is an important example of Market Basket Analysis in Machine Learning and Data Science.

The aim of K-Means Clustering is to ensure that the clusters acquired at the end of the segmentation process are maximum and the clusters’ similarities are minimum.

Project 14. Parkinson’s Disease Detection using Machine Learning – Python | Machine Learning Project

This video is about building a Machine Learning System that can detect Parkinson’s Disease with Python. In this use case, the Support Vector Machine model is used for prediction.

SVM stands for support vector machine, it is a supervised machine learning algorithm which can be used for both Regression and Classification. If you have n features in your training data set, SVM tries to plot it in n-dimensional space with the value of each feature being the value of a particular coordinate. SVM uses hyper planes to separate out different classes based on the provided kernel function.

Project 15. Titanic Survival Prediction using Machine Learning in Python | Machine Learning Project

This video is about Titanic Survival Prediction using Machine Learning with Python. This is one of the important and standard Machine Learning Projects. For this project, I have used the Logistic regression model.

SVM determines the hyperplanes that correctly classify classes and then selects the hyperplane that is the farthest from the data.

Project 16. Calories Burnt Prediction using Machine Learning with Python | Machine Learning Projects

This video is about Calories burnt prediction using Machine Learning with Python. For this prediction, I have used the XGBoost Regressor model.

The bias-variance trade-off is the problem of simultaneously minimizing two sources of error that prevent supervised learning algorithms from generalizing beyond their training set. Our goal is to find the optimal middle ground where the errors from both over-fitting and under-fitting are minimal. We want a model that is highly enough to capture the signals in our data, but not too complex that it can’t be applied to new data.

Project 17. Face Recognition Tutorial in Python | OpenCV | End To End Machine Learning Project

This is a Hands-on Video on Building a Face Recognition system with Python using OpenCV Library. In this video, we will be using Python Face Recognition to do a few things. Face Recognition is one of the important applications of Machine Learning & Artificial Intelligence.