Starting Your Journey to Master Machine Learning with Python

What are these fields anyways? Artificial Intelligence is a vast field. The topics like Machine Learning, Data Science, statistics, natural language processing, all come under Artificial Intelligence. Deep Learning is a subset of Machine Learning. Artificial Intelligence is — “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such … Read more

How do you present your Data Science solution?

An example of how to prepare a report on Credit Risk Modelling via Machine Learning Photo by The New York Public Library on Unsplash Its weekend time to try something new in Machine learning and make this time worthwhile. It is important to document the machine learning approach and solution to share it with the … Read more

How to version control Jupyter Notebooks

Image used under license from Jupyter notebooks are fantastic in many ways but collaboration is not so easy with them. In this article we’ll look at all the tools you can leverage to make notebooks play nicely with modern version control systems like git! The software world has converged on git as it’s version … Read more

Easiest flowcharts eveR?

[This article was first published on R – G-Forge, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. A guide to flowcharts using my Gmisc package. The image is … Read more

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Pandas!!! What I’ve Learned after my 1st On-site Technical Interview

So after getting Product out of Product_Description, there is another challenge in your way! The interviewer asked if you could split up the Product column in 2 manners: Split the column into Product_1, Product_2, … Do one-hot encoding on each product Photo by Tom Grünbauer on Unsplash 1. Split this column into Product_n We can … Read more

Significant? You Really Mean Detectable

There was a significant decrease of size D in the outcome. photo by Anthony at Interpretations Common Misinterpretation: There was a scientifically or clinically significant, important, meaningful, or useful decrease of size D in the outcome. A Correct Interpretation*: There was significant statistical evidence that a true, unknown change (i.e., decrease/increase) in the mean … Read more

How BERT Determines Search Relevance

Understanding BERT’s limitations and biases will help you better understand how BERT and Search view the world and your content. On October 25, 2019, Pandu Nayak, VP of Search for Google announced: by applying BERT models to both ranking and featured snippets in Search, we’re able to do a much better job helping you find … Read more

When the Median is Favorable to the Mean

Choosing the more appropriate descriptive statistic Sneaker Order Prices The mean and the median are two of the most common features used when describing numerical data. The two are known as measures of central tendency, meaning they describe a set of data by shedding light on the central position of the data. The mean is … Read more

Building End-to-End Customer Segmentation Solution with Alibaba Cloud

Photo by Sharon McCutcheon on Unsplash Imagine we have a retail store selling various products. To be more successful in your business, we have to understand our customers well. Especially in today’s competitive world. So that we can answer: – Who are our best customers? – Who are our potential customers? – Which customers that … Read more

Global, Local and Nonlocal variables in Python

Same old definition In Python or any other programming languages, the definition of global variables remains the same, which is “A variable declared outside the function is called global function”. We can access a global variable inside or outside the function. Creating a global variable and accessing it Let’s use the same example from above … Read more

How to Colab with TPU

Training a Huggingface BERT on Google Colab TPU TPU Demo via Google Cloud Platform Blog TPUs (Tensor Processing Units) are application-specific integrated circuits (ASICs) that are optimized specifically for processing matrices. Cloud TPU resources accelerate the performance of linear algebra computation, which is used heavily in machine learning applications — Cloud TPU Documentation Google Colab … Read more

Forecasting the Copper Producer Price Index with Prophet

Using Prophet to forecast commodity prices Source: Image by papazachariasa from Pixabay Forecasting asset prices can be a tricky business at the best of times. For one, asset prices are subject to a high degree of stochasticity (or random influence), which makes generating future predictions difficult. With that being said, one useful feature of the … Read more

Essential Embeds for Medium’s Technical Writers

Anyone who’s worked with creating charts should know Plotly, a great service that provides interactive visualizations. Unfortunately, Medium does not support interactivity, but embedding Plotly charts directly displays it with pristine resolution and is not restricted by the size of the user’s screen. Additionally, if you decide to change the plot, you don’t need to … Read more

Why you should try the Bayesian approach of A/B testing

The intuitive way of A/B testing. The advantages of the Bayesian approach and how to do it. “Uncertainty” by is licensed under CC BY-SA 2.0 “Critical thinking is an active and ongoing process. It requires that we all think like Bayesians, updating our knowledge as new information comes in.”― Daniel J. Levitin, A Field … Read more

