Artificial intelligence can be used to solve problems across the board. AI can help businesses increase sales, detect fraud, improve customer experience, automate work processes and provide predictive analysis.
Artificial Intelligence solutions
Industries like health care, automotive, financial services and logistics have a lot to gain from AI implementations. Artificial intelligence can help health care service providers with better tools for early diagnostics. The autonomous cars are a direct result of improvements in AI.
Financial services can benefit from AI-based process automation and fraud detection. Logistics companies can use AI for better inventory and delivery management. The retail business can map consumer behavior using AI. Utilities can use smart meters and smart grids to decrease power consumption communication.
How Do You Decide Which Machine Learning Algorithm to Use?
How AI/Machine Learning Works?
Choosing the right algorithm can seem overwhelming—there are dozens of supervised and unsupervised machine learning algorithms, and each takes a different approach to learning.
There is no best method or one size fits all. Finding the right algorithm is partly just trial and error—even highly experienced data scientists can’t tell whether an algorithm will work without trying it out. But algorithm selection also depends on the size and type of data you’re working with, the insights you want to get from the data, and how those insights will be used.
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
Data is the lifeblood of all business. Data-driven decisions increasingly make the difference between keeping up with competition or falling further behind. Machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition.
Why Machine Learning Matters
With the rise in big data, machine learning has become a key technique for solving problems in areas, such as:
- Computational finance, for credit scoring and algorithmic trading
- Image processing and computer vision, for face recognition, motion detection, and object detection
- Computational biology, for tumor detection, drug discovery, and DNA sequencing
- Energy production, for price and load forecasting
Automotive, aerospace, and manufacturing, for predictive maintenance
- Natural language processing, for voice recognition applications
More Data, More Questions, Better Answers
Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are used every day to make critical decisions in medical diagnosis, stock trading, energy load forecasting, and more. For example, media sites rely on machine learning to sift through millions of options to give you song or movie recommendations. Retailers use it to gain insight into their customers’ purchasing behavior.
When Should You Use Machine Learning?
Consider using machine learning when you have a complex task or problem involving a large amount of data and lots of variables, but no existing formula or equation. For example, machine learning is a good option if you need to handle situations like these:
Hand-written rules and equations are too complex–as in face recognition and speech recognition.
The rules of a task are constantly changing — as in fraud detection from transaction records.
The nature of the data keeps changing, ans the program needs to adapt — as in automated trading, energy demand forecasting, and predicting shopping trends.