Machine Learning (ML) has seeped into every aspect of our lives, right from automated chatbots like ChatGTP to crafting personalized playlists. It is the most exciting branch of computer science and also a career choice with the highest growth trajectory. While the subject seems daunting at first, exploring some diverse machine learning projects from a variety of domains will fuel your interest in the field. Here is a curated list of machine learning projects for beginners to boost your understanding of neural networks and their applications.
Uses of Machine Learning
The key value of machine learning is its ability to predict outcomes by analyzing past data through a variety of learning techniques. Some of the real-life applications of advanced ML models include:
- Image recognition and Natural Language Processing (NLP)
- Image processing for healthcare applications
- Speech recognition
- Predicting patient health and recurrence of diseases
- Personalizing entertainment and lifestyle media
- Prediction of market systems, company health, and business potential
Machine Learning Project Ideas for Beginners
This list of machine learning projects covers a variety of ideas for approaches to different ML models:
Sentiment analysis uses NLP to scan thousands of user-generated social media content to analyze user emotions. Businesses utlize this ML application to generate deep insights into customer behavior and usher in optimal consumer satisfaction. Common approaches to sentiment analysis include analyzing the polarity of a text piece, its overall emotional essence, and the opinion polarity of specific business aspects.
MNIST Handwritten Digit Classification
The MNIST (Modified National Institute of Standards and Technology) Handwritten Digit is a standard dataset that uses convolutional neural networks to train a variety of image processing systems. The dataset recognizes handwritten digits within texts by analyzing 60,000 training samples of different sizes and orientations. This beginner-friendly project idea effectively teaches multi-layer pattern recognition with simplicity.
BigMart Sales Prediction ML Project
The BigMart Sales Prediction machine learning project is a go-to training model for beginners. The dataset contains 2013 sales data from 10 BigMart stores in different cities for 1,560 products, relevant product attributes, and information on each store. The aim is to track the specific products and stores that have the highest impact on increasing sales to foresee customer demand and optimize inventory management.
Predicting Wine Quality Using Wine Quality Dataset
The wine quality prediction project needs you to develop a comprehensive model that can independently classify wines of different grades. Apart from age, there are over 10 variables—such as citric acid quantity, residual sugar, and density, among others.—and a dataset of over 4,800 observations to approximate wine quality.
Movie Ticket Pricing System
Training for dynamic pricing and follow-up models is a great launchpad for ML beginners in the era of personalization. The aim is to develop an algorithm for movie booking systems that handles a dynamic range of customers to enhance profitability. Working with the parameters that affect movie pricing, such as supply-demand factors, customer focus on comfort, and viewer sentiments, will hone your computational thinking skills.
Neural Network To Read Handwriting
Intelligent Character Recognition (ICR) has gained immense popularity in the past decade, fast-tracking the progress of business digitization. ICR uses multi-dimensional recurrent neural networks to identify patterns among varied handwriting styles and make the inputs machine-readable. Working with this complex machine learning project will boost your hiring chances as the ICR market size exceeded $6 billion in 2021.
Object Detection Using Deep Learning
It is another ML technique that trains with annotated visuals to identify and name objects within videos, static images, and live footage. Object detection machine learning projects comprise image classification, object localization, and pixel segmentation, helping boost your knowledge of Region-based Convolutional Neural Networks (RCNNN).
Human Activity Recognition Using Smartphone Datasets
The goal of Human Activity Recognition (HAR) is to accurately interpret and classify human fitness data. This project challenges the prevalent classification models due to the sheer number of observations generated every moment and their temporal nature. Working on HAR using smartphone datasets will drastically improve your multi-classification skills with Hybrid Deep Learning (HDL) and Transfer Learning (TL) models.
The development of Artificial Intelligence (AI) and healthcare wearable technology enables remote patient monitoring, robotic surgeries, and telemedicine, among other things, to deliver holistic healthcare services. There is a heavy demand for ML experts who can draw insights from the enormous amount of raw healthcare data.
Fake News Detection
There are two primary types of fake news:
- Linguistic: Contains news in the form of text
- Graphic-based: Contains visual data alongside text
With the use of NLP, Naive Bayes, and Support Vector Machines (SVMs), ML models can combine datasets of fake and authentic news to filter misleading information. The exercise sharpens your ideas on the classification matrix, Tfidf Vectorizer, and sophisticated text-cleaning functions.
Iris Flowers Classification ML Project
The Iris Flowers Project is one of the simplest machine learning projects that teach beginners the basics of data handling. The dataset comprises only four numerical details—length and breadth of petals, length and width of sepals—of three classes of iris flowers. Thus, you don’t need to scale the given data and can readily visualize the dataset to train the ML model.
Recommendation Engines with Movielens Dataset
User personalization has been a driving factor for success in the entertainment industry. The Movielens Dataset is the most extensive collaborative support base comprising one million movie ratings for 3,900 films. Using NLP and deep learning techniques, you can design a neural network that suggests movies to users based on their previous queries and other preferences of similar users.
Stock Prices Predictor
The stock price predictor datasets work with granular data and comprise a large array of fluctuating indicators. As stocks undergo regular micro-variations, ML models deploy time series forecasting methods that analyze events over a specific time period. To predict stock market movements, you can start with simpler problems. Furthermore, predicting the price movements of a specific organization based on its past data and fundamental performance indicators.
Prepare Ml Algorithms from Scratch
A background in Python and developing simple ML algorithms from scratch is a solid starting point. For creating state-of-the-art ML models. This will enhance your understanding of what goes on inside neural networks. Moreover, apart from the application of the different linear, non-linear, and ensemble algorithms.
Parkinson’s Disease (PD) is a neurodegenerative motor disorder that affects the central nervous system. Diagnosing PD in its earliest stages is now possible with ML models and it is one of the most crucial medical applications of data science. These models are subjected to data on neuroimaging, handwriting patterns, cerebrospinal fluid, fMRIs, and other brain scans. For advanced studying of symptoms often missed or misinterpreted by doctors.
Sports Score Predictor
Working with sports datasets is one of the most effective ways to master data exploration and visualization. You can select player performance statistics from different sports to perform efficient talent scouting. Also, with regression analysis and classification models, you could generate insights into team management. This can be done by calculating individual and team strengths and weaknesses.
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By Bishwadeep Mitra