2020 Jun Tutorials, Overviews
All (86) | Events (2) | News, Education (7) | Opinions (19) | Top Stories, Tweets (10) | Tutorials, Overviews (48)
- Software engineering fundamentals for Data Scientists - Jun 30, 2020.
As a data scientist writing code for your models, it's quite possible that your work will make its way into a production environment to be used by the masses. But, writing code that is deployed as software is much different than writing code for exploratory data analysis. Learn about the key approaches for making your code production-ready that will save you time and future headaches.
- How to Prepare Your Data - Jun 30, 2020.
This is an overview of structuring, cleaning, and enriching raw data.
- Learn Data Science from Top Universities for Free - Jun 29, 2020.
Where to find free lectures, seminars and complete courses from the likes of MIT, Stanford and Harvard.
- An Introduction to Statistical Learning: The Free eBook - Jun 29, 2020.
This week's free eBook is a classic of data science, An Introduction to Statistical Learning, with Applications in R. If interested in picking up elementary statistical learning concepts, and learning how to implement them in R, this book is for you.
- Practical Markov Chain Monte Carlo - Jun 26, 2020.
This is a slightly more intricate example of MCMC, compared to many with a fairly simple model, a single predictor (maybe two), and not much else, which highlights a couple of issues and tricks worth noting for a handwritten implementation.
- Free Economics & Finance Courses for Data Scientists - Jun 25, 2020.
Here is a selection of courses for those interested in diversifying their domain knowledge into the related realms of economics and finance, with the goal of being able to apply your data science skills to these domains.
- Build a Branded Web Based GIS Application Using R, Leaflet and Flexdashboard - Jun 24, 2020.
By using R, Flexdashboard and Leaflet, we can build a customized and branded web application to showcase location based data interactively across the organization. Instead of crowding the application with many widgets, we use menu tabs and pages to separate the interactive aspects.
- The 8 Basic Statistics Concepts for Data Science - Jun 24, 2020.
Understanding the fundamentals of statistics is a core capability for becoming a Data Scientist. Review these essential ideas that will be pervasive in your work and raise your expertise in the field.
- Time Complexity: How to measure the efficiency of algorithms - Jun 24, 2020.
When we consider the complexity of an algorithm, we shouldn’t really care about the exact number of operations that are performed; instead, we should care about how the number of operations relates to the problem size.
- A TensorFlow Modeling Pipeline Using TensorFlow Datasets and TensorBoard - Jun 23, 2020.
This article investigates TensorFlow components for building a toolset to make modeling evaluation more efficient. Specifically, TensorFlow Datasets (TFDS) and TensorBoard (TB) can be quite helpful in this task.
- Bias in AI: A Primer - Jun 23, 2020.
Those interested in studying AI bias, but who lack a starting point, would do well to check out this introductory set of slides and the accompanying talk on the subject from Google researcher Margaret Mitchell.
- Machine Learning in Dask - Jun 22, 2020.
In this piece, we’ll see how we can use Dask to work with large datasets on our local machines.
- 4 Free Math Courses to do and Level up your Data Science Skills - Jun 22, 2020.
Just as there is no Data Science without data, there's no science in data without mathematics. Strengthening your foundational skills in math will level you up as a data scientist that will enable you to perform with greater expertise.
- How to Deal with Missing Values in Your Dataset - Jun 22, 2020.
In this article, we are going to talk about how to identify and treat the missing values in the data step by step.
- Graph Machine Learning in Genomic Prediction - Jun 19, 2020.
This work explores how genetic relationships can be exploited alongside genomic information to predict genetic traits with the aid of graph machine learning algorithms.
- What is emotion AI and why should you care? - Jun 19, 2020.
What is emotion AI, why is it relevant, and what do you need to know about it?
- modelStudio and The Grammar of Interactive Explanatory Model Analysis - Jun 19, 2020.
modelStudio is an R package that automates the exploration of ML models and allows for interactive examination. It works in a model agnostic fashion, therefore is compatible with most of the ML frameworks.
- 6 Easy Steps to Implement a Computer Vision Application Using Tensorflow.js - Jun 18, 2020.
In this article, we are going to see how we can implement computer vision applications using tensorflow.js models.
- The Most Important Fundamentals of PyTorch you Should Know - Jun 18, 2020.
PyTorch is a constantly developing deep learning framework with many exciting additions and features. We review its basic elements and show an example of building a simple Deep Neural Network (DNN) step-by-step.
- LightGBM: A Highly-Efficient Gradient Boosting Decision Tree - Jun 18, 2020.
LightGBM is a histogram-based algorithm which places continuous values into discrete bins, which leads to faster training and more efficient memory usage. In this piece, we’ll explore LightGBM in depth.
- Build Dog Breeds Classifier Step By Step with AWS Sagemaker - Jun 17, 2020.
This post takes you through the basic steps for creating a cloud-based deep learning dog classifier, with everything accomplished from the AWS Management Console.
- A Classification Project in Machine Learning: a gentle step-by-step guide - Jun 17, 2020.
Classification is a core technique in the fields of data science and machine learning that is used to predict the categories to which data should belong. Follow this learning guide that demonstrates how to consider multiple classification models to predict data scrapped from the web.
