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Exploring Neural Networks  By : Nikhil Gupta Introduction Having a solid grasp on deep learning techniques feels like acquiring a super power these days. From classifying images and translating languages to building a self-driving car, all these tasks are being driven by computers rather than manual human effort. Deep learning has penetrated into multiple and diverse industries, and it continues to break new ground on an almost weekly basis. What is a Neural Network? Let’s begin with the crux of the matter and a very critical question. What is a neural network? Consider an example where we have to predict the price of a house. The variables we are given are the size of the house in square feet (or square meters) and the price of the house. Now assume we have 6 houses. So first let’s pull up a plot to visualize what we’re looking at: On the x-axis, we have the size of the house and on the y-axis we have it’s corresponding price. A linear regression model will...
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Decision Trees For Classification : A ML Algorithm Introduction Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves. The leaves are the decisions or the final outcomes. And the decision nodes are where the data is split. An example of a decision tree can be explained using above binary tree. Let’s say you want to predict whether a person is fit given their information like age, eating habit, and physical activity, etc. The decision nodes here are questions like ‘What’s the age?’, ‘Does he exercise?’, ‘Does he eat a lot of pizzas’? And the leaves, which are outcomes like either ‘fit’, or ‘unfit’. In this case this was a binary classification problem (a yes no type problem). There are two main types of Decision Trees: Classific...
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Machine Learning Workflow  By : Nikhil Gupta                                                 The workflow process of  ML is a step wise process employed in order to make an ML project / Model. The diagram below gives a high-level overview of the stages in an ML workflow. The blue-filled boxes indicate where  ML Engine provides managed services and APIs: ML workflow To develop and manage a production-ready model, we must work through the following stages: Source and prepare your data. Develop your model. Train an ML model on your data: Train model Evaluate model accuracy Tune hyperparameters Deploy your trained model. Send prediction requests to your model: Online prediction Batch prediction Monitor the predictions on an ongoing basis. Manage your models and model versions. These stages are iterati...
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  Logistic Regression  By : Nikhil Gupta                                                                                                                                              Logistic regression is used to find the probability of event=Success and event=Failure. We should use logistic regression when the dependent variable is binary (0/ 1, True/ False, Yes/ No) in nature. Here the value of Y ranges from 0 to 1. In simple words, it predicts the probability of occurrence of an event by fitting data to a  logit function . Hence, it is also known as  logit regression . Since...
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