Jan
2018 Feb Tutorials, Overviews
All (102) | Courses, Education (3) | Meetings (19) | News, Features (11) | Opinions, Interviews (22) | Top Stories, Tweets (9) | Tutorials, Overviews (34) | Webcasts & Webinars (4)
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Introduction to Functional Programming in Python
- Feb 28, 2018.
Python facilitates different approaches to writing code, and while an object-oriented approach is common, an alternative and useful style of writing code is functional programming. -
Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms
- Feb 27, 2018.
We compare Gartner 2018 Magic Quadrant for Data Science, Machine Learning Platforms vs its 2017 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, Alteryx, H2O.ai, and Domino. - The Great Big Data Science Glossary
- Feb 27, 2018.
To help those new to the field stay on top of industry jargon and terminology, we've put together this glossary of data science terms.
- Applying Machine Learning to DevOps
- Feb 27, 2018.
This article explains the synergy between DevOps and Machine Learning and their applications like tracking application delivery, troubleshooting and triage analytics, preventing production failures, etc.
- A Tour of The Top 10 Algorithms for Machine Learning Newbies
- Feb 26, 2018.
For machine learning newbies who are eager to understand the basic of machine learning, here is a quick tour on the top 10 machine learning algorithms used by data scientists.
- Control Structures in R: Using If-Else Statements and Loops
- Feb 23, 2018.
Control structures allow you to specify the execution of your code. They are extremely useful if you want to run a piece of code multiple times, or if you want to run a piece a code if a certain condition is met.
- A Guide to Hiring Data Scientists
- Feb 22, 2018.
This article provides a short overview of emerging data scientist types and their unique skillsets, as well as a guide for HR professionals and analytics managers who are looking to hire their first data scientists or build a data science team. Included are an overview of skills for each type and specific questions that can be asked to assess candidates.
- 5 Fantastic Practical Natural Language Processing Resources
- Feb 22, 2018.
This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.
- A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018
- Feb 22, 2018.
In this article, we will compare the most commonly used platforms and analyze their main features to help you choose one or several platforms that will provide indispensable aid for your work communication.
- Recommender Engine - Under The Hood
- Feb 21, 2018.
We examine two main types of recommender systems: Content based and Collaborative filtering. Both have their pros and cons depending upon the context in which you want to use them.
- Top 20 Python AI and Machine Learning Open Source Projects
- Feb 20, 2018.
We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors.
- Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch
- Feb 20, 2018.
Now you can develop deep learning applications with Google Colaboratory - on the free Tesla K80 GPU - using Keras, Tensorflow and PyTorch.
- Applied Data Science: Solving a Predictive Maintenance Business Problem Part 2
- Feb 20, 2018.
In this post we will discuss further on how exploratory analysis can be used for getting insights for feature engineering.
- 5 Things You Need To Know About Data Science
- Feb 19, 2018.
Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists
- Want a Job in Data? Learn This
- Feb 19, 2018.
Why mastering a 50-year-old programming language is the key to getting a data science job.
- Logistic Regression: A Concise Technical Overview
- Feb 16, 2018.
Interested in learning the concepts behind Logistic Regression (LogR)? Looking for a concise introduction to LogR? This article is for you. Includes a Python implementation and links to an R script as well.
- Calculating Customer Lifetime Value: SQL Example
- Feb 15, 2018.
In order to understand how to estimate LTV, it is useful to first think about evaluating a customer’s lifetime value at the end of their relationship with us.
- Data Science at the Command Line: Exploring Data
- Feb 14, 2018.
See what's available in the freely-available book "Data Science at the Command Line" by digging into data exploration in the terminal.
- Building a Toy Detector with Tensorflow Object Detection API
- Feb 13, 2018.
This project is second phase of my popular project - Is Google Tensorflow Object Detection API the easiest way to implement image recognition? Here I extend the API to train on a new object that is not part of the COCO dataset.
- A Basic Recipe for Machine Learning
- Feb 13, 2018.
One of the gems that I felt needed to be written down from Ng's deep learning courses is his general recipe to approaching a deep learning algorithm/model.
- 3 Essential Google Colaboratory Tips & Tricks
- Feb 12, 2018.
Google Colaboratory is a promising machine learning research platform. Here are 3 tips to simplify its usage and facilitate using a GPU, installing libraries, and uploading data files.
- Introduction to Python Ensembles
- Feb 9, 2018.
In this post, we'll take you through the basics of ensembles — what they are and why they work so well — and provide a hands-on tutorial for building basic ensembles.
- Top 15 Scala Libraries for Data Science in 2018
- Feb 9, 2018.
For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.
- 5 Machine Learning Projects You Should Not Overlook
- Feb 8, 2018.
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
- Fast.ai Lesson 1 on Google Colab (Free GPU)
- Feb 8, 2018.
In this post, I will demonstrate how to use Google Colab for fastai. You can use GPU as a backend for free for 12 hours at a time. GPU compute for free? Are you kidding me?
- Building a Daily Bitcoin Price Tracker with Coindeskr and Shiny in R
- Feb 7, 2018.
This tutorial is to help an R user build his/her own Daily Bitcoin Price Tracker using three packages, Coindeskr, Shiny and Dygraphs.
- Deep Feature Synthesis: How Automated Feature Engineering Works
- Feb 7, 2018.
Automating feature engineering optimizes the process of building and deploying accurate machine learning models by handling necessary but tedious tasks so data scientists can focus more on other important steps.
- 5 Fantastic Practical Machine Learning Resources
- Feb 6, 2018.
This post presents 5 fantastic practical machine learning resources, covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks.
- A Simple Starter Guide to Build a Neural Network
- Feb 5, 2018.
This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out.
- Avoid Overfitting with Regularization
- Feb 2, 2018.
This article explains overfitting which is one of the reasons for poor predictions for unseen samples. Also, regularization technique based on regression is presented by simple steps to make it clear how to avoid overfitting.
- Web Scraping Tutorial with Python: Tips and Tricks
- Feb 1, 2018.
This post is intended for people who are interested to know about the common design patterns, pitfalls and rules related to the web scraping.
- The AGI/Deep Learning Connection
- Feb 1, 2018.
Also, deep learning would definitely prove to be an essential component to create truly intelligent machines but probably not enough alone.
- Understanding Learning Rates and How It Improves Performance in Deep Learning
- Feb 1, 2018.
Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy). Thus getting it right from the get go would mean lesser time for us to train the model.
- The 8 Neural Network Architectures Machine Learning Researchers Need to Learn
- Jan 31, 2018.
In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.