The Missing List of JupyterLab Keyboard Shortcuts

With keyboard shortcuts, you can whiz around Jupyter notebooks in JupyterLab. You can save time, reduce wrist fatigue from using your mouse, and impress your friends. 🙂 Below is the missing list of common JupyterLab keyboard shortcuts from a GitHub Gist I made. Enjoy! 🎉 Source: I write about Python, SQL, Docker, and other … Read more

Creating A Chess AI using Deep Learning

Using Neural Networks to decode the world’s oldest game… Photo by Hassan Pasha on Unsplash When Gary Kasparov was dethroned by IBM’s Deep Blue chess algorithm, the algorithm did not use Machine Learning, or at least in the way that we define Machine Learning today. This article aims to use Neural Networks to create a … Read more

Machine Learning and Plato’s Allegory of the Cave

How the current approach to machine learning fits in Plato’s famous allegory and whither we go from there. The allegory of the cave was presented by the Greek philosopher Plato in his work Republic, originally to compare “the effect of education and the lack of it on our nature”. Oddly enough, the state-of-the-art field of … Read more

Top 5 Statistical Concepts Every Data Scientist Must Know

A probability distribution is a function that gives the probability of occurrence for every possible outcome of an experiment. If you’re picturing a bell curve, you’re on the right track. It shows, at a glance, how the values of a random variable are dispersed. Random variables, and therefore distributions, can be either discrete or continuous. … Read more

NBA salaries

I came across a dataset of NBA player salaries from the 1984-1985 season to the 2017-2018 season here, and I thought it would be a fun dataset to practice my tidyverse skills on. All the code for this post can be found here. First, let’s import the tidyverse package, set the plotting theme, and read … Read more

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Grand Central Dispatch

[This article was first published on MeanMean, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. About a decade late, but I decided to give Grand Central Dispatch (GCD) … Read more

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The Vandermonde Determinant, A Novel Proof

Applications of the Vandermonde Matrix and An Original Proof of the Determinant Almost all students of linear algebra will learn about the Vandermonde matrix at some point throughout the course of their studies. This type of matrix has wide applications in math and science and it is quite accessible, which makes it a very useful … Read more

Building Machine Learning Pipelines

Setting Custom Selectors and Transformers in Pipelines Let’s go a step further and say we don’t even want to select the columns in advance. Instead, we need the pipeline to do the selection for us. Now, we introduce FeatureUnion- It concatenates the results of multiple transformations happening in parallel! Image by Author: Complex Pipeline Since … Read more

Deep Reinforcement Learning for Automated Stock Trading

Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details. This blog is based on our paper: Deep Reinforcement … Read more

Data Repositories for almost Every Type of Data Science Project

Given the nature of my job, I have to work on new projects every week solving a different problem. My work requires me to parse through a lot of different kinds of datasets to design and develop instructions for Data Science aspirants. The blog contains a few useful datasets and data repositories categorized in different … Read more

Neural Language Models as Domain-Specific Knowledge Bases

Natural Language Processing | Transformer Based Language Models | Domain Knowledge and Domain Ontology An analysis of domain knowledge carried by Transformer based Language Models The fundamental challenge of natural language processing (NLP) is resolution of the ambiguity that is present in the meaning of and intent carried by natural language. Ambiguity occurs at multiple … Read more

Five Books that Aspiring Data Scientists Should Read

Photo by Kimberly Farmer on Unsplash Data science is not just about mathematics, statistics, and coding. It is about telling a great story. Data science is not just about mathematics, statistics, and coding. It is about using these tools to generate new insights, and about telling a great story. When data analysis and great storytelling … Read more

Calculating Audio Song Similarity Using Siamese Neural Networks

At AI Music, where our back catalogue of content grows every day, it is becoming increasingly necessary for us to create more intelligent systems for searching and querying the music. One such system for doing that can be dictated by the ability to define and quantify the degree of similarity between songs. The core methodology … Read more

Have R Look After Your Stocks!

