Machine Learning Infographic – Everything You Need to Know About ML
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What is Machine Learning?
ML is the art of granting machines the ability to think. It is a field of computer science that uses statistical techniques to give machines the ability to learn without being explicitly programmed. Researchers claim this is a good way to observe progress toward human-level AI. This Machine Learning Infographic is specially designed for beginners, covers all the basic concept of ML in an image form.
Machine Learning deals with building algorithms that can receive input data, perform statistical analysis to predict output, and update the output as newer data become available.
Machine Learning is – Optimizing a performance criterion using example data and past experience. Alpavdin
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Machine Learning Infographic
Kinds of Learning in ML
ML is a subset of Artificial Intelligence (AI). One part of the family of machine learning methods is Deep Learning. This is an approach to Machine Learning and is based on learning data representations (feature learning) instead of on task-specific algorithms.
The three kinds of learning in Machine Learning are:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Steps in Machine Learning
Often, we carry out the following set of steps in Machine Learning:
- Collect data
- Filter data
- Analyze data
- Train algorithms
- Test algorithms
- Use algorithms for future predictions
Ethical Challenges in Machine Learning
There are some ethical challenges that Machine Learning presents:
- Digitization of cultural prejudices on use of systems trained on biased datasets (algorithmic bias)
- Training of machines on languages corpora that contain biases
- The possibility that these systems in the health care field might be designed not in public interest, but to boost income
While Machine Learning can prove to be really effective in improving health care, these ethical questions must be addressed first.
We hope this Machine Learning infographic could serve to intrigue you to consider a career in the domain. We have a complete series of Machine Learning tutorials. You may refer them to learn Machine Learning. Keep reading and leave your comments and suggestions below.