Machine learning is one of the subsets of the artificial intelligence domain that involves algorithms and statistics to allow computers to learn from data. Machine learning has taken over the world! It is more popular than ever with all the progress and development happening. Because of this, we will be seeing more jobs being created in the future than ever before. But where do we start? Which jobs are worth choosing? Which projects should we work on? Which online courses should you choose? And which projects can help us stand out amongst our peers when applying for jobs? Don’t worry; this article is going to help you out with some of these questions. This way, you can have a collection of examples to back up your skills, giving you a much better chance at getting that job you always wanted!

Netflix Recommendation System

This project is about implementing a recommendation system for Netflix. It uses the IMDB dataset and provides an interactive web app to explore different models. This project is good for getting familiar with basic concepts of machine learning and how it works in practice.

Spotify Music Recommender System

Another project aims at creating a music recommender system using Spotify data. If you want to work on something more challenging, this is a good choice as it requires some more advanced knowledge in order to get it done properly.

Customer Service Chatbot

This project aims at creating a chatbot that will be able to answer customer service questions and provide relevant information based on user input. It also requires some basic knowledge of NLP (Natural Language Processing).

Stock Market Prediction

The aim of this application is to use machine learning algorithms such as Random Forest and Support Vector Machines to predict stock market movements. The results will be compared with actual stock market movements over time and evaluated to see how accurate they are in predicting future stock prices.

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Real-Time Spam Detection

Spam is a major problem on the web, and it only gets worse with time. A spam detection system classifies emails as spam or non-spam. The system can determine whether an incoming email is spam or not based on its content and sender information. It uses Machine Learning algorithms to classify emails into spam and non-spam categories.

The goal of this project is to build a real-time spam detection system using machine learning algorithms such as Naive Bayes, Support Vector Machines, and k-Nearest Neighbors.

News Article Classification System

This project aims at classifying news articles into different categories, such as politics, sports, etc., based on their content using machine learning algorithms such as Decision Trees, Naive Bayes, and SVM.

Mood Recognition System

This project uses machine learning algorithms for the purpose of classifying the mood of a person by analyzing his/her facial expressions. The system can identify positive, negative, or neutral moods from a person’s face by detecting facial expressions like smiling, frowning, eyes wide open, etc., which are triggered when a person feels happy, sad, or angry, respectively.

YouTube Comment Sentiment Analysis

The primary aim here is to train your model on a dataset of positive and negative comments from YouTube videos and then predict the sentiment of new comments. You’ll use Natural Language Processing (NLP) techniques like word vectors, bag-of-words, and n-grams to extract features from the text. This project will help you understand how NLP works and how it can be used to solve real-world problems in machine learning.

Wildlife Object Detection System

This project uses computer vision to detect objects using images taken by drones flying over wildlife habitats – specifically birds, elephants, and rhinos. You’ll use deep learning models like YOLOv2 or ResNet50 along with the OpenCV library to classify images into categories like “bird” or “elephant”.

Neural Network Image Classification using Convolutional Neural Networks

In this project, you will learn how to build a neural network image classifier using convolutional neural networks (CNNs). The model is trained by using the MNIST dataset, which contains handwritten digits from 0-to 9. This project requires knowledge of Python libraries like NumPy and SciPy, but no knowledge of deep learning concepts is needed.

Sales Forecasting

The process of predicting future sales on the basis of historic data and other factors is called Sales forecasting. Sales forecasting can help companies make better decisions about inventory levels and staffing. A basic sales forecast model would include elements such as past sales, seasonality, macroeconomic indicators, competitors’ actions, and other factors that could affect sales. The most basic form of sales forecasting is using historical data to predict future results. More sophisticated techniques can be used to improve accuracy.

Market Basket Analysis

Market basket analysis is a technique used by retailers to understand their customers’ buying habits. It involves analyzing groups of items purchased together, known as baskets or bags. For example, if someone buys milk as well as bread at the same time, this could indicate that they have a family. This data can be helpful in targeted marketing campaigns at specific groups of people with products an individual might want to buy based on their basket analysis results.

Myers-Briggs Personality Prediction App

The Myers-Briggs Type Indicator (MBTI) is a popular personality assessment that was developed by Katharine Cook Briggs and Isabel Briggs Myers in the beginning of the 1940s. The test identifies 16 different personality types based on how people perceive the world and make decisions. It’s been used by psychologists and businesses since its inception, but it’s also become popular among laypeople who want to learn more about themselves.

A simple app that predicts which MBTI type someone has based on their responses to questions would be an excellent project for beginner programmers because it doesn’t require much code or resources — just a website where people can take the test and submit their results.