- Top R Packages for Data Cleaning - Mar 15, 2019.
Data cleaning is one of the most important and time consuming task for data scientists. Here are the top R packages for data cleaning.
Data Cleaning, Data Preparation, Data Science, Machine Learning, R
- My favorite mind-blowing Machine Learning/AI breakthroughs - Mar 14, 2019.
We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.
AI, AlphaStar, GANs, Generative Adversarial Network, Machine Learning, Machine Translation, Reinforcement Learning, Robots
- [PDF] Executive Guide To Machine Learning - Mar 13, 2019.
The Executive Guide covers the benefits to your business, the build-or-buy process, and gives a practical overview for implementing ML in your organization.
ActiveState, ebook, Machine Learning
- Towards Automatic Text Summarization: Extractive Methods - Mar 13, 2019.
The basic idea looks simple: find the gist, cut off all opinions and detail, and write a couple of perfect sentences, the task inevitably ended up in toil and turmoil. Here is a short overview of traditional approaches that have beaten a path to advanced deep learning techniques.
Bayesian, Deep Learning, Machine Learning, Sciforce, Text Analysis, Text Mining, Topic Modeling
- AI: Arms Race 2.0 - Mar 12, 2019.
An analysis of the current state of the competition between US, Europe, and China in AI, examining research, patent publications, global datasphere, devices and IoT, people, and more.
AI, China, Deep Learning, Europe, Investment, IoT, Machine Learning, Neural Networks, Startups, Trends, USA
- Beating the Bookies with Machine Learning - Mar 8, 2019.
We investigate how to use a custom loss function to identify fair odds, including a detailed example using machine learning to bet on the results of a darts match and how this can assist you in beating the bookmaker.
Machine Learning, PyTorch, Sports, Statistics
- 19 Inspiring Women in AI, Big Data, Data Science, Machine Learning - Mar 8, 2019.
For the 2019 international women's day, we profile a new set of 19 inspiring women who lead the field in AI, Big Data, Data Science, and Machine Learning fields.
AI, Data Science, Machine Learning, Women
- Designing Ethical Algorithms - Mar 8, 2019.
Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.
AI, Algorithms, Bias, Ethics, Machine Learning
- Another 10 Free Must-Read Books for Machine Learning and Data Science - Mar 6, 2019.
Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.
Books, Data Science, ebook, Free ebook, Machine Learning
- GANs Need Some Attention, Too - Mar 5, 2019.
Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.
AISC, Attention, Deep Learning, GANs, Image Generation, Machine Learning
- 4 Reasons Why Your Machine Learning Code is Probably Bad - Feb 26, 2019.
Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG.
Data Science, Machine Learning, Programming, Python, Workflow
- What are Some “Advanced” AI and Machine Learning Online Courses? - Feb 22, 2019.
Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?
AI, Machine Learning, MOOC, Online Education
- Artificial Neural Network Implementation using NumPy and Image Classification - Feb 21, 2019.
This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset
Pages: 1 2
Deep Learning, Machine Learning, Neural Networks, numpy, Python
- State of the art in AI and Machine Learning – highlights of papers with code - Feb 20, 2019.
We introduce papers with code, the free and open resource of state-of-the-art Machine Learning papers, code and evaluation tables.
AI, Machine Learning, Multitask Learning, NLP, Papers with code, Recommender Systems, Semantic Segmentation, TensorFlow, Transfer Learning
- How to Setup a Python Environment for Machine Learning - Feb 18, 2019.
In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.
Machine Learning, Programming, Python
- Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.
Deep Learning, Deep Neural Network, Machine Learning, Neural Networks, Optimization, TensorFlow
- An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning - Feb 13, 2019.
If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library.
Machine Learning, Python, scikit-learn
- Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.
Alteryx, Data Science Platform, Dataiku, DataRobot, Gartner, Google, H2O, IBM, Knime, Machine Learning, Magic Quadrant, MathWorks, Microsoft, RapidMiner, SAS, TIBCO
- Neural Networks – an Intuition - Feb 7, 2019.
Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.
