Machine learning analytics
Updated:03/06/2020 by Computer Hope
Machine learning is the concept that a computer program can learn and adapt to new data without human intervention. Machine learning is a field of artificial intelligence (AI) that keeps a computer's built-in algorithms current regardless of changes in the worldwide economy.
Importance of Machine Learning .
Machine learning is a key component in technologies such as predictive analytics and artificial intelligence. The automated nature of machine learning means it can save time and money, as developers and analysts are freed up to perform high-level tasks that a computer simply cannot handle.On the flip side, you have a computer running the show and that’s something that is certain to make any developer squirm with discomfort. For now, technology is imperfect. Still, there are workarounds. For instance, if you’re employing machine learning technology in order to develop an algorithm, you might program the machine learning interface so it just suggests improvements or changes that must be implemented by a human.ses are not just simply collecting all of this data that we are generating. They’re actually analyzing it, and finding ways to improve their products and services, which in turn shapes our lives and the experiences that we are having with the world around us.
Advantages of Machine Learnng
- Simplifies Product Marketing and Assists in Accurate Sales Forecasts
- Facilitates Accurate Medical Predictions and Diagnoses
- Simplifies Time-Intensive Documentation in Data Entry
- ML also has a significant impact on the finance sector. Some of the common machine learning benefits in Finance include portfolio management, algorithmic trading, loan underwriting and most importantly fraud detection.
- Easy Spam Detection.
- Increases the Efficiency of Predictive Maintenance in the Manufacturing Industry.
- Better Customer Segmentation and Accurate Lifetime Value Prediction.
Key Machine Learning technologies and tools
- Microsoft Azure Machine Learning , which is an open source framework for storing and processing big data sets. Hadoop can handle large amounts of structured and unstructured data.
- IBM Watson Watson is an IBM supercomputer that combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a “question answering” machine. The supercomputer is named for IBM’s founder, Thomas J. Watson.Watson is an IBM supercomputer that combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a “question answering” machine. The supercomputer is named for IBM’s founder, Thomas J. Watson.IBM Watson is a system based on cognitive computing. You can say, IBM Watson is a system which will provide answer(s) to your question(s).Now, the big question, What does cognitive computing actually mean?Cognitive computing is a technique which is a mixture of different techniques such as machine learning, natural language processing, artificial intelligence, human interaction, reasoning etc.
- Google TensorFlow TensorFlow is an end-to-end open source platform for machine learning. TensorFlow is a rich system for managing all aspects of a machine learning system; however, this class focuses on using a particular TensorFlow API to develop and train machine learning models. See the TensorFlow documentation for complete details on the broader TensorFlow system.
- Amazon Machine Learning Amazon Machine Learning (Amazon ML) is a robust, cloud-based service that makes it easy for developers of all skill levels to use machine learning technology. Amazon ML provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon ML makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Machine Learning Problems
There are perhaps 14 types of learning that you must be familiar with as a machine learning practitioner; they are:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Hybrid Learning Problems
- Semi-Supervised Learning
- Self-Supervised Learning
- Multi-Instance Learning
- Inductive Learning
- Deductive Inference
- Transductive Learning
- Multi-Task Learning
- Active Learning
- Online Learning
- Transfer Learning
- Ensemble Learning