Classic machine learning is a term used to refer to traditional or “old-school” approaches to machine learning, as opposed to more modern or cutting-edge methods. Classic machine learning typically involves the use of well-established algorithms, such as decision trees and support vector machines, to build and train machine learning models on labeled datasets. These algorithms are often relatively simple and easy to understand, and can be applied to a wide range of data and problems. Classic machine learning algorithms are typically supervised, meaning that they require a labeled dataset, with known correct outputs, in order to learn and make predictions. They are also often deterministic, meaning that they always produce the same output for a given input, and are not designed to handle uncertainty or noise in the data.
Just a year ago, ChatGPT was launched, and it has since catalyzed a seismic shift in the AI landscape. Given the astonishing capabilities of generative AI and the rapid evolution of foundation models, one question has risen to the forefront of discussions: What will be the impact of foundation models on classic machine learning and … Read more
Predictive maintenance is a game-changer for the modern industry. Still, it is based on a simple idea: By using machine learning algorithms, businesses can predict equipment failures before they happen. This approach can help businesses improve their operations by reducing the need for reactive, unplanned maintenance and by enabling them to schedule maintenance activities during … Read more
Have you ever found yourself wondering how you can better understand your customer base and target your marketing efforts more effectively? One solution is to use hierarchical clustering, a method of grouping customers into clusters based on their characteristics and behaviors. By dividing your customers into distinct groups, you can tailor your marketing campaigns and … Read more
The insurance industry has long harnessed technology’s transformative power. From online policy applications to modernized claims processing systems, the tech revolution in insurance has been in motion for years. However, machine learning promises to be one of the most influential and disruptive advancements in the sector. Machine learning empowers insurers to analyze vast data volumes, … Read more
Perfecting your machine learning model’s hyperparameters can often feel like hunting for a proverbial needle in a haystack. But with the Random Search algorithm, this intricate process of hyperparameter tuning can be efficiently automated, saving you valuable time and effort. Hyperparameters are properties intrinsic to your model, like the number of estimators in an ensemble … 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 ready to learn about the exciting world of social media sentiment analysis using Python? In this article, we’ll dive into how companies are leveraging machine learning to extract insights from Twitter comments, and how you can do the same. By comparing two popular classification models – Naive Bayes and Logistic Regression – we’ll … Read more
Online shopping has become a part of our daily lives, and online stores are continually seeking to improve their sales. One way to achieve this is by using machine learning to predict customers’ purchase intentions. This innovative process can help businesses understand their customers’ behavior and tailor their marketing strategies accordingly. In this article, we … Read more
Get ready to level up your time-series forecasting game! In this tutorial, we’re going to take things up a notch by showing you how to adjust prediction intervals using Keras recurrent neural networks and Python. Now, you may remember our previous article on stock market forecasting where we made a forecast for the S&P500 stock … Read more
If you travel a lot, you’ve probably already experienced this – you’re in a hurry on your way to the airport trying to catch a flight, only to find out that your flight is delayed. Wasn’t it great to know when a flight will be delayed in advance? We can use past flight delay data … Read more
