Top 10 Machine Learning Projects for Beginners on Guru99

Top 10 Machine Learning Projects for Beginners on Guru99

Are you a beginner in machine learning eager to get your hands dirty with projects? Look no further! In this article, we’ll present the top 10 machine learning projects for beginners on Guru99, along with a brief introduction to machine learning for context.

Before we dive into the projects, let’s define what machine learning is. Machine learning is a branch of artificial intelligence focused on enabling machines to learn from data, without being explicitly programmed to do so. In simpler terms, it’s teaching computers to learn from data and make decisions based on that learning.

Now, let’s get started with the top 10 machine learning projects for beginners on Guru99.

1. Iris Flowers Classification

This project involves using the famous iris dataset to classify different species of iris flowers based on their features such as petal length and width, and sepal length and width. You’ll use the K-Nearest Neighbors algorithm to make predictions. This project is a great way to dip your toes into machine learning and get a feel for working with datasets.

2. Credit Card Fraud Detection

In this project, you’ll work with credit card transaction data to detect fraudulent transactions using machine learning. You’ll use the Isolation Forest algorithm along with Local Outlier Factor to detect anomalies in the data, which may indicate fraudulent activity. This project is a great example of applying machine learning in real-world scenarios.

3. Text Classification

Text classification involves classifying text into different categories based on its content. In this project, you’ll use a dataset of news articles to classify them based on their topics. You’ll use the Naïve Bayes algorithm to make predictions. Text classification is a widely-used technique in natural language processing and is used in various applications such as sentiment analysis and spam detection.

4. Human Activity Recognition

Human Activity Recognition (HAR) involves recognizing different human activities based on data from wearable sensors. In this project, you’ll use HAR datasets to differentiate between different activities such as walking, running, and sitting. You’ll use various machine learning algorithms such as Decision Trees and Random Forests to make predictions.

5. Image Recognition

Image recognition involves identifying and classifying images based on their content. In this project, you’ll use a dataset of handwritten digits to build a model that can recognize the digits. You’ll use the Support Vector Machine algorithm to make predictions. Image recognition is widely used in various industries such as healthcare and self-driving cars.

6. Stock Price Prediction

In this project, you’ll use machine learning to predict stock prices based on historical data. You’ll use various machine learning algorithms such as Linear Regression and Time Series Analysis to make predictions. Stock price prediction is widely used in the financial industry to make investment decisions.

7. Movie Recommendation System

A recommendation system is a type of machine learning algorithm that suggests items to users based on their preferences. In this project, you’ll build a movie recommendation system using the MovieLens dataset. You’ll use collaborative filtering techniques to make recommendations based on user ratings.

8. Handwritten Text Recognition

Handwritten text recognition involves converting handwritten text into digital text. In this project, you’ll use machine learning to recognize handwritten text using the MNIST dataset. You’ll use various machine learning algorithms such as Convolutional Neural Networks to make predictions. Handwritten text recognition is widely used in various applications such as digitization of old documents.

9. Customer Segmentation

Customer Segmentation involves dividing customers into different groups based on their behavior and preferences. In this project, you’ll use customer behavior data to segment customers into different groups using various clustering algorithms such as K-Means and Hierarchical Clustering.

10. Facial Recognition

Facial recognition involves recognizing faces in images and videos. In this project, you’ll build a facial recognition system using the AT&T dataset. You’ll use various machine learning algorithms such as Principal Component Analysis and Linear Discriminant Analysis to make predictions. Facial recognition is widely used in various industries such as security and law enforcement.

In conclusion, these top 10 machine learning projects for beginners on Guru99 are a great way to dive into the world of machine learning and gain hands-on experience. Each project covers a different aspect of machine learning and provides a unique learning opportunity. So, get your hands dirty and start building!