[This article was first published on R on Curious Joe, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. “If you don’t find a way to make money while … Read more

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Custom tick marks with R’s base graphics system

[This article was first published on R-bloggers on inSileco, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Context If you are using R’s base graphics system for your … Read more

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tsmp is going big!

Since the beginning of the tsmp package, it was evident that a series of algorithms around the Matrix Profile would pop-up sooner or later. After the creation of the Matrix Profile Foundation (MPF), the tsmp package had doubled the number of monthly downloads, and that is a good thing! The current version of tsmp, as … Read more

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The Relationship between Significance, Power, Sample Size & Effect Size

Congratulations, your experiment has yielded significant results! You can be sure (well, 95% sure) that the independent variable influenced your dependent variable. I guess all you have left to do is write up your discussion and submit your results to a scholarly journal. Right…………? Obtaining significant results is a tremendous accomplishment in itself self but … Read more

An Introduction to Golang

All you need to get started It’s time to learn Golang. [Image source] This post intends to be an introduction to the Go programming language, also known as Golang. I’m not an expert in Go. In fact, I’ve started learning about Go very recently. Therefore, take everything in this post with a pinch of salt. … Read more

What is Google API Vision? And how to use it

Extract text from image with OCR using a service account. Introduction This post finds his root in an interesting project of knowledge extraction. The first step was to extract the text of pdf documents. The company that I work for is based on the Google platform, so naturally, I would like to use the OCR … Read more

AWS announces General Availability of Amazon GameLift feature update

In April, we announced the release of this update to GameLift FleetIQ in preview. Configurations you set during preview will continue to work in GA. In addition to the preview feature set, we are also introducing new improvements for GameLift FleetIQ that enable you to use only On-Demand Instances and check game server instance statuses. As a … Read more

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PyCaret 2.1 is here — What’s new?

PyCaret 2.1 is now available for download using pip. We are excited to announce PyCaret 2.1 — update for the month of Aug 2020. PyCaret is an open-source, low-code machine learning library in Python that automates the machine learning workflow. It is an end-to-end machine learning and model management tool that speeds up the … Read more

Pause and Resume Workloads on M5a and R5a Instances with Amazon EC2 Hibernation

Hibernation saves effort in setting up the applications from scratch, saves time by reducing the bootstrapping time taken by applications, and saves cost by pausing the EC2 instances when not required. By using Hibernation, you can maintain a fleet of pre-warmed instances to get to a productive state faster without modifying your existing applications. Hibernation … Read more

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Detecting Security Vulnerabilities in R Packages

[This article was first published on r – Jumping Rivers, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. One of our main roles at Jumping Rivers is to … Read more

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Generating Piano Music with Dilated Convolutional Neural Networks

How to build fully convolutional neural networks that can model the complex structure of piano music with striking success Fully convolutional neural networks consisting of dilated 1D convolutions are straightforward to construct, easy to train, and can generate realistic piano music, such as the following: Example performance generated by a fully convolutional network trained on … Read more

An application modernization bonanza—What happened at Next OnAirAn application modernization bonanza—What happened at Next OnAirGlobal Product Marketing Lead, Application Modernization

Week seven of Google Cloud Next ‘20: OnAir was all about application modernization—of your existing workloads, and the ones you will build tomorrow.  We kicked things off with not one, not two, but three keynotes: the first, by Eyal Manor, GM & VP, Engineering; Pali Bhat, VP, Product & Design and Chen Goldberg, Engineering Director, … Read more

Better together: orchestrating your Data Fusion pipelines with Cloud ComposerBetter together: orchestrating your Data Fusion pipelines with Cloud ComposerStrategic Cloud Engineer

The data analytics world relies on ETL and ELT pipelines to derive meaningful insights from data. Data engineers and ETL developers are often required to build dozens of interdependent pipelines as part of their data platform, but orchestrating, managing, and monitoring all these pipelines can be quite a challenge. That’s why we’re pleased to announce … Read more

How to build an image automatic rotator in 24 hours

The simplicity of Neural Network and Keras’ tools Photo by Uriel SC on Unsplash Recently, I was challenged to do this task which basically asked to use neural networks to predict the image orientation (upright, upside down, left or right) and with that prediction rotate the image to the correct position (upright), all of this … Read more