Feb
2017 Mar Tutorials, Overviews
http likes 637All (117) | Courses, Education (9) | Meetings (17) | News, Features (21) | Opinions, Interviews (27) | Software (4) | Tutorials, Overviews (35) | Webcasts & Webinars (4)
- Medical Image Analysis with Deep Learning
- Apr 6, 2017.
In this article, I start with basics of image processing, basics of medical image format data and visualize some medical data.
- A Short Guide to Navigating the Jupyter Ecosystem
- Mar 31, 2017.
This post presents a no-nonsense overview of the Jupyter ecosystem, and a few tips, tricks and concepts you may find useful for navigating it.
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The Best R Packages for Machine Learning
- Mar 30, 2017.
There is no doubt R is language of choice for the majority of data scientists who want to understand data, especially those looking to leverage its great machine learning packages. - A Beginner’s Guide to Tweet Analytics with Pandas
- Mar 29, 2017.
Unlike a lot of other tutorials which often pull from the real-time Twitter API, we will be using the downloadable Twitter Analytics data, and most of what we do will be done in Pandas.
- Deep Learning, Generative Adversarial Networks & Boxing – Toward a Fundamental Understanding
- Mar 28, 2017.
In this post we will see why GANs have so much potential, and frame GANs as a boxing match between two opponents.
- The Next Challenges for Reinforcement Learning
- Mar 28, 2017.
Despite the recent success of RL, there is still a lot of work to be done before it will become a mainstream technique. In this blog-post, we look at some of the remaining challenges that are currently being studied.
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What is Structural Equation Modeling?
- Mar 27, 2017.
Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. What is its relevance to Marketing Research? - Getting Started with Deep Learning
- Mar 24, 2017.
This post approaches getting started with deep learning from a framework perspective. Gain a quick overview and comparison of available tools for implementing neural networks to help choose what's right for you.
- Unsupervised Investments: A Comprehensive Guide to AI Investors
- Mar 24, 2017.
This article presents a list of 80 funds investing in Artificial Intelligence and Machine Learning.
- What Is Data Science, and What Does a Data Scientist Do?
- Mar 23, 2017.
This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose, and given that the ideal experience and skill set is relatively rare to find in one individual.
- Statistical Modeling: A Primer
- Mar 21, 2017.
It's critical to understand that statistical models are simplified representations of reality and they're all wrong but some of them are useful. So why do we use statistical models?
- Getting Up Close and Personal with Algorithms
- Mar 21, 2017.
We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. Read to learn more about Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.
- Analytics 101: Comparing KPIs
- Mar 20, 2017.
Different business units in the organisation have different behaviours (e.g. turnover rate) and they can’t be compared with each other. So, how can we tell whether the changes in their behaviour are reasons for concern?
- The Most Underutilized Function in SQL
- Mar 20, 2017.
Find out why md5() is an SQL function that's used surprisingly often, and find out how -- and why -- you can use it yourself.
- Email Spam Filtering: An Implementation with Python and Scikit-learn
- Mar 17, 2017.
This post is an overview of a spam filtering implementation using Python and Scikit-learn. The results of 2 classifiers are contrasted and compared: multinomial Naive Bayes and support vector machines.
- Applying Machine Learning To March Madness
- Mar 16, 2017.
March Madness is upon us. But before you get your brackets set, check out this overview of using machine learning to do the heavy lifting for you. A great discussion, and a timely topic.
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50 Companies Leading The AI Revolution, Detailed
- Mar 16, 2017.
We detail 50 companies leading the Artificial Intelligence revolution in AD Sales, CRM, Autotech, Business Intelligence and analytics, Commerce, Conversational AI/Bots, Core AI, Cyber-Security, Fintech, Healthcare, IoT, Vision, and other areas. - 17 More Must-Know Data Science Interview Questions and Answers, Part 3
- Mar 15, 2017.
The third and final part of 17 new must-know Data Science interview questions and answers covers A/B testing, data visualization, Twitter influence evaluation, and Big Data quality.
