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 –Â Optimizing a performance criterion using example data and past experience. Alpavdin
Netflix saved $1 Billion in 2018 because of its Machine Learning algorithm. StatWolf
The revenue generated by AI software will grow from $1.4 billion in 2016 to $59.8 billion by 2025. TracticaÂ
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
Other related forms of Machine Learning include semi-supervised and self-supervised learning methods. Semi-supervised learning involves using both labeled and unlabeled data to produce better results as compared to supervised learning. Self-supervised learning on the other hand involves creating labels for the data and this helps in minimizing the use of big labeled data set. These advanced approaches are further upgrading the functionalities and performances of the Machine Learning systems in numerous applications.
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
It is common to observe that Machine Learning models are updated via feedback cycles and assessment of performance. This process of continual updating minimizes over-fitting and allows the models to incorporate new information, hence providing more accurate prediction as the pattern changes with time. Furthermore, integration of the Machine Learning applications with real time data sources help in improving the application performance in terms of speed and precision in highly volatile situations.
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.
Conclusion
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.
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