Machine Learning In Python

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The right — and wrong — way to compete with a locked market





“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI (Artificial Intelligence) will transform in the next several years.”

~Andrew Ng
Why Machine Learning Is Important?


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f Machine Learning, Mobile Development, Software Engineering etc are different arts of sword fighting, competitive is the blade of your sword.


Not only has roughly 90 percent of the data created in the last two years, but current data output is 2.5 quintillion bytes of data daily.Only equipped with machine learning tools, we can process this amount of data and gain meaningful insights.Also through data driven, companies make better decisions with better financial gain. Machine learning is based on the idea that machines can learn from data, identify patterns and make decisions. It important because machine learning models are able to independently adapt and have ability to automatically apply complex mathematical calculations to huge amount of data multiple no of times and much faster with iterations of development.

Supervised learning These algorithms are trained using labeled data, such as an input where the desired output is known. For example, a set of experiments could have observations labeled either “Success” or “Failure”. The learning algorithm receives a set of inputs along with the corresponding correct outputs, and it learns by comparing its own output with correct outputs to find errors. It then modifies the model accordingly by reducing the error.



Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate how much a team would score in a match or what would be the price of any product at any given particular time in future.

Unsupervised learning It is used against data that has no predefined labels. The learning algorithm is not told the "right answer." The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other.Clustering is the task of gathering samples into groups of similar samples according to some predefined similarity or dissimilarity measure.




Popular techniques include self-organizing maps, nearest-neighbour mapping and k-means clustering. These algorithms are also used to segment text topics, recommend items and identify data outliers.