Meticlux

Here are some definitions for commonly used terms/technologies in machine learning. I’ll try to update and improve this page with new entries over time. Apache Spark — A library for distributed computing for large-scale data manipulation and machine learning. Artificial Neural Networks — Machine learning algorithms inspired by biological neural networks. Back-propagation — An algorithm for training neural networks in which errors are propagated backwards through the network. Big Data — Data which is difficult to work upon using a single machine, typically in the order of terabytes or more. It can also mean machine learning and other types of analyses on data of this scale. Classification — A machine learning problem involving the prediction of two or more classes from an observation. Clustering — The process of grouping observations that are similar according to a particular criterion. Cython — A Python-like language uses to give C-like performance to Python. Cross Validation — ...

“Learning is any process by which a system improves performance from experience” 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 is concerned with computer programs that automatically improve their performance through experience. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. Why Machine Learning ? •Develop Systems that can automatically adapt and customize themselves to individual users. •Discover Knowledge from Large Database (Data Mining) •Supporting many Industries. •Increasing Computational Power. Who’s using Machine Learning? In today’s everyone wants to switch to machine learning. Government Agencies Financial Services Health Care Industry Retails ( Website Recommending Items) Transportation Industry Defense & Security IT Companies

 Most important functions of Excel for data analysis Excel and google sheets are powerful tools for managing the raw data. There are many functions that can make your task easier.  As a Data Analyst, you use many functions like VLOOKUP, Pivot Tables, Conditional formatting, etc., So here, I have listed some most important functions of excel for data analysis : - 1. Pivot Table Pivot tables are a way to aggregate data to look at smaller subsets without using manual filters. Without it, you would use the manual filters on every column, check to see if they work, and then use [SUM] functions. All of that is built for you with a pivot table and pivot chart. You can set rows, columns, values and filters at the same at your level. You can use any aggregate function while creating a Pivot table.  In short, Pivot table gives you the summary of your data. We will see more detail in next blog. 2. VLOOKUP VLOOKUP stands for "Vertical Lookup", this means the act of looking up da...

Quiz 1 : Basics of Power BI and Data Analytics

  Modelling is a process to get your connected data for visualization. Basically, in modelling, you will connect to multiple data sources to create your reports.   Now the question is "How to connect multiple data sources to create a report?"  So, the answer is "You can create a relationship to create a logical connection between different data sources". A relationship enables Power BI to connect tables to one another so that you can create reports. How to Create a Data Relationship? The Model view in Power BI Desktop allows you to visually set the relationship between tables or elements. A relationship is where two or more tables are linked together with one or more join key(s) because they contain related data. In the Model view, notice that a block represents each table and the lines between them represent relationships. Adding and removing relationships is straightforward. To remove a relationship, right-click the relationship and select Delete . To create a r...

 There is a good news from Microsoft Power BI. Now you can use Power BI in Jupyter Notebook. You can get your Power BI analytics in a Jupyter notebook with the new powerbiclient Python package. Now you can embed Power BI reports, dashboards, dashboard tiles, etc., in Jupyter notebooks easily. You’ll be able to export data from visuals in a Power BI report to the Jupyter notebook for in-depth data exploration. You can also filter the report for quick analysis or use bookmarks to apply a saved view. You can install the Power BI Client for Jupyter from PyPI and find the open-sourced Python package and associate TypeScript widget on GitHub . Install the package using pip: pip install powerbiclient Open your notebook and add the following: Import Report class and models from the package : from powerbiclient import Report, models Authenticate against Power BI using Azure AD : # Import the DeviceCodeLoginAuthentication class to authenticate against Power BI from powerbiclient.authen...

  Excel or Google sheets are used by all organizations to keep the raw data, sales and many different type of records. A successful Excel spreadsheet will organize raw data into a readable format that makes it easier to extract actionable insights. With more complex data, Excel allows you to customize fields and functions that make calculations for you. Excel provide commands, functions and tools that make your data analysis tasks easy. You can avoid many time consuming and/or complex calculations using Excel. You can create graphs and can customize the records in an easier way. You can do automation using google sheets. You can write your own script to fetch data from Database. Many organizations use  Excel files to catalog data sets, import data, create data models, and more.