Machine Learning is being predicted to be the game changer of the upcoming decade. It has upgraded the world of Data Science by enabling systems to understand and manipulate large data sets. Machine Learning algorithms allow computers to use statistical analysis to deliver values that fall within a specific range.
Machine Learning is a subfield of Artificial Intelligence, which is why the terms Machine Learning and Artificial Intelligence are often used interchangeably. Though these technologies supplement each other, they vary in terms of their core functions.
The emergence of Machine Learning
- Machine Learning has grabbed its placed in hundreds of applications in a variety of disciplines. These applications include advertising, autonomous vehicles, chatbots, cybersecurity, e-commerce, drones, healthcare, marketing and robotics, among others.
- The benefits of Machine Learning are not limited to the elite few anymore. The popularity of product recommenders and chatbots is growing rapidly among the masses.
- The cost of implementing Machine Learning is also reducing rapidly.
It is being believed that human beings will soon be replaced with robots that will be self-guided and self-learning. Let us understand if any such thing is going to happen soon by going through the trends corresponding to Machine Learning these days.
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Machine Learning Trends
2018 has changed the trend from ‘doing digital’ to ‘becoming digital’. Enlisted are some of the major trends that are shaping Machine Learning these days.
- Shift to Unsupervised Learning – The most important trend in Machine Learning is the gradual shift from supervised to an unsupervised learning pattern. Unsupervised learning brings with it a lot of advantages as it doesn’t require large training datasets and works on the principle of self-learning. This method is totally opposite to the supervised learning method which requires training on large datasets. Supervised learning is undoubtedly time-consuming, expensive and prone to errors.
- Reinforcement Learning – Reinforcement learning is a type of programming that allows algorithms to automatically understand the optimal behavior using a system of reward and punishment. An algorithm or an agent learns by interacting with its environment and receives rewards when performs correctly and punishments when performs incorrectly. Reinforcement learning has made a place for itself mainly because of two reasons. Firstly, it has produced great results in a variety of applications and secondly, because the method resembles the way a human brain develops from infancy to adulthood. This type of learning enables machines to use soft skills like feeling and intuition to learning.
- Memory Networks – The major problem while executing tasks occurs during the time of diverse environments. For real-world environments, neural networks must be capable of learning sequential tasks without forgetting. This can be achieved with the help of powerful architectures such as long-short-term memory networks, elastic weight consolidation algorithm and progressive neural networks. This will enable prediction of time and series, slow learning and extraction of useful information from previously learned networks for future use.
- Simulation Environments – Simulation techniques need to be embedded in Machine Learning as it is nearly impossible to generate training data for systems. This will require the generalization of almost all situations. The simulation will help systems to perform effectively in real-world environments.
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Plan a Career in Machine Learning
A lot of companies these days are seeking people with skills in the areas of Machine Learning and Artificial Intelligence. Major names include Google, IBM, Microsoft, Twitter, Qubit, Intel, Apple and Salesforce, among others. Therefore, planning a career in Machine Learning is also seen as a lucrative option for the younger lot.