- Crop Disease Detection Using Machine Learning and Computer Vision - Jun 17, 2020.
Computer vision has tremendous promise for improving crop monitoring at scale. We present our learnings from building such models for detecting stem and wheat rust in crops.
- Best Machine Learning Youtube Videos Under 10 Minutes - Jun 16, 2020.
The Youtube videos on this list cover concepts such as what machine learning is, the basics of natural language processing, how computer vision works, and machine learning in video games.
- A Complete guide to Google Colab for Deep Learning - Jun 16, 2020.
Google Colab is a widely popular cloud service for machine learning that features free access to GPU and TPU computing. Follow this detailed guide to help you get up and running fast to develop your next deep learning algorithms with Colab.
- Simplified Mixed Feature Type Preprocessing in Scikit-Learn with Pipelines - Jun 16, 2020.
There is a quick and easy way to perform preprocessing on mixed feature type data in Scikit-Learn, which can be integrated into your machine learning pipelines.
- Uber’s Ludwig is an Open Source Framework for Low-Code Machine Learning - Jun 15, 2020.
The new framework allow developers with minimum experience to create and train machine learning models.
- Free Data Analytics Courses - Jun 15, 2020.
Wherever your skills are at today, check out these top course recommendations for 2020 to help you master data analytics.
- Understanding Machine Learning: The Free eBook - Jun 15, 2020.
Time to get back to basics. This week we have a look at a book on foundational machine learning concepts, Understanding Machine Learning: From Theory to Algorithms.
- Deploy a Machine Learning Pipeline to the Cloud Using a Docker Container - Jun 12, 2020.
In this tutorial, we will use a previously-built machine learning pipeline and Flask app to demonstrate how to deploy a machine learning pipeline as a web app using the Microsoft Azure Web App Service.
- Five Cognitive Biases In Data Science (And how to avoid them) - Jun 12, 2020.
Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work. Learn more about five biases that can all too easily make your seemingly objective work become surprisingly subjective.
- Easy Speech-to-Text with Python - Jun 10, 2020.
In this blog, I am demonstrating how to convert speech to text using Python. This can be done with the help of the “Speech Recognition” API and “PyAudio” library.
- Overview of data distributions - Jun 10, 2020.
With so many types of data distributions to consider in data science, how do you choose the right one to model your data? This guide will overview the most important distributions you should be familiar with in your work.
- Centroid Initialization Methods for k-means Clustering, by Matthew Mayo - Jun 10, 2020.
This article is the first in a series of articles looking at the different aspects of k-means clustering, beginning with a discussion on centroid initialization.
- 5 Essential Papers on Sentiment Analysis - Jun 9, 2020.
To highlight some of the work being done in the field, here are five essential papers on sentiment analysis and sentiment classification.
- Naïve Bayes Algorithm: Everything you need to know - Jun 8, 2020.
Naïve Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks. In this article, we will understand the Naïve Bayes algorithm and all essential concepts so that there is no room for doubts in understanding.
- Natural Language Processing with Python: The Free eBook - Jun 8, 2020.
This free eBook is an introduction to natural language processing, and to NLTK, one of the most prevalent Python NLP libraries.
- Deep Learning for Detecting Pneumonia from X-ray Images - Jun 5, 2020.
This article covers an end to end pipeline for pneumonia detection from X-ray images.
- Machine Learning Experiment Tracking - Jun 4, 2020.
Why is experiment tracking so important for doing real world machine learning?
- 5 Essential Papers on AI Training Data - Jun 4, 2020.
Data pre-processing is not only the largest time sink for most Data Scientists, but it is also the most crucial aspect of the work. Learn more about training data and data processing tasks from 5 leading academic papers.
- Skills to Build for Data Engineering - Jun 4, 2020.
This article jumps into the latest skill set observations in the Data Engineering Job Market which could definitely add a boost to your existing career or assist you in starting off your Data Engineering journey.
- Introduction to Convolutional Neural Networks - Jun 3, 2020.
The article focuses on explaining key components in CNN and its implementation using Keras python library.
- From Languages to Information: Another Great NLP Course from Stanford - Jun 3, 2020.
Check out another example of a Stanford NLP course and its freely available courseware.
- Four Ways to Apply NLP in Financial Services - Jun 2, 2020.
Natural language processing (NLP) is increasingly used to review unstructured content or spot trends in markets. How is Refinitiv Labs applying NLP in financial services to meet challenges around investment decision-making and risk management?
- Docker: Containerization for Data Scientists - Jun 2, 2020.
This article is a simple explanation to containerization with Docker.
- Forecasting Stories 4: Time-series too, Causal too - Jun 1, 2020.
This article is about the story of taking effective business decisions basis a combined model. Let us together study how these components work hand in hand.
- Introduction to Pandas for Data Science - Jun 1, 2020.
The Pandas library is core to any Data Science work in Python. This introduction will walk you through the basics of data manipulating, and features many of Pandas important features.
- Deep Learning for Coders with fastai and PyTorch: The Free eBook - Jun 1, 2020.
If you are interested in a top-down, example-driven book on deep learning, check out the draft of the upcoming Deep Learning for Coders with fastai & PyTorch from fast.ai team.