Go gaga over Machine Learning

Data science team at Dell EMC uses a methodology called DEPP that guides the collaboration with the business stakeholders through the following stages: Descriptive Analytics to clearly understand what happened and how the business is measuring success. Exploratory Analytics to understand the financial, business and operational drivers behind what happened. Predictive Analytics to transition the business stakeholder mindset to focus on predicting what is likely to happen. Prescriptive Analytics to identify actions or recommendations based upon the measures of business success and the Predictive Analytics. The DEPP Methodology is an agile and iterative process that continues to evolve in scope and complexity as the clients mature in their advanced analytics capabilities Read more at: Lessons in Becoming an Effective Data Scientist I was recently a guest lecturer at the University of California Berkeley Extension in San Francisco. On a lovely Saturday afternoon, the...

I often wonder when I see people completing certificates on ML, are these more for showing the world that you know and understand ML or is it really a sense of achieving something! If it is the latter then why pay ? and why need a certificate at all? Let me elaborate on the resources that are free and can do a better job than most paid ML courses: Googles free website to choose videos, docs,tutorials, courses , sample code and Interactive demos -  https://ai.google/education/#?modal_active=none Have you read this? Triskelion one of the leaders on Kaggle explains how he went from a  beginner and finished up as a master with a top 10 finish -  https://mlwave.com/reflecting-back-on-one-year-of-kaggle-contests/  or  https://machinelearningmastery.com/master-kaggle-by-competing-consistently/ Kaggles Learning modules that are amazing  Machine Learning -  https://www.kaggle.com/learn/machine-learning R -  https://www.kaggle.co...

This one is definitely something every person who codes should do Track GitHub trending repositories in your favorite programming language by native GitHub notifications! vitalets/github-trending-repos github-trending-repos - Track GitHub trending repositories in your favorite programming language by native GitHub notifications!

The best ML learning path that helped me: Each subject does not require a whole day to be able to understand it fully, and you can do multiple of these in a day. Each day I take one subject from the list below, read it cover to cover, take notes, do the exercises and write an implementation in Python or R. Table of Contents What is it? Why use it? How to use it Don't feel you aren't smart enough Machine learning overview Machine learning mastery Machine learning is fun Inky Machine Learning Machine Learning: An In-Depth Guide Stories and experiences Machine Learning Algorithms Beginner Books Practical Books Kaggle knowledge competitions Video Series MOOC Resources Becoming an Open Source Contributor Games Podcasts Communities Conferences Interview Questions Source:  https://github.com/ZuzooVn/machine-learning-for-software-engineers Here is my post on Kaggle: https://www.kaggle.com/getting-started/48594#post275859

Source:  https://www.kdnuggets.com/2018/01/mlaas-amazon-microsoft-azure-google-cloud-ai.html

Based on the testing pyramid by Martin Fowler , I decided we need something similar that gives us a reference to all possible tools for each layer of the pyramid. and a similar one for mobile devices and Non Functional testing. Here is a curated list of all possible testing tools classified by the pyramid as a reference: https://github.com/AdyKalra/TestingToolsviaTestPyramid TestingToolsviaTestPyramid List of all testing tools in every programming language , based on the Testing Pyramid Let's finish this list! Click on each slice of the pyramid to see what tools do we have in that space Automated GUI Tests Automated API Tests Automated Integration Tests Automated Component Tests Automated Unit Tests Automated Database Tests Framework - Reporting / CI / LiveDoc / Code Analysis Agnostic Frameworks Reporting Documentation Code Analysis Continuous Integration Non Functional Testing - Performance / Security NFR Mobile Real Device Tests Si...

Here is a curated list of static analysis tools, linters and code quality checkers for various programming languages: https://github.com/mre/awesome-static-analysis C# Static code Analysis tools .NET Analyzers  - An organization for the development of analyzers (diagnostics and code fixes) using the .NET Compiler Platform. Code Analysis Rule Collection  - Contains a set of diagnostics, code fixes and refactorings built on the Microsoft .NET Compiler Platform "Roslyn". code-cracker  - An analyzer library for C# and VB that uses Roslyn to produce refactorings, code analysis, and other niceties. CSharpEssentials  - C# Essentials is a collection of Roslyn diagnostic analyzers, code fixes and refactorings that make it easy to work with C# 6 language features. Designite  ©️ - Designite is a software design quality assessment tool. It supports detection of implementation and design smells, compu...