What is Machine Learning and How to learn Machine Learning ?

Machine Learning

Importance of Machine Learning

Typically machine learning is used to simplify things. The goal of machine learning is to make predictions that are useful. For a company with several products, the internal search working of the website should be able to effectively push the relevant products to the search engine results page(SERP) based on user queries. User intent and retrieving relevant products to recommend to the user plays a significant role in optimizing the user experience as well as directing the user to the products.

google play store

Training model-Gradient descent

With the intent of searching as the x-axis on a graph and products as the y-axis, several data points will be picked to get the needed product that has to be pushed on to SERP for recommended products. By mapping the data points of intent of user search and products from highest to lowest, they are bound to meet at some point where the user intent collides with the product. The data point at this collision does not necessarily be the optimal valued product that has to be displayed. This model is more like someone searching for an oasis in a desert, where the chances of finding water might depend on luck. Similarly the results from this machine learning model are unpredictable. They may pick the best product or mediocre product , or sometimes even irrelevant product to be brought on to the search results page which makes the gradient descent model very unreliable for our purposes.

More about Machine Learining

For a search engine to get optimal user satisfaction and improved product publicity, a machine learning model needs to be played out. A perfect machine learning model would find the optimal balance between the user intent of search and products to be recommended. This model should be able to get the straight line of y=mx+c between user intent and products and only then will the search engine be able to project the products that are very relevant to users and gain the attention of users by directing them in required direction.

To get a better understanding of generating the optimal machine learning model that extracts relevant products to search engines based on user intent, let's look through different models of machine learning.

Linear Model: This model is straightforward and works based on a linear combination of features. Training data is used to understand the relationships between different data components and these relationships will be used to predict the data. As the model itself is very simple, it is very easy to understand as well as understand the data. It is also less prone to unwanted data. But for the model to achieve near perfection results, it needs to process large amounts of training data to completely understand the data relationships. This model is typically used for credit risk scoring, forecasts of sales, estimates for price changes of products etc., Google play also uses this model for pushing out the apps to be displayed based on user behavior.

For a real world machine learning model several components are needed to make it more ideal and usable, Most important components are Configurations, Data collection, Feature extraction, Data verification, Machine resource management, Analysis tools, Process management tools, Serving infrastructure, Monitoring

Constant monitoring is needed for machine learning models once the model is launched to verify its results and also to make changes as necessary. For a model which is based on user intent of search and product, the user picking the suggested product on the search engine’s results page is a confirmation that the prediction of the machine learning model is accurate.

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