Azure Machine Learning Services : Automated Machine Learning -Part 2

In the last post, what is Azure ML Studio and Azure ML Services has been explained briefly. In this post, I will explain how to use Automated machine learning service. Automated machine learning (automated ML) picks an algorithm and hyperparameters for you and generates a model ready for deployment. Automated machine learning helps to identify Read more about Azure Machine Learning Services : Automated Machine Learning -Part 2[…]

Azure ML Part 6: A Machine Learning Prediction scenario (3)

In previous posts (Part 4 and Part 5), I have explained some of the main components of Azure ML via a prediction scenario. In post one the process of data cleaning (using SQL Transformation, Cleaning Missing Value, Select specific Columns, and Edit Meta Data)  has been explained. and in the second Post, I have explained how to apply Read more about Azure ML Part 6: A Machine Learning Prediction scenario (3)[…]

K-mean clustering In R, writing R codes inside Power BI: Part 6

In the previous post,I have explained the main concepts and process behind the K-mean clustering algorithm. Now I am going to use this algorithm for classifying my Fitbit data in power BI.   as I have explained in part 5, I gathered theses data from Fitbit application and I am going to cluster them using Read more about K-mean clustering In R, writing R codes inside Power BI: Part 6[…]

Azure Machine Learning Part 3: Data Transformation in R

In previous post I have explained how to import data into Azure ML environment. In this part,I will show how to do data cleaning, data transformation in Azure ML environment. The second step in machine learning process is bout collecting (Part 2), cleaning and loading data (current part). Azure ML has different components for data Read more about Azure Machine Learning Part 3: Data Transformation in R[…]

Azure Machine Learning Part 2: Azure ML Environment

In the previous post, the main concepts of Machine Learning has been explained very briefly. However, if we want to talk about Machine Learning, it needs to read a whole book. In the second part, I am going to show the main Azure ML environment and its essential components. Azure ML has two type of Read more about Azure Machine Learning Part 2: Azure ML Environment[…]

Azure Machine Learning Part 1: Introduction

In this series, I will talk about Microsoft cloud machine learning: Azure ML. I will explain the main components and concepts of Azure ML. In the first post, I will talk about the Machine Learning concepts and Azure ML. What is Machine Learning: Machine learning according to Wikipedia is: “subfield of computer science that gives Read more about Azure Machine Learning Part 1: Introduction[…]

Prediction via KNN (K Nearest Neighbours) KNN Power BI: Part 3

K Nearest Neighbour (KNN ) is one of those algorithms that are very easy to understand and it has a good level of accuracy in practice. In Part One of this series, I have explained the KNN concepts. In Part 2 I have explained the R code for KNN, how to write R code and how Read more about Prediction via KNN (K Nearest Neighbours) KNN Power BI: Part 3[…]

Make Business Decisions: Market Basket Analysis Part 1

Market Basket analysis (Associative rules), has been used for finding the purchasing customer behavior in shop stores to show the related item that have been sold together. This approach is not just used for marketing related products, but also for finding rules in health care, policies, events management and so forth. In this Post I will Read more about Make Business Decisions: Market Basket Analysis Part 1[…]

A Glance At My Speaking Life (In a Year)

This year was an active year in my speaking life, In almost a year I’ve done below speakings: One SQL Rally Session (Denmark, Nordic) One Microsoft Ignite (New Zealand) Five SQL Saturdays (Austria, USA, New Zealand, Australia) Bunch of User Group and Codecamp speakings I started my peaking life with talking about MS data mining Read more about A Glance At My Speaking Life (In a Year)[…]

DMX with .Net-Part 1

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Predictions always matter; it is always nice to find a pattern in existing data. It will help to have a more accurate decision-making. These days, I am busy with designing and implementing a prototype for tourist recommendation system. I need to use variety of data mining algorithm such as “clustering” “Decision Tree”, “Regression”, “Neural Network”. All of these algorithms accept inputs with different data types. These inputs are the main factors that will affect the predictions.

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