Before exploring the machine learning project ideas, let us revisit the history. Many scholars have in-depth knowledge about Machine learning concepts and algorithms, but that alone is not sufficient to get a clear foundation about the tweaks of these concepts. To get your hands on machine learning, you need to do a few ML projects by yourself, try to resolve the errors you encounter independently, this will enhance your skills and build a strong portfolio.
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8 Best Machine Learning Project Ideas in 2021
We understand that for machine learning beginners it becomes really difficult to sort out feasible project ideas, they take references from numerous sources and end up getting confused. We have prepared a detailed list that contains some of the top Machine Learning project ideas for beginners. You can also land up getting a job or an internship with the help of these projects. These ideas can also be used in Machine Learning Hackathons.
Check out the coolest Machine Learning project ideas, given below:
1. Predictive Machine Learning Applications
Machine learning is very densely employed for making predictions. Predictive models can be built very efficiently by elementary machine learning algorithms such as Linear Regression. For deriving a prediction, you need a large set of basic information.
If you have a theoretical concept of regression, you can apply it to solving real-life problems. Typically beginners engage themselves in creating a stock price predictor or Covid-19 spread rate predictor. You can come up with a new concept and build a predictive model by analysing current fluctuations for making forecasts.
For Example: GammaStack is a customisable software used for predicting sports results. This is widely used before making a bet so that the chances of winning are maximised. For giving comprehensive results, this algorithm relies on only one regression algorithm.
Tutorial: For housing price prediction, you can refer to this Kernel on Kaggle. This gives you a complete idea about importing data, reading dataset and applying regression algorithms.
2. Sentiment Analysis
Sentiment analysis aims at revealing emotions in the text. With the help of movie reviews, customer feedback, support tickets generated, unique inferences are drawn by firms. It is a very practical and in-demand skill to learn building sentiment analysis models.
You don’t even need to collect the training data by yourself. For training and testing your model, you can refer to the largest open-source sentiment analysis based database, created and maintained by IMDb.
For Example: Brand24 allows companies to keep a record of their mentions on social media sites and assess opinion polarization using artificial intelligence.
Tutorial: There is a free course by Analytics Vidhya on YouTube that teaches you how to conduct sentiment analysis using Twitter.
3. Exploratory Analysis
Dealing with unstructured data has always been tedious, this includes search history, images, transactions or unstructured text. If the amount of unstructured data is large, human analysis becomes ineffective. You need to reveal patterns using ML and employ them for drawing conclusions.
You can carry out exploratory analysis on any piece of data, even speeches of men and women can be analysed for deriving patterns. If you are interested in sports, you can go for collecting historical data over the past years and build a model for depicting a player’s success.
For Example: Banks figure out a candidate’s eligibility with the help of exploratory analysis.
4. Anomaly Detection
An anomaly detection system reveals potentially harmful or fraudulent activities. It is a very promising field for cybersecurity experts, as it helps them to study suspicious transactions or search inquiries.
Anomaly detection is widely used in the diagnosis of healthcare issues. You can add the breast cancer dataset to build your first machine learning healthcare model for improving medical diagnosis.
For Example: Anodot is a popularly used product for business anomaly monitoring. It identifies and reports any suspicious activity in real-time.
Tutorial: Refer this tutorial you learn about cancer detection.
5. Image Recognition
Image recognition systems can be built very easily. It is very useful to learn the use of artificial neural networks for solving real-life tasks. The computer can be trained for classifying images, recognising faces, and finding objects in the frame.
ImageNet comes with a large number of diverse images for building the dataset. NNs can be used to identify handwritten stuff. The dataset MNIST can be used for providing examples of handwritten digits.
For Example: Noldus uses image recognition technology to infer emotions. FaceReader automatically analyses facial expressions and generates reports based on the findings.
Tutorial: Stanford provides a YouTube course on computer vision and image recognition.
6.Social Media Mining
Social media mining can be fairly considered to be a type of exploratory research. In this, developers use posts, comments, likes, reactions, connections and reviews from various social media handles such as Facebook, Instagram, Twitter, and LinkedIn. This process is intersectional: it may also include sentiment analysis and anomaly detection.
Parsing through enormous amounts of raw social media data for building an algorithm is one of the key aspects of social media mining. There is a Social Media Mining Toolkit available for assistance. Monitoring people’s likes and dislikes, tracking their online behaviour, previous buying pattern, reviews, is widely used in marketing.
For Example: Supermetrics is a tool for marketers used for discovering hidden patterns in datasets taken from different resources such as Facebook, Instagram, Google Analytics etc.
Tutorial: The University of Washington provides a free course on social media mining.
Coding Ninjas’s Machine Learning Career Track is one destination that fulfills several prospects. This course will complement your learning experience with the blend of Machine Learning, Foundation in Python, Data Structures, and Algorithms. The implication of such a trend will improve your reachability and explore different segments of technology.
7. Recommendation System
ML can be used for building a recommendation engine, this gives you an actual idea about personalized online customer experience. This is widely used by e-commerce sites, media pages, news portals, e-book sites and content providers to keep the customers engaged and amused with their product.
You can try out MovieLens dataset, which is the largest open-source dataset with public ratings for a recommender system for movies. Alternatively, you can go for Youtube trending video stats. Click here, for more recommender datasets.
For Example: Netflix’s Cinematch is a remarkable recommendation based system, refer to it.
Tutorial: Follow Google’s crash course on recommender system design is well aligned for beginners.
Any beginner can create a chat by going through a step-by-step tutorial. Chatbots are now a must for business units to avoid communication delays, it also helps employees in orientation. It is a must for an ML developer to know about the creation of a ChatBot.
You can seek chatbot datasets on Reddit or create your own knowledge base. For communication tutorials, Twitter is conventionally used.
For Example, ChatBot is a widely used SaaS platform that allows business units to build a chatbot without any actual coding.
Tutorial: You can even create diverse chatbots that are in feminine voice to avoid racism and sexism.
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Frequently Asked Questions
You can do a variety of projects on Machine Learning: numerous kinds of analysis and predictions can be carried out using machine learning. You just need a viable problem statement and then you may efficiently build the model using the trained data.
You can find hundreds of machine learning projects and libraries on GitHub, you can even collaborate and contribute to any of these projects as most of them are open sources.
Understand and define the problem statement in your own words.
1. Analyse and prepare the training data.
2. Apply the ML algorithms.
3. Reduce the errors by iterative testing.
4. Predict the results using the model.
1. Identifying the problem and deriving the use-case.
2. Gathering and processing data.
3. Development and deployment of the model.
1. Data Collection
2. Data Normalisation
3. Data Modeling
4. Model Training and Feature Engineering
5. Deploying Models to Production
You need to do a handful of machine learning projects independently to improve your machine learning skills. Try to implement algorithms for multiple domains, this will help you in finding your eventual interest, it can be finance, sports, medical, academics, and so on.
Learn one of the most powerful and portable programming languages C++ and become eligible to apply for the positions at Google, Microsoft, Facebook, Amazon etc.
A project gives you the actual in-depth knowledge of algorithms, when you do real-time problem solving, then only you understand the actual significance of space and time complexity. To reach the optimal solution, a lot of modifications have to be made to the basic code. You can always seek out collaborators and post your queries for help if you stumble anywhere.
By Vanshika Singolia