Explained, History, Machine Learning, Neural Networks, Perceptron
- The Essential Data Science Venn Diagram - Feb 4, 2019.
A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition.
Analytics, Data Science, Machine Learning, Statistics, Venn Diagram
- Five Ways Your Safety Depends on Machine Learning - Feb 2, 2019.
Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.
AI, Eric Siegel, Machine Learning, Safety
- The Algorithms Aren’t Biased, We Are - Jan 29, 2019.
We explain the concept of bias and how it can appear in your projects, share some illustrative examples, and translate the latest academic research on “algorithmic bias.”
Algorithms, Bias, Machine Learning
- 7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition - Jan 29, 2019.
With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python!
7 Steps, Classification, Clustering, Jupyter, Machine Learning, Python, Regression
- Machine Learning Security - Jan 25, 2019.
We take a look at how malicious actors can break machine learning models and what some of the best practices are when it comes to stopping them.
Adversarial, Alexa, Machine Learning, Security
- How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy - Jan 23, 2019.
We explain how to retrieve estimates of a model's performance using scoring metrics, before taking a look at finding and diagnosing the potential problems of a machine learning algorithm.
Cross-validation, Forecasting, Machine Learning, Overfitting, Time Series
- Logistic Regression: A Concise Technical Overview - Jan 23, 2019.
Logistic Regression is a Regression technique that is used when we have a categorical outcome (2 or more categories). Logistic Regression is one of the most easily interpretable classification techniques in a Data Scientist’s portfolio.
Logistic Regression, Machine Learning
- How to Monitor Machine Learning Models in Real-Time - Jan 18, 2019.
We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.
Anomaly Detection, Deployment, Machine Learning, MapR, Monitoring, Real-time
- Automated Machine Learning in Python - Jan 18, 2019.
An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
Automated Machine Learning, AutoML, H2O, Keras, Machine Learning, Python, scikit-learn
- Comparing Machine Learning Models: Statistical vs. Practical Significance - Jan 18, 2019.
Is model A or B more accurate? Hmm… In this blog post, I’d love to share my recent findings on model comparison.
Machine Learning, Model Performance, P-value, Statistical Modeling, Statistical Significance
- The Hundred-Page Machine Learning Book - Jan 17, 2019.
This book covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.
Andriy Burkov, Book, Machine Learning, Peter Norvig
- Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples - Jan 17, 2019.
We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.
Cold Start, Data Scientist, Kirk D. Borne, Machine Learning
- How to build an API for a machine learning model in 5 minutes using Flask - Jan 17, 2019.
Flask is a micro web framework written in Python. It can create a REST API that allows you to send data, and receive a prediction as a response.
API, Flask, Machine Learning, Python
- The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
Automated Machine Learning, Facebook, fast.ai, Google, Keras, Machine Learning, Object Detection, Python, Reinforcement Learning, Word Embeddings
- Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated - Jan 14, 2019.
Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
AI, Analytics, Big Data, Blogs, Data Mining, Data Science, Data Visualization, Machine Learning
- End To End Guide For Machine Learning Projects - Jan 14, 2019.
Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.
Machine Learning, Workflow
- Why Vegetarians Miss Fewer Flights – Five Bizarre Insights from Data - Jan 12, 2019.
A frenzy of number-crunching is churning out a heap of insights that are colorful, sometimes surprising, and often valuable. We explain how this works, and investigate five bizarre discoveries found in data.
Credit Risk, Eric Siegel, Healthcare, Machine Learning, Overfitting, Uber
- 4 Myths of Big Data and 4 Ways to Improve with Deep Data - Jan 9, 2019.
There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.
Big Data, Data Lakes, Data Warehouse, Hype, Machine Learning, Sampling
- Math for Machine Learning - Jan 4, 2019.
This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.
Book, ebook, Machine Learning, Mathematics, Richard Han
- What to do when your training and testing data come from different distributions - Jan 4, 2019.
However, sometimes only a limited amount of data from the target distribution can be collected. It may not be sufficient to build the needed train/dev/test sets. What to do in such a case? Let us discuss some ideas!
