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Analyzing Financial Time Series Using BigQuery and Cloud Datalab

This solution illustrates the power and utility of BigQuery and Cloud Datalab as tools for quantitative analysis. The solution provides an introduction (this document) and gets you set up to run a notebook-based Cloud Datalab tutorial. If you’re a quantitative analyst, you use a … Continue reading Analyzing Financial Time Series Using BigQuery and Cloud Datalab

Running Spark on Azure Databricks

Who Should See This Blog Post? This course is intended for the people who wants to Run their Analytics workload and Machine Learning workload on DataBricks And chosen Azure as their distributor. Skills Required To Follow: SQL Machine Learning Why we need DataBricks and Azure services ….. well we can train one model on DataBricks and deploy it on Azure Services Before we can run a spark job we needs to create a computer cluster and before we create a computer cluster we need to create a DataBricks Workspace.All the work done in DataBricks need not to be done within … Continue reading Running Spark on Azure Databricks

A Practical Approach To Stochastic gradient descent(SGD)

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It is called stochastic because the method uses randomly selected (or shuffled) samples to evaluate the gradients, hence SGD can be regarded as a stochastic approximation of gradient descent optimization. Background Both statistical estimation and machine learning consider the … Continue reading A Practical Approach To Stochastic gradient descent(SGD)

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Machine Learning Applications for Big Data(Regression):

Machine Learning: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it … Continue reading Machine Learning Applications for Big Data(Regression):

Practical Aspects Of Testing and Debugging in Machine Learning

This post tries to give you a sense of humor on how to work with debugging when you are calling yourself a ML engineer or Data scientist. This post reduces your task as a ML Engineer and gives you the ability to implement the understanding of basic life’s rules which is go slow , grow more. What We Are Discussing Here? Once you had finished of framing your idea and wasted 3-4 months trying copy paste other’s work to gain your reputation in your organization when everything fails as your organazation has a unique collection of data though organization is … Continue reading Practical Aspects Of Testing and Debugging in Machine Learning

Fine grained analysis of K- mean clustering and where we are using it

K-means is a centroid based algorithm that means points are grouped in a cluster according to the distance(mostly Euclidean) from centroid. Centroid-based Clustering Centroid-based clustering organizes the data into non-hierarchical clusters, in contrast to hierarchical clustering defined below. k-means is the … Continue reading Fine grained analysis of K- mean clustering and where we are using it

In Depth Clustering Analysis

Clustering is the Unsupervised version of classification if we have labeled data then we will get classification when we grouped same labeled data . And if we don’t have the labels we will use features of the vectors to identify the same data points and group them with same properties these is what clustering is . let’s tell you that by an example suppose assume that you have two different scenario in two different life you are living in two parallel Universe. In the first world you know who your mother is and you love the food your mother cooks … Continue reading In Depth Clustering Analysis