Here you’ll find everything about AI use cases, including Python tutorials on implementing specific use cases.
A machine learning use case is a specific application or instance of using algorithms and techniques to solve a problem or accomplish a task. It typically involves a combination of data preparation, model selection, training, and deployment. AI and machine learning are closely related. In fact, machine learning is a subset of AI, which refers to the use of algorithms and statistical models to enable a system to improve and learn from data over time. In other words, machine learning algorithms allow AI systems to automatically learn and improve from experience, without being explicitly programmed. AI can be applied in a wide range of industries and applications. Some common examples of AI in action include virtual assistants like Siri and Alexa, which use natural language processing to understand and respond to user requests. Another example is self-driving cars. They use a combination of computer vision and machine learning algorithms to navigate roads and traffic. Additionally, AI is often used in healthcare, finance, and other industries. In all these industries, AI improves decision-making, automate processes, and enhance the customer experience.
Embark on a journey into the world of unsupervised machine learning with this beginner-friendly Python tutorial focusing on K-Means clustering, a powerful technique used to group similar data points into distinct clusters. This invaluable tool helps us make sense of complex datasets, finding hidden patterns and associations without the need for a predetermined target variable. … Read more
Credit card fraud has become one of the most common use cases for anomaly detection systems. The number of fraud attempts has risen sharply, resulting in billions of dollars in losses. Early detection of fraud attempts with machine learning is therefore becoming increasingly important. In this article, we take on the fight against international credit … Read more
The digital age presents us with an unmanageable number of decisions and even more options. Which series to watch today? What song to listen to next? Nowadays, the internet and its vast content offer too many choices. But there is hope – recommender systems are here to solve this problem and support our decision-making. They … Read more
This tutorial develops a Twitter bot in Python that will generate automated trading signals. The bot will pull real-time price data on various cryptocurrencies (Bitcoin, Ethereum, Doge, etc.) from the crypto exchange Gate.io and analyze it using predefined rules. Whenever the bot detects a relevant price change, it automatically posts a tweet via Twitter. Simple … Read more
You can do various things with cryptocurrency price data, such as creating forecasting models, illustrating historical prices, or performing chart analysis. But first, you need to get hold of the data. Nowadays, several APIs provide access to cryptocurrency price data. One of the most important and trusted sources is Coinmarketcap.com. The website offers multiple API … Read more
In this tutorial, we’ll be using machine learning to predict and map out crime in San Francisco. We’ll be working with a dataset from Kaggle that contains information on 39 different types of crimes, including everything from vehicle theft to drug offenses. Using Python and the powerful Scikit-Learn library, we’ll train a classification model using … Read more
Time series analysis and forecasting is a tough nut to crack, but the ARIMA model has been cracking it for decades. ARIMA, short for “Auto-Regressive Integrated Moving Average,” is a powerful statistical modeling technique for time series analysis. It’s particularly effective when the time series you’re analyzing follows a clear pattern, like seasonal changes in … Read more
Are you intrigued by the fascinating world of cryptocurrency and looking to visually decipher its price trends? Welcome aboard! In this comprehensive tutorial, we will explore creating color-coded line charts using Python and Matplotlib, a powerful tool for effective analysis of changes along a third dimension. The past few years have witnessed a meteoric rise … Read more
This tutorial shows how to use Convolutional Neural Networks (CNNs) with Python for image classification. CNNs belong to the field of deep learning, a subarea of machine learning, and have become a cornerstone to many exciting innovations. There are endless applications, from self-driving cars over biometric security to automated tagging in social media. And the … Read more
Customer retention is a prime objective for service companies, and understanding the patterns that lead to customer churn can be the key to maintaining long-lasting client relationships. Businesses incur significant costs when customers discontinue their services, hence it’s vital to identify potential churn risks and take preemptive actions to retain these customers. Machine Learning models … Read more
Are you struggling to find the best hyperparameters for your machine learning model? With Python’s Scikit-learn library, you can use grid search to fine-tune your model and improve its performance. In this article, we’ll guide you through the process of hyperparameter tuning for a classification model, using a random decision forest that predicts the survival … Read more
