How Industries Use The Technology of Machine Learning?
Artificial intelligence and Machine Learning technologies are the next future for the world.
As a machine learning enthusiast, you must have heard the above statement. But have you ever thought that when that future will come?
You know what? The transition towards ML technology from traditional programming has already been started. This article will give you 5 hot areas where Machine learning is already a full-fledged product. Companies like Microsoft, Google, Uber, Amazon, and many more have already placed their products to work in the real market.
1. ML for recommendations
When it comes to recommending a product that similar users have preferred, ML is a one-stop point for industries. Algorithms such as KNN, decision trees, Random Forests can be used for this purpose. These algorithms' implementation is very bi-directional; that is, an algorithm says KNN is developed to prepare a customer list to recommend a product. Another KNN is built to prepare a list of products to recommend a customer. A naive way of implementing is encoding every customer and product, and The products and the customer in the environment are label-encoded. You can easily find these recommender systems in your Amazon or Flipkart applications.
2. ML for time-series forecasting
Pic Credit: SciJinks
Time series forecasting is a problem that is now dealt with deep learning more. Nevertheless, many ML algorithms have proven to deliver the same accuracy as the deep learning models. Some traditional Machine Learning models such as Random Forest, Gradient Boosting, and even conventional neural networks with time delay incorporated can be expected to deliver the same accuracy as deep learning models, at a lower complexity cost and smaller storage requirement model parameters. This is an application employed by almost all actively working industries to monitor different aspects such as their sales, growth, publications, profits, etc. Weather forecasting can be the best example for this use case.
3. ML for pattern recognition
Pattern recognition is a way of matching the information stored in the database with the incoming data. In other words, Pattern recognition algorithms commonly aim to provide a rational answer for all conceivable inputs and to perform “odds-on” matching of the inputs, bearing in mind their statistical disparity. Pattern recognition helps enable tasks such as recognizing texts from a picture.IBM acquired the patent for pattern recognition using Optical Character Recognition (OCR) by Emanuel Goldberg. It is a must-have key in many applications in the areas of Artificial Intelligence and Computer Vision.
4. ML for medical science
Pic Credit : Diginomica
Many industries are using machine learning technologies to build image processing tools to analyze three-dimensional medical images. Microsoft has been working on InnerEye, which aims for powerful medical image analysis such as tumor segmentation and blood cell type prediction to aid the health-care sector. Similar approaches were made in analyzing clinical texts and establishing relations such as a drug to its adverse effects or its reason. Breast cancer classification can be a perfect example of this.
5. ML for autonomous navigation
Pic Credit: Zack’s Notes
Autonomous navigation and manipulation are critical focus areas for delivering, constructing, and handling materials harmful to human beings. Nvidia is working on multiple areas to accomplish such tasks. This involves combining real-world data with other sensors such as LiDAR, Depth Map, etc., to develop models that better understand the surroundings and the environment in general. Some of these tasks are
- Stereo depth sensing: attaining the depth from pixels
- Visual odometry: using visual or sensor data to estimate the change in position
Some of the famous cars that are already in the market are Waymo, Uber Taxi.
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