Predicting stock prices: Use machine learning algorithms to analyze historical stock market data and make predictions about future stock prices.
Image classification: Build a machine learning model that can classify images into different categories, such as identifying animals in photos or detecting objects in images.
Chatbot: Build a chatbot using natural language processing (NLP) to understand and respond to user input.
Sentiment analysis: Build a machine learning model that can analyze text data, such as customer reviews, and determine the sentiment expressed in the text.
Recommender system: Build a recommendation system that can suggest products or services based on user preferences and past behavior.
Speech recognition: Build a machine learning model that can transcribe speech to text, which can be used for applications such as voice-controlled assistants.
Fraud detection: Build a machine learning model that can detect fraudulent transactions or behavior, which can be used in applications such as banking and finance.
Object detection: Build a machine learning model that can detect objects in images or videos, which can be used for applications such as security surveillance.
Predicting stock prices: Use machine learning algorithms to analyze historical stock market data and make predictions about future stock prices.
Image classification: Build a machine learning model that can classify images into different categories, such as identifying animals in photos or detecting objects in images.
Chatbot: Build a chatbot using natural language processing (NLP) to understand and respond to user input.
Sentiment analysis: Build a machine learning model that can analyze text data, such as customer reviews, and determine the sentiment expressed in the text.
Recommender system: Build a recommendation system that can suggest products or services based on user preferences and past behavior.
Speech recognition: Build a machine learning model that can transcribe speech to text, which can be used for applications such as voice-controlled assistants.
Fraud detection: Build a machine learning model that can detect fraudulent transactions or behavior, which can be used in applications such as banking and finance.
Object detection: Build a machine learning model that can detect objects in images or videos, which can be used for applications such as security surveillance.