Apr2019 May Tutorials, Overviews
All (67) | Courses, Education (2) | Meetings (2) | News (4) | Opinions (23) | Top Stories, Tweets (8) | Tutorials, Overviews (27) | Webcasts & Webinars (1)
- How to Deploy Machine Learning Models: The Ultimate Guide
- May 24, 2019.
Deploying machine learning models is hard. This article explains the complexity behind deploying machine learning models into production, its ongoing maintenance, iterative adjustments, and much more.
- Careful! Looking at your model results too much can cause information leakage
- May 24, 2019.
We all are aware of the issue of overfitting, which is essentially where the model you build replicates the training data results so perfectly its fitted to the training data and does not generalise to better represent the population the data comes to, with catastrophic results when you feed in new data and get very odd results.
- Analyzing Tweets with NLP in Minutes with Spark, Optimus and Twint
- May 24, 2019.
Social media has been gold for studying the way people communicate and behave, in this article I’ll show you the easiest way of analyzing tweets without the Twitter API and scalable for Big Data.
- Your Guide to Natural Language Processing (NLP)
- May 23, 2019.
This extensive post covers NLP use cases, basic examples, Tokenization, Stop Words Removal, Stemming, Lemmatization, Topic Modeling, the future of NLP, and more.
- End-to-End Machine Learning: Making videos from images
- May 23, 2019.
Video is a natural way for us to understand three dimensional and time varying information. Read this short post on how to achieve the creation of videos from still images.
- When Too Likely Human Means Not Human: Detecting Automatically Generated Text
- May 23, 2019.
Passably-human automated text generation is a reality. How do we best go about detecting it? As it turns out, being too predictably human may actually be a reasonably good indicator of not being human at all.
- Extracting Knowledge from Knowledge Graphs Using Facebook’s Pytorch-BigGraph
- May 22, 2019.
We are using the state-of-the-art Deep Learning tools to build a model for predict a word using the surrounding words as labels.
- Probability Mass and Density Functions
- May 21, 2019.
This content is part of a series about the chapter 3 on probability from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016). It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as my understanding of these concepts.
- Building a Computer Vision Model: Approaches and datasets
- May 20, 2019.
How can we build a computer vision model using CNNs? What are existing datasets? And what are approaches to train the model? This article provides an answer to these essential questions when trying to understand the most important concepts of computer vision.
- Autoencoders: Deep Learning with TensorFlow’s Eager Execution
- May 20, 2019.
Deep Learning has revolutionized the Machine Learning scene in the last years. Can we apply it to image compression? How well can a Deep Learning algorithm reconstruct pictures of kittens? What’s an autoencoder?
- 60+ useful graph visualization libraries
- May 17, 2019.
We outline 60+ graph visualization libraries that allow users to build applications to display and interact with network representations of data.
- PyCharm for Data Scientists
- May 17, 2019.
This article is a discussion of some of PyCharm's features, and a comparison with Spyder, an another popular IDE for Python. Read on to find the benefits and drawbacks of PyCharm, and an outline of when to prefer it to Spyder and vice versa.
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7 Steps to Mastering SQL for Data Science — 2019 Edition
- May 17, 2019.
Follow these updated 7 steps to go from SQL data science newbie to practitioner in a hurry. We consider only the necessary concepts and skills, and provide quality resources for each. - A complete guide to K-means clustering algorithm
- May 16, 2019.
Clustering - including K-means clustering - is an unsupervised learning technique used for data classification. We provide several examples to help further explain how it works.
- Large-Scale Evolution of Image Classifiers
- May 16, 2019.
Deep neural networks excel in many difficult tasks, given large amounts of training data and enough processing power. The neural network architecture is an important factor in achieving a highly accurate model... Techniques to automatically discover these neural network architectures are, therefore, very much desirable.
- Building Recommender systems with Azure Machine Learning service
- May 15, 2019.
Microsoft has provided a GitHub repository with Python best practice examples to facilitate the building and evaluation of recommendation systems using Azure Machine Learning services.
- Mathematical programming — Key Habit to Build Up for Advancing Data Science
- May 15, 2019.
We show how, by simulating the random throw of a dart, you can compute the value of pi approximately. This is a small step towards building the habit of mathematical programming, which should be a key skill in the repertoire of a budding data scientist.
- Customer Churn Prediction Using Machine Learning: Main Approaches and Models
- May 14, 2019.
We reach out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn prediction using Machine Learning.
- A Complete Exploratory Data Analysis and Visualization for Text Data: Combine Visualization and NLP to Generate Insights
- May 9, 2019.
Visually representing the content of a text document is one of the most important tasks in the field of text mining as a Data Scientist or NLP specialist. However, there are some gaps between visualizing unstructured (text) data and structured data.
- How to fix an Unbalanced Dataset
- May 8, 2019.
We explain several alternative ways to handle imbalanced datasets, including different resampling and ensembling methods with code examples.
- Linear Programming and Discrete Optimization with Python using PuLP
- May 8, 2019.
Knowledge of such optimization techniques is extremely useful for data scientists and machine learning (ML) practitioners as discrete and continuous optimization lie at the heart of modern ML and AI systems as well as data-driven business analytics processes.
- Intro to XGBoost: Predicting Life Expectancy with Supervised Learning
- May 8, 2019.
Today we’ll use XGBoost Boosted Trees for regression over the official Human Development Index dataset. XGBoost is a framework that allows us to train Boosted Trees exploiting multicore parallelism.
- Best US/Canada Masters in Analytics, Business Analytics, Data Science
- May 7, 2019.
In the final part of this series, we provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across the US and Canada.
- How to Automate Tasks on GitHub With Machine Learning for Fun and Profit
- May 3, 2019.
Check this tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.
- Modeling Price with Regularized Linear Model & XGBoost
- May 2, 2019.
We are going to implement regularization techniques for linear regression of house pricing data. Our goal in price modeling is to model the pattern and ignore the noise.
- How to correctly select a sample from a huge dataset in machine learning
- May 1, 2019.
We explain how choosing a small, representative dataset from a large population can improve model training reliability.
- Build Your First Chatbot Using Python & NLTK
- May 1, 2019.
Today we will learn to create a simple chat assistant or chatbot using Python’s NLTK library.