Distribution, Machine Learning, Training Data
- Ensemble Learning: 5 Main Approaches - Jan 3, 2019.
We outline the most popular Ensemble methods including bagging, boosting, stacking, and more.
Bagging, Boosting, Ensemble Methods, Machine Learning
- 3 More Google Colab Environment Management Tips - Jan 2, 2019.
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.
Google, Google Colab, Jupyter, Machine Learning, Python
- Papers with Code: A Fantastic GitHub Resource for Machine Learning - Dec 31, 2018.
Looking for papers with code? If so, this GitHub repository, a clearinghouse for research papers and their corresponding implementation code, is definitely worth checking out.
GitHub, Machine Learning, Research
- Supervised Learning: Model Popularity from Past to Present - Dec 28, 2018.
An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?
Decision Trees, Deep Learning, Linear Regression, Logistic Regression, Machine Learning, Neural Networks, SVM
- The Essence of Machine Learning - Dec 28, 2018.
And so now, as an exercise in what may seem to be semantics, let's explore some 30,000 feet definitions of what machine learning is.
Aaron Courville, Classification, Ian Goodfellow, Machine Learning, Tom Mitchell, Yoshua Bengio
- A Case For Explainable AI & Machine Learning - Dec 27, 2018.
In support of the explainable AI cause, we present a variety of use cases covering operational needs, regulatory compliance and public trust and social acceptance.
Bias, Explainable AI, Explanation, Interpretability, Machine Learning
- Synthetic Data Generation: A must-have skill for new data scientists - Dec 27, 2018.
A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.
Pages: 1 2
Classification, Clustering, Datasets, Machine Learning, Python, Synthetic Data
- A Guide to Decision Trees for Machine Learning and Data Science - Dec 24, 2018.
What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.
Algorithms, Data Science, Decision Trees, Machine Learning, Python, scikit-learn
- Six Steps to Master Machine Learning with Data Preparation - Dec 21, 2018.
To prepare data for both analytics and machine learning initiatives teams can accelerate machine learning and data science projects to deliver an immersive business consumer experience that accelerates and automates the data-to-insight pipeline by following six critical steps.
Data Preparation, Machine Learning
- Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI - Dec 20, 2018.
We explain the key differences between explainability and interpretability and why they're so important for machine learning and AI, before taking a look at several techniques and methods for improving machine learning interpretability.
AI, Explainable AI, Explanation, Interpretability, Machine Learning
- 10 More Must-See Free Courses for Machine Learning and Data Science - Dec 20, 2018.
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.
AI, Algorithms, Big Data, Data Science, Deep Learning, Machine Learning, MIT, NLP, Reinforcement Learning, U. of Washington, UC Berkeley, Yandex
- Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning - Dec 19, 2018.
Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning.
Data Science, Deep Learning, Machine Learning, Pandas, Python, PyTorch, TensorFlow
- Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019 - Dec 18, 2018.
This is a collection of data science, machine learning, analytics, and AI predictions for next year from a number of top industry organizations. See what the insiders feel is on the horizon for 2019!
2019 Predictions, AI, Analytics, Data Science, Domino, dotData, Figure Eight, Industry, Knime, Machine Learning, MapR, MathWorks, OpenText, ParallelM, Salesforce, Splice Machine, Splunk
- Four Approaches to Explaining AI and Machine Learning - Dec 12, 2018.
We discuss several explainability techniques being championed today, including LOCO (leave one column out), permutation impact, and LIME (local interpretable model-agnostic explanations).
AI, Explainable AI, Interpretability, LIME, Machine Learning
- Machine Learning & AI Main Developments in 2018 and Key Trends for 2019 - Dec 11, 2018.
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2018 and their 2019 key trend predictions.
2019 Predictions, AI, Ajit Jaokar, Andriy Burkov, Anima Anandkumar, Brandon Rohrer, Daniel Tunkelang, Machine Learning, Pedro Domingos, Rachel Thomas, Zachary Lipton
- Learning Machine Learning vs Learning Data Science - Dec 11, 2018.