- Homebrewed Deep Learning and Do-It-Yourself Robotics
- Mar 14, 2017.
Learn how to experiment with embodied robotic cognition with IBM Project Intu, a platform that extends Deep Learning and other cognitive services to new devices with minimum coding.
- Open Source Toolkits for Speech Recognition
- Mar 14, 2017.
This article reviews the main options for free speech recognition toolkits that use traditional Hidden Markov Models and n-gram language models.
- Toward Increased k-means Clustering Efficiency with the Naive Sharding Centroid Initialization Method
- Mar 13, 2017.
What if a simple, deterministic approach which did not rely on randomization could be used for centroid initialization? Naive sharding is such a method, and its time-saving and efficient results, though preliminary, are promising.
- Working With Numpy Matrices: A Handy First Reference
- Mar 10, 2017.
This introductory tutorial does a great job of outlining the most common Numpy array creation and manipulation functionality. A good post to keep handy while taking your first steps in Numpy, or to use as a handy reminder.
- Visualizing Time-Series Change
- Mar 9, 2017.
When creating time-series line charts, it’s important to consider which of the following messages you would like to communicate: Actual value of units? Change in absolute units? Percent change? Change from a specific point in time?
- Beginner’s Guide to Customer Segmentation
- Mar 9, 2017.
At the core of customer segmentation is being able to identify different types of customers and then figure out ways to find more of those individuals so you can... you guessed it, get more customers!
- What makes a good data visualization – a Data Scientist perspective
- Mar 8, 2017.
We examine principles of good data visualization, including some great and terrible examples, guidelines for human perception, focus on key variables, changes and trends, avoiding chart junk, and more.
- The Challenges of Building a Predictive Churn Model
- Mar 8, 2017.
Unlike other data science problems, there is no one method for predicting which customers are likely to churn in the next month. Here we review the most popular approaches.
- Building Regression Models in R using Support Vector Regression
- Mar 8, 2017.
The article studies the advantage of Support Vector Regression (SVR) over Simple Linear Regression (SLR) models for predicting real values, using the same basic idea as Support Vector Machines (SVM) use for classification.
- Neuroscience for Data Scientists: Understanding Human Behaviour
- Mar 8, 2017.
Neuroscience is very complex and advanced study of brain and people often misuse this term. Here we try to explain neuroscience terminologies and use of data science for such studies.
- K-Means & Other Clustering Algorithms: A Quick Intro with Python
- Mar 8, 2017.
In this intro cluster analysis tutorial, we'll check out a few algorithms in Python so you can get a basic understanding of the fundamentals of clustering on a real dataset.
- A Simple XGBoost Tutorial Using the Iris Dataset
- Mar 7, 2017.
This is an overview of the XGBoost machine learning algorithm, which is fast and shows good results. This example uses multiclass prediction with the Iris dataset from Scikit-learn.
- Bokeh Cheat Sheet: Data Visualization in Python
- Mar 3, 2017.
Bokeh is the Python data visualization library that enables high-performance visual presentation of large datasets in modern web browsers. The package is flexible and offers lots of possibilities to visualize your data in a compelling way, but can be overwhelming.
- Every Intro to Data Science Course on the Internet, Ranked
- Mar 2, 2017.
For this guide, I spent 10+ hours trying to identify every online intro to data science course offered as of January 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings.
- Building a Bot to Answer FAQs: Predicting Text Similarity
- Mar 2, 2017.
In this post, learn to build a bot to answer frequently asked questions, reducing lag time for more customers and taking the load off of engineers, ensuring they can concentrate on building products!
- What is Customer Churn Modeling? Why is it valuable?
- Mar 1, 2017.
Getting new customers is much more more expensive than retaining existing ones, so reducing churn is a top priority for many firms. Understanding why customers churn and estimating the risks are powerful components of a data-driven retention strategy.
- 7 More Steps to Mastering Machine Learning With Python
- Mar 1, 2017.
This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
GitHub Python Data Science Spotlight: AutoML, NLP, Visualization, ML Workflows