Top 20 Machine Learning Projects For Beginners

Machine Learning is a branch of Artificial Intelligence. Machine Learning requires complex mathematical functions to perform computations and learn from large datasets. Examples for areas where  ML is used are, Email Filtering and computer image processing, etc. 

Here are some machine learning datasets you can refer to.

There are 2 types of Machine Learning: Supervised learning and Unsupervised learning. 

In supervised learning, the computer builds the mathematical model of sets of data which has both inputs and outputs. The training is given by examples and machines learn them. They are represented using vectors and matrices. 

In unsupervised learning, the computer learns from the test data and they contain only inputs to the machines. There are other learning such as Reinforced learning, Semi-supervised learning, feature learning, and sparse dictionary learning, anomaly detection, robot learning, and self-learning, etc. 

Top 20 Machine Learning Projects

As a beginner, you might be confused about choosing a project in Machine Learning. To make things easier and help you make a quicker decision, here are the top 20 Machine Learning Projects you could consider and see which one works best for you:

1. Email Filtering

One of the security implications is spam and phishing emails. This needs to be filtered from the genuine incoming emails. Here, the data sets are the emails that are received by the organization. This can be done by including machine learning techniques/ features in the email filtering software. This project may be one of the exciting projects in ML for beginners. 

2. Chatbots

You might have heard of Siri, Cortana, Alexa, and Bixby, etc. These work on Machine Learning and Natural Language Processing techniques. These chatbots learn to have text and speech recognition software and trained to learn with examples. This could be an easy and an interesting project in ML. You have many datasets which are the text that you provide for learning.

3. Image Caption Generator

In this project, you have datasets in the form of a large image base and the images are processed with the help of Machine Learning technique and then the right caption is generated to each image based on the study of the image. This requires an in-depth understanding of ML, text processor Image recognition and processing software.

4. Parkinson’s Medical Detection Software

Here you have a patient’s data as well as large community data set. For this, the software helps to have an early diagnosis of the patient with Parkinson’s disease by finding them early and accurately. The algorithm used is XGboost and which is the expansion of extreme gradient boosting. This algorithm is based on decision trees techniques and Machine Learning.

5. Cataract Medical Recognition Software

You have datasets of iris and categorization of the different eye conditions, different eye models based on the patient community data. You need to model the software in such a way that it learns from the data and detects the cataract, based on the conditions and different eye patterns. Regression models and ML can be used to detect the cataract early.

6. Detecting Fraudulent activities in the emails

With the datasets for the emails and conditions which show that emails are used for fraud detection by segregating the emails with keywords and email source. Emails may be used for transferring privacy data and organizing data such as sensitive documents outside the organization to the personal emails and other external document repositories. Using ML we can highlight this fraud by capturing such email even before it goes out of the organization.

7. Flower datasets

Here we have datasets of flower petal and sepal sizes. We can use ML to classify them using the regression techniques into 3 classes and 50 instances in each class. The software built must learn from the examples provided and do the job. 

8. Image detection

Have a large collection of images that form the datasets, Using face recognition, image processing, and object detection we must detect these images. (By using the ML in python and working on the Convolutional Neural Network (CNN) to work and classify the images and detect the images).

9. Shopping habits and targeted marketing

Here we have datasets of people who do mall shopping and they are classified based on gender, age, preference, and interest. You can segregate these large data sets using ML within their categories. This data output can be used for targeted marketing. AI in digital marketing is a niche skill and is the popular buzzword in the market.

10. Trends in search engines

In this project, we need to find out what are the current trends in the Google search engine and Bing search engines and find out what the people are searching for. These trends provide information on current trends, customers and people’s habits. The search engine optimizer helps to optimize the keywords required for the various key meta information to be provided on the websites.

11. Building and housing

We need a lot of data on the house location, crime rates, number of rooms, types of avenues nearby, and so on. Based on these datasets we need to calculate the right prices of the houses with the help of ML and linear regression. Linear regression helps to know the prices that are the unknown values based on the linear relationship between the output and the inputs.

12. Taxi Optimization

Here we gather data sets from popular taxis operations such as Uber or Ola etc. The data sets consist of information about the traffic congestion, maps for tracking the shortest path to reach the destination in the shortest time, number of people, concentration of taxis, locations, etc. We need to build models using ML and find out and optimize what is the best possible profitable way for the business and excellent customer experience.

13. COVID 19 vaccination

For the required symptoms and community trends data, we need to know the action taken by the vaccines developed on different categories of people. This insight provides the output whether the vaccination will be successful and what are the correct symptoms for the vaccine to be used, are there any trends and side effects on any particular set of the community of people. We can use the ML to do this by learning from this vast patient and test datasets.

14. Recommendation systems for the online stores

The retail giants such as Wal-Mart, Flipkart, and Amazon spend millions of dollars in this system. It tracks the user’s behaviors, on what interests the particular users, what are the shopping habits and so on and has built this recommender system to suggest the future shopping needs and to do targeted marketing and targeted advertising.

15. Traffic signs recognition for the self-driving vehicles

Here ML is used in the detection of the traffic signals and classifying them to assist the self-driving vehicles to take action based on the traffic signals. Here supervised learning is used to do this.

16. Image detection for autonomous vehicles

The autonomous vehicles in the future need to understand each object that is seen in the streets such as cars, buses, trees, traffic signals, signboards, read maps, and detect various other objects from the video images to take some best decision. ML is used to segment images and identify the images.

17. Human action recognition

In many cases like for the blind and other normal humans the machines must be trained to understand the hand, face, leg signals, and other body languages to check what they are meant to be. Using ML on the large kinetics dataset you can design software that recognizes this and provide the necessary output.

18. Breast cancer detection and classification

We have large data sets of medical history, patient databases, community information and other Patient Identified Information (PII), etc to segregate and detect breast cancer early in the stages for the number of people who go for diagnosis or medical testing. This is done by ML to classify people and identify the proper diagnosis for the set of people and identify the stages of cancer etc.

19. Color detection and classification

This is a project in ML that has more than 1000 color codes as datasets. The aim is to find out the correct color names from the RGB values of each color. The colors are stored in Hexadecimal numbers. The user is asked to pick the color on the screen and the machine will detect and name the color based on the samples and built-in learning.

20.Speech detection and text detection

One of the easiest and important projects in ML is to detect the speech and find the text which is there in the databases. The machine learns to recognize the speech from different accents and understand the corresponding text. This uses a lot of ML data sets on hours of training the machine.

Conclusion

As you have seen, there are a lot of varied applications of Machine Learning, which you can choose for your Machine learning projects. We recommend you to enrol in Great Learning’s AIML PGP course for mastering AI and machine learning. Great Learning has the best AI course online in the market with expert consultants to train the professionals. Enquire today and visit the Great Learning’s website for more information.