We clarify some important and often-overlooked distinctions between Machine Learning and Data Science, covering education, scalable vs non-scalable jobs, career paths, and more.
Career, Data Science, Education, Machine Learning
- Should you become a data scientist? - Dec 10, 2018.
An overview of the current situation for data scientists, from its origins and history, to the recent growth in job postings, and looking at what changes the future might bring.
Career, Data Science, Data Scientist, History, Machine Learning, Tips, Trends
- How Different are Conventional Programming and Machine Learning? - Dec 10, 2018.
When I heard about Machine Learning I couldn't contain the amazement. I was not able to get my mind around the fact, that unlike normal software programs - which I was accustomed to - I wouldn't even have to teach a computer the "how" in detail about all the future scenarios up front.
Machine Learning, Programming
- A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more - Dec 7, 2018.
A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.
Cheat Sheet, Data Science Education, Deep Learning, Machine Learning, Mathematics, Open Source, Reinforcement Learning, Resources, Statistics
- The Machine Learning Project Checklist - Dec 7, 2018.
In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow."
Checklist, Machine Learning, Process, Workflow
- Common mistakes when carrying out machine learning and data science - Dec 6, 2018.
We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!
Data Preparation, Data Science, Data Visualization, Machine Learning, Missing Values, Mistakes, Multicollinearity
- Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies - Dec 6, 2018.
The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.
Pages: 1 2
Explainable AI, Interpretability, LIME, Machine Learning, SHAP
- Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools - Dec 3, 2018.
We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.
Big Data, Data Visualization, Deep Learning, Jupyter, Machine Learning, Python, R, Tableau
- Interpretability is crucial for trusting AI and machine learning - Nov 30, 2018.
We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.
AI, Explainable AI, Explanation, Interpretability, Machine Learning, Trust
- A Complete Guide to Choosing the Best Machine Learning Course - Nov 30, 2018.
A collection of the best courses covering machine learning concepts and techniques, including supervised and unsupervised learning, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer.
Career, Machine Learning, Online Education, Simplilearn
- Deep Learning for the Masses (… and The Semantic Layer) - Nov 30, 2018.
Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.
Pages: 1 2
AI, Deep Learning, Machine Learning, Semantic Analysis
- Variational Autoencoders Explained in Detail - Nov 30, 2018.
We explain how to implement VAE - including simple to understand tensorflow code using MNIST and a cool trick of how you can generate an image of a digit conditioned on the digit.
Autoencoder, Deep Learning, Machine Learning, MNIST, TensorFlow
- Bringing Machine Learning Research to Product Commercialization - Nov 27, 2018.
In this blog post I want to share some of the insights into the differences between academia and industry when applying deep learning to real-world problems as we experienced them at Merantix over the last two years.
Academics, Machine Learning, Products, Research
- How Important is that Machine Learning Model be Understandable? We analyze poll results - Nov 19, 2018.
About 85% of respondents said it was always or frequently important that Machine Learning model be understandable. This was is especially important for academic researchers, and surprisingly more in US/Canada than in Europe or Asia.
Asia, Europe, Explainable AI, Explanation, GDPR, Machine Learning, Poll, USA
- Mastering The New Generation of Gradient Boosting - Nov 15, 2018.
Catboost, the new kid on the block, has been around for a little more than a year now, and it is already threatening XGBoost, LightGBM and H2O.
Boosting, Gradient Boosting, Machine Learning, Python
- [Download] Real-Life ML Examples + Notebooks - Nov 13, 2018.
In this eBook, we will walk you through four Machine Learning use cases on Databricks: Loan Risk Use Case; Advertising Analytics & Prediction Use Case; Market Basket Analysis Problem at Scale; Suspicious Behavior Identification in Video Use Case. Get your copy now!
Databricks, ebook, Jupyter, Machine Learning, Use Cases
- 10 Free Must-See Courses for Machine Learning and Data Science - Nov 8, 2018.
Check out a collection of free machine learning and data science courses to kick off your winter learning season.
Data Science, Deep Learning, fast.ai, Google, Linear Algebra, Machine Learning, MIT, NLP, Reinforcement Learning, Stanford, Yandex
- Machine Learning Classification: A Dataset-based Pictorial - Nov 5, 2018.
In order to relate machine learning classification to the practical, let's see how this concept plays out, step by step (and with images), specifically in direct relation to a dataset.
Datasets, Machine Learning, Supervised Learning
- Naive Bayes from Scratch using Python only – No Fancy Frameworks - Oct 25, 2018.
We provide a complete step by step pythonic implementation of naive bayes, and by keeping in mind the mathematical & probabilistic difficulties we usually face when trying to dive deep in to the algorithmic insights of ML algorithms, this post should be ideal for beginners.
Pages: 1 2
Machine Learning, Naive Bayes, Python
- Implementing Automated Machine Learning Systems with Open Source Tools - Oct 25, 2018.
What if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline? Rest assured that there is no need to reinvent any wheels.
Automated Machine Learning, Feature Engineering, Feature Selection, Hyperparameter, Machine Learning, Open Source
- Building a Question-Answering System from Scratch - Oct 24, 2018.
This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems. In the future parts, we will try to implement deep learning techniques, specifically sequence modeling for this problem.
Machine Learning, NLP, Question answering
- Introduction to Active Learning - Oct 23, 2018.
An extensive overview of Active Learning, with an explanation into how it works and can assist with data labeling, as well as its performance and potential limitations.
Active Learning, Data Preparation, Figure Eight, Machine Learning
- How to Define a Machine Learning Problem Like a Detective - Oct 22, 2018.
The common refrain among machine learning practitioners is that it’s as much an art as a science. True enough, but in this discipline, you can only appreciate the former if you understand the latter.
Crime, Data journalism, Machine Learning
- The Intuitions Behind Bayesian Optimization with Gaussian Processes - Oct 19, 2018.
Bayesian Optimization adds a Bayesian methodology to the iterative optimizer paradigm by incorporating a prior model on the space of possible target functions. This article introduces the basic concepts and intuitions behind Bayesian Optimization with Gaussian Processes.
Bayesian, Distribution, Hyperparameter, Machine Learning, Optimization
- The Main Approaches to Natural Language Processing Tasks - Oct 17, 2018.
Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches.
Machine Learning, Neural Networks, NLP, Text Classification
- Using Confusion Matrices to Quantify the Cost of Being Wrong - Oct 11, 2018.
The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.
Confusion Matrix, Data Science, Machine Learning, Metrics, Predictive Modeling
- Top 8 Python Machine Learning Libraries - Oct 9, 2018.
Part 1 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
GitHub, Keras, Machine Learning, Python
- A Concise Explanation of Learning Algorithms with the Mitchell Paradigm - Oct 5, 2018.
A single quote from Tom Mitchell can shed light on both the abstract concept and concrete implementations of machine learning algorithms.
Algorithms, Learning, Machine Learning, Tom Mitchell
- Semantic Segmentation: Wiki, Applications and Resources - Oct 4, 2018.
An extensive overview covering the features of Semantic Segmentation and possible uses for it, including GeoSensing, Autonomous Drive, Facial Recognition and more.
Deep Learning, Image Recognition, Machine Learning, Object Detection, Segmentation
- How to Create a Simple Neural Network in Python - Oct 2, 2018.
The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that.
Machine Learning, Neural Networks, Python
- 5 Reasons Why You Should Use Cross-Validation in Your Data Science Projects - Oct 2, 2018.
In cross-validation, we do more than one split. We can do 3, 5, 10 or any K number of splits. Those splits called Folds, and there are many strategies we can create these folds with.
Cross-validation, Data Science, Machine Learning
- Recent Advances for a Better Understanding of Deep Learning - Oct 1, 2018.
A summary of the newest deep learning trends, including Non Convex Optimization, Overparametrization and Generalization, Generative Models, Stochastic Gradient Descent (SGD) and more.
Deep Learning, Explained, Flat Minima, Linear Networks, Machine Learning, Optimization, SGD
- When Bayes, Ockham, and Shannon come together to define machine learning - Sep 25, 2018.
A beautiful idea, which binds together concepts from statistics, information theory, and philosophy.
Bayes Theorem, Machine Learning
- Building a Machine Learning Model through Trial and Error - Sep 24, 2018.
A step-by-step guide that includes suggestions on how to preprocess data and deriving features from this. This article also contains links to help you explore additional resources about machine learning methods and other examples.
Deployment, Machine Learning, MathWorks
- Machine Learning: How to Build a Model From Scratch - Sep 20, 2018.
Register now for upcoming webinar, Building a Machine Learning Fraud Model with Momentum Travel, on Sep 27 @ 10 AM PT.
Machine Learning, Modeling, WhitePages
- 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study - Sep 20, 2018.
Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process.
Data Science, Machine Learning, Neural Networks
- Free resources to learn Natural Language Processing - Sep 18, 2018.
An extensive list of free resources to help you learn Natural Language Processing, including explanations on Text Classification, Sequence Labeling, Machine Translation and more.
Beginners, Machine Learning, Machine Translation, NLP, Sentiment Analysis, Text Classification
- Machine Learning Cheat Sheets - Sep 11, 2018.
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.
Cheat Sheet, Deep Learning, Machine Learning, Mathematics, Neural Networks, Probability, Statistics, Supervised Learning, Tips, Unsupervised Learning
- Journey to Machine Learning – 100 Days of ML Code - Sep 7, 2018.
A personal account from Machine Learning enthusiast Avik Jain on his experiences of #100DaysOfMLCode, a challenge that encourages beginners to code and study machine learning for at least an hour, every day for 100 days.
GitHub, K-nearest neighbors, Machine Learning, Python, SVM
- AI Knowledge Map: How To Classify AI Technologies - Aug 31, 2018.
What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI.
AI, Classification, Deep Learning, Machine Intelligence, Machine Learning, Neural Networks
- How to Make Your Machine Learning Models Robust to Outliers - Aug 28, 2018.
In this blog, we’ll try to understand the different interpretations of this “distant” notion. We will also look into the outlier detection and treatment techniques while seeing their impact on different types of machine learning models.
Machine Learning, Modeling, Outliers
- 9 Things You Should Know About TensorFlow - Aug 22, 2018.
A summary of the key points from the Google Cloud Next in San Francisco, "What’s New with TensorFlow?", including neural networks, TensorFlow Lite, data pipelines and more.
Deep Learning, Google, Keras, Machine Learning, Python, TensorFlow
- Interpreting a data set, beginning to end - Aug 20, 2018.
Detailed knowledge of your data is key to understanding it! We review several important methods that to understand the data, including summary statistics with visualization, embedding methods like PCA and t-SNE, and Topological Data Analysis.
Analytics, Big Data, Data Science, Data Visualization, Machine Learning, SAS, Statistics, t-SNE
- Why Automated Feature Engineering Will Change the Way You Do Machine Learning - Aug 20, 2018.
Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage.
Automated Machine Learning, Feature Engineering, Machine Learning, Python
- Cartoon: Machine Learning takes a vacation - Aug 18, 2018.
August is a popular time for vacation, and even hard-working AI may want to take a few epochs off from its training. KDnuggets Cartoon looks at how this might go.
Cartoon, Deep Learning, Humor, Machine Learning, Robots
- Reinforcement Learning: The Business Use Case, Part 2 - Aug 16, 2018.
In this post, I will explore the implementation of reinforcement learning in trading. The Financial industry has been exploring the applications of Artificial Intelligence and Machine Learning for their use-cases, but the monetary risk has prompted reluctance.
Business, Finance, Machine Learning, Reinforcement Learning, Use Cases
- Setting up your AI Dev Environment in 5 Minutes - Aug 13, 2018.
Whether you're a novice data science enthusiast setting up TensorFlow for the first time, or a seasoned AI engineer working with terabytes of data, getting your libraries, packages, and frameworks installed is always a struggle. Learn how datmo, an open source python package, helps you get started in minutes.
AI, datmo, Development, Docker, Machine Learning, Python, TensorFlow
- Unsupervised Learning Demystified - Aug 13, 2018.
Unsupervised learning is a pattern-finding technique for mining inspiration from your data. Let's demystify!
Cassie Kozyrkov, Clustering, Machine Learning, Unsupervised Learning
- Building Reliable Machine Learning Models with Cross-validation - Aug 9, 2018.
Cross-validation is frequently used to train, measure and finally select a machine learning model for a given dataset because it helps assess how the results of a model will generalize to an independent data set in practice.
Comet.ml, Cross-validation, Machine Learning, Modeling, scikit-learn
- Reinforcement Learning: The Business Use Case, Part 1 - Aug 9, 2018.
At base, RL is a complex algorithm for mapping observed entities and measures into some set of actions, while optimizing for a long-term or short-term reward.
Business, Machine Learning, Reinforcement Learning, Use Cases
- Selecting the Best Machine Learning Algorithm for Your Regression Problem - Aug 1, 2018.
This post should then serve as a great aid in selecting the best ML algorithm for you regression problem!
Algorithms, Machine Learning, Regression
- Remote Data Science: How to Send R and Python Execution to SQL Server from Jupyter Notebooks - Jul 27, 2018.
Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around.
Jupyter, Machine Learning, Microsoft, Python, R, SQL, SQL Server
- 9 Reasons why your machine learning project will fail - Jul 25, 2018.
This article explains in detail some of the issues that you may face during your machine learning project.
Deployment, Failure, Machine Learning, Project Fail
- Receiver Operating Characteristic Curves Demystified (in Python) - Jul 20, 2018.
In this blog, I will reveal, step by step, how to plot an ROC curve using Python. After that, I will explain the characteristics of a basic ROC curve.
Machine Learning, Metrics, Python, ROC-AUC
- Math for Machine Learning: Open Doors to Data Science and Artificial Intelligence - Jul 18, 2018.
This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.
ebook, Machine Learning, Mathematics, Richard Han
- What is Minimum Viable (Data) Product? - Jul 12, 2018.
This post gives a personal insight into what Minimum Viable Product means for Machine Learning and the importance of starting small and iterating.
AirBnB, Data, Machine Learning, Products
- fast.ai Machine Learning Course Notes - Jul 6, 2018.
This posts is a collection of a set of fantastic notes on the fast.ai machine learning MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.
fast.ai, Jeremy Howard, Machine Learning, MOOC
- Automated Machine Learning vs Automated Data Science - Jul 2, 2018.
Just by adding the term "automated" in front of these 2 separate, distinct concepts does not somehow make them equivalent. Machine learning and data science are not the same thing.
Automated Data Science, Automated Machine Learning, Data Science, Machine Learning
- 5 Data Science Projects That Will Get You Hired in 2018 - Jun 26, 2018.
A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.
Data Preparation, Data Science, Data Visualization, Hiring, Jupyter, Machine Learning
- How to Execute R and Python in SQL Server with Machine Learning Services - Jun 25, 2018.
Machine Learning Services in SQL Server eliminates the need for data movement - you can install and run R/Python packages to build Deep Learning and AI applications on data in SQL Server.
Azure ML, Machine Learning, Microsoft, Python, R, SQL, SQL Server
- 30 Free Resources for Machine Learning, Deep Learning, NLP & AI - Jun 25, 2018.
Check out this collection of 30 ML, DL, NLP & AI resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
AI, Deep Learning, Machine Learning, NLP
- An Intuitive Introduction to Gradient Descent - Jun 21, 2018.
This post provides a good introduction to Gradient Descent, covering the intuition, variants and choosing the learning rate.
Gradient Descent, Machine Learning, Optimization
- The 5 Clustering Algorithms Data Scientists Need to Know - Jun 20, 2018.
Today, we’re going to look at 5 popular clustering algorithms that data scientists need to know and their pros and cons!
Clustering, Data Scientist, DBSCAN, Machine Learning
- Data Science Predicting The Future - Jun 19, 2018.
In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future.
Data Science, Forecasting, Machine Learning, Programming Languages, Regression
- