6 ML Projects That Every Beginner Should Try

Wine is differentiated based on its flavour, undernotes, smell and colour. The taste of wine is acquired over time, so how do we know if one is good or not in the absence of a wine expert? Machine learning to the rescue. Beginners can use the R programming language to build an ML model to predict the quality of wine with the help of available datasets, using data exploration, regression models and data visualisation. 

This beginner-level ML project allows programmers to create an algorithm that can scrape tweets using a natural language processor. The algorithm can determine tweets matching specific themes, keywords, or individuals. Using a sentiment analysis with a text mining algorithm will thus allow one to filter tweets quickly and seamlessly.

An easy and fun project for beginners starting out with machine learning would be building an algorithm to convert handwritten documents into digital notes or files. Also known as the offline Handwritten Text Recognition (HTR) system, this algorithm can transcribe the text in scanned images into digital text. To build this algorithm, one would need Python, TensorFlow, NumPy and OpenCV. Building this algorithm would help beginners try their hands in concepts like image recognition, deep learning and neural networks. The project uses logistic regression.

Loans form an integral part of any bank. It is the loan interest from which banks earn most of their profits. Beginners can use Python to build simple ML projects for loan prediction. The project will introduce developers to concepts like logistic regression, decision tree and random forest. 

The fake news detection model will introduce programmers to the concept of text classification . This hands-on project trains the deep learning model to detect fake news from a given news compilation. Beginners can use Python for this project.