Go gaga over Machine Learning

NBN using machine learning to proactively tackle network faults NBN says it will employ machine learning to help it analyse the 38 terabytes of network health and performance data it gathers every day in order to take a proactive approach to mitigating faults across the collection of broadband technologies it is rolling out across Australia.

People Who Have "Too Many Interests" Are More Likely To Be Successful According To Research In order to become an effective online writer, I've deliberately combined academic research, digital journalism, and growth hacking into one skillset. I didn't go to college for any of these skills, but practiced them over time and received coaching on them.

Awesome simple principles to follow: 5 Tips To Deal With Untested Code - Hacker Noon As software testers, we deal every day with a lot of untested (or insufficiently tested) code. You might say "that's a tester's role, to find bugs in the code, is not the developer's role" and I wouldn't say you're not right. But, you are partially right, though.

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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...

One of the good articles I read in a long time, that makes complete sense in today's agile environment:  Architecturally Aligned Testing Testing microservices should not be done in a separate test phase, by a dedicated test team, but instead collaboratively by cross-functional teams. There is a shift left in testing to ensure that teams stay autonomous and a shift right in testing towards exploration and experimentation.

A Diagram Editor for JupyterLab - Jupyter Blog The new JupyterLab interface is much more than a replacement for the classic notebook. It aims to bring together all the pieces required for a complete scientific workflow. The extension-based architecture of JupyterLab comes with a number of components already enabled: a Jupyter notebook, a text editor, a file browser in the sidebar, a number of editors and viewers for various file formats, and much more.

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!

Here is an interesting illustration that might help you to choose your ML algorithm.  Source: scikit-learn.org

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

Good to see there are smart people having a futuristic vision in this space, though 2030 sounds far! New Australian innovation roadmap calls for focus on AI, machine learning Data science and artificial intelligence (AI) represent a significant economic opportunity for the Australian economy, according to a new report released by Innovation and Science Australia (ISA). ISA was tasked by the federal government with developing a strategic plan for the Australian innovation, science and research system out to 2030.

What is a Kernel? Kernels contain both the code that is needed for analysis and the analysis itself! "Kaggle Kernels is a cloud computational environment that enables reproducible and collaborative analysis. Kernels supports scripts in R and Python, Jupyter Notebooks, and RMarkdown reports. Go to the Kernels tab to view all of the publicly shared code on this competition. For more on how to use Kernels to learn data science, visit the Tutorials tab . " There are two types of Kernels on Kaggle: Notebook Scripts Script or notebook depends on what you are trying to achieve, If you were me then start with a notebook and move to a Script!

I recently gave a talk on ML and that has inspired me to tread down the path and explore more. I can't call it a recent post but it is a recent post  from  Jason Brownlee   that I read which has given me better direction to add some seriousness to this field Some key takeaway on how to start mine/your ML journey: DO NOT go through ML tutorials , Get onto hands-on realtime problems  Get onto Kaggle Follow the likes of Triskelion  Reproduce what others have done, DO NOT reinvent the wheel What other tools ? Vowpal Wabbit Scikit-learn R or Weka or Python or DO NOT have a weapon of choice "Competing consistently is the key to getting good." New to Data Science? Get started with a tutorial on our most popular competition for beginners, Titanic: Machine Learning from Disaster . Machine learning is the hottest field in data science, and this track will get you started quickly.  https://www.kaggle.com/learn/machine-learning Practice a lot : ...

Okay, I have been asked by a lot of people and if you follow my blog you’re probably wondering why did I change the name of my blog right?  Well, if you are not living under a rock you've probably heard about "Machine Learning" and how it's changing everything we do. Therefore the new name "Go gaga over Testing   Machine Learning" , all the posts going ahead will be very much focused in this direction. There is another reason "I love Alan Turing" :) Machine learning applications have the potential to disrupt industries, take pioneers miles ahead of competitors, and even create new revenue channels. Because cloud-based analytics solutions have become affordable for startups, we already have hundreds of business success stories that have been written using the power of machine learning. I haven’t officially changed the URL, so you will still type in go-gaga-over-testing.blogspot.com to access my site, but the name of the blog, logo, ...

The best time that I spend on my way to work reading blogs and sharing information, the byproduct a few must reads for almost anyone who wants an answer to where is technology going in the near future: IT goals for 2018: Five CIO must-dos For me, 2017 was a good year. We got a lot done. And the situation in the world -- everything moving at a faster... pace, constant change, new types of security threats, new technologies, new vendors, acquisitions, the on-going IT skills shortage and so on -- made it easier for me to make the case that we urgently needed to overhaul our organizational culture, processes and technologies. Top 10 blog posts of 2017 illuminate top CIO goals SearchCIO's most popular blog posts of 2017 point to a set of lofty -- and mandatory -- CIO goals: artificial intelligence,... digital transformation, multicloud management. IT leaders are learning all they can about these tech trends. The aim? To help their companies gain business advantage -- b...

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...

One last blog post before the year ends and we start a new beginning in the testing world, this article sums up where testing is headed to! Complexity, testing and its impact on innovation in 2018 Commercial industry has been fundamentally changed by digital technology. The most revolutionary organisations, like Uber and Netflix have shifted the goalposts, moving entire businesses online and introducing accessible, engaging and immersive consumer experiences. And, while tech innovation has historically happened at a discrete product or operational level, today it's taking place across an entire business - and beyond.