How Machine Learning is Taking Center Stage?

Google acquired the startup Moodstacks which develops machine learning based image recognition capabilities for smartphones. To improve its users’ experience of photos and videos on its platform, Twitter has acquired Magic Pony. Machine learning startups have amassed venture funding in the range of $4 billion all over the globe. MIT researchers are simulating object recognition through machine learning. There is so much hype on this child of artificial intelligence in tech newsletters, magazines.

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Machine learning unboxed

Machine learning is a sub-branch of artificial intelligence. Without being explicitly programmed, it allows computers to get into a self-learning mode. Upon being exposed to new information, these programs are enabled to analyze, grow and develop by way of themselves. Machine learning is the method with the aid of which patterns are discovered within many data sources from multivariate statistics, advanced analytics and records mining. This is helpful to the user in making predictions.

Big data companies can optimally collect and analyze data using machine learning. Machine learning is a much more powerful way to analyze information which can work with sizable quantities of different forms of data this is constantly changing. It has the capacity to analyze a complete dataset, not just a small part of it, and also provides highly accurate results. Along with this you can also expect speedy analytics.

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Why should you learn machine learning?

It is widely used in computational finance like algorithmic trading and credit scoring. In the computer vision and photo processing domain machine learning is used for object detection, face recognition, and motion detection. It is used for DNA sequencing, tumor detection, and drug discovery in computational biology. In the energy sector, for load forecasting and pricing machine learning is used heavily. To achieve predictive maintenance it is used in automotive, aerospace, and manufacturing. For voice recognition capabilities, it is heavily used in natural language processing. What do you think is behind the research of Google, Uber, and Tesla on self-driving cars? It is machine learning. What do you think is used in developing recommender engines for Netflix? Can you guess how Facebook simulates friend suggestions through its large network? Machine learning is the answer to both of these questions.

Machine learning is more appropriate for the current times as compared to the traditional statistical methods which used static models. A lot of machine learning techniques are essentially enhanced upgrades of the previous statistical techniques. Take for example pattern recognition which relied on regularities in the data to predict the outcomes. It is simply another word for supervised learning in machine learning world. You can only get closed form results from traditional regression and classification models. But if you use gradient boosting which is a machine learning concept you can iteratively and continuously find the local minimum. This technique only gets better in time as it adapts and learns with every successive iteration.

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Supervised, Unsupervised, Reinforcement and semi-supervised machine learning

Supervised learning is where the inputs and outputs are precisely identified and the algorithms are trained making use of labeled examples. But this learning heavily uses training set and serves as zero help when there is no past data to predict the future. Unsupervised learning is helpful when there is no historical data. When you have to deal with huge transactional data you can use ANN or GMM unsupervised learning techniques. This is helpful in the sense it can explore surpassed data and can find structure in it. It is very useful in finding out customer segments and clusters with specific attributes which finds application in content personalization.

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Semi-supervised learning is a combination of both supervised and unsupervised learning and for training, it uses both labeled and unlabeled data. Most often the algorithm uses small chunks of labeled data with large chunks of unlabeled data. Classification, regression, and prediction models use this type of learning. Face and voice recognition techniques are its obvious applications. Mirroring traditional data analysis,reinforcement learning uses algorithms which discover profitable actions based on trial and error. There are three major components of reinforcement learning namely environment, agent and the actions. The agent is the decision maker, actions are what the agent can do and the environment includes everything that the agent interacts with.

Conclusion

You can consider machine learning to be statistics on steroids. You can literally build lot of trees with basic assumptions and aggregate them to come up with predictions with less bias. Machine learning is result oriented and focuses on final predictions rather than on asymptotic tests and underlying distributions which statistical methods use heavily. We on the other hand are focused on providing top notch training to you on emerging technologies. In our training, you get to deep dive in the field of machine learning and explore its child deep learning with TensorFlow model. Learn everything from data preprocessing to model tuning in our industry-relevant Machine Learning training.