2016 Jul Tutorials, Overviews
All (112) | Courses, Education (13) | Meetings (8) | News, Features (17) | Opinions, Interviews, Reports (31) | Software (8) | Tutorials, Overviews (31) | Webcasts & Webinars (4)
- Getting Started with Data Science – Python - Aug 1, 2016.
A great introductory post from DataRobot on getting started with data science in the Python ecosystem, including cleaning data and performing predictive modeling.
- Deep Learning For Chatbots, Part 2 – Implementing A Retrieval-Based Model In TensorFlow - Jul 29, 2016.
Check out part 2 of this tutorial on building chatbots with deep neural networks. This part gets practical, and using Python and TensorFlow to implement.
- Data Science Statistics 101 - Jul 28, 2016.
Statistics can often be the most intimidating aspect of data science for aspiring data scientists to learn. Gain some personal perspective from someone who has traveled the path.
- Data Science for Beginners 2: Is your data ready? - Jul 28, 2016.
This second video and write-up in the Data Science for Beginners series discusses what is required of your data before it can be useful.
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 3 - Jul 27, 2016.
This is the final part of a 3 part introductory series on machine learning in Python, using the Titanic dataset.
- 7 Steps to Understanding NoSQL Databases - Jul 27, 2016.
Are you a newcomer to NoSQL, interested in gaining a real understanding of the technologies and architectures it includes? This post is for you.
- Internet of Things Key Terms, Explained - Jul 27, 2016.
This post will define 12 Key Terms for the Internet of Things, in straightforward manner.
- The Fallacy of Seeing Patterns - Jul 26, 2016.
Analysts are often on the lookout for patterns, often relying on spurious patterns. This post looks at some spurious patterns in univariate, bivariate & multivariate analysis.
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 2 - Jul 26, 2016.
This is part 2 of a 3 part introductory series on machine learning in Python, using the Titanic dataset.
- Data Science for Beginners 1: The 5 questions data science answers - Jul 26, 2016.
A series of videos and write-ups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 1 - Jul 25, 2016.
Check out the first of a 3 part introductory series on machine learning in Python, fueled by the Titanic dataset. This is a great place to start for a machine learning newcomer.
- 35 Open Source tools for Internet of Things - Jul 25, 2016.
If you have heard about the Internet of Things many times by now, its time to join the conversation. Explore the many open source tools & projects related to Internet of Things.
- Building a Data Science Portfolio: Machine Learning Project Part 3 - Jul 22, 2016.
The final installment of this comprehensive overview on building an end-to-end data science portfolio project focuses on bringing it all together, and concludes the project quite nicely.
- Building a Data Science Portfolio: Machine Learning Project Part 2 - Jul 21, 2016.
The second part of this comprehensive overview on building an end-to-end data science portfolio project concentrates on data exploration and preparation.
- Building a Data Science Portfolio: Machine Learning Project Part 1 - Jul 20, 2016.
Dataquest's founder has put together a fantastic resource on building a data science portfolio. This first of three parts lays the groundwork, with subsequent posts over the following 2 days. Very comprehensive!
- Multi-Task Learning in Tensorflow: Part 1 - Jul 20, 2016.
A discussion and step-by-step tutorial on how to use Tensorflow graphs for multi-task learning.
- In Deep Learning, Architecture Engineering is the New Feature Engineering - Jul 19, 2016.
A discussion of architecture engineering in deep neural networks, and its relationship with feature engineering.
- MNIST Generative Adversarial Model in Keras - Jul 19, 2016.
This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.
- Statistical Data Analysis in Python - Jul 18, 2016.
This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.
- Predictive Analytics Introductory Key Terms, Explained - Jul 18, 2016.
Here is a collection of introductory predictive analytics terms and concepts, presented for the newcomer in a straight-forward, no frills definition style.
- America’s Next Topic Model - Jul 15, 2016.
Topic modeling is a a great way to get a bird's eye view on a large document collection using machine learning. Here are 3 ways to use open source Python tool Gensim to choose the best topic model.
- How to Start Learning Deep Learning - Jul 14, 2016.
Want to get started learning deep learning? Sure you do! Check out this great overview, advice, and list of resources.
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Bayesian Machine Learning, Explained - Jul 13, 2016.
Want to know about Bayesian machine learning? Sure you do! Get a great introductory explanation here, as well as suggestions where to go for further study.
- Support Vector Machines: A Simple Explanation - Jul 7, 2016.
A no-nonsense, 30,000 foot overview of Support Vector Machines, concisely explained with some great diagrams.
- Deep Residual Networks for Image Classification with Python + NumPy - Jul 7, 2016.
This post outlines the results of an innovative Deep Residual Network implementation for Image Classification using Python and NumPy.
- Mining Twitter Data with Python Part 7: Geolocation and Interactive Maps - Jul 6, 2016.
The final part of this 7 part series explores using geolocation and interactive maps with Twitter data.
- How to Compare Apples and Oranges ? : Part III - Jul 6, 2016.
In the previous article, look at techniques to compare categorical variables with the help of an example. In this article, we shall look at techniques to compare mixed type of variables i.e. numerical and categorical variables together.
- A Brief Primer on Linear Regression – Part III - Jul 5, 2016.
This third part of an introduction to linear regression moves past the topics covered in the first to discuss linearity, normality, outliers, and other topics of interest.
- Mining Twitter Data with Python Part 6: Sentiment Analysis Basics - Jul 5, 2016.
Part 6 of this series builds on the previous installments by exploring the basics of sentiment analysis on Twitter data.
- What is Softmax Regression and How is it Related to Logistic Regression? - Jul 1, 2016.
An informative exploration of softmax regression and its relationship with logistic regression, and situations in which each would be applicable.
- Text Mining 101: Topic Modeling - Jul 1, 2016.
We introduce the concept of topic modelling and explain two methods: Latent Dirichlet Allocation and TextRank. The techniques are ingenious in how they work – try them yourself.
Bayesian Machine Learning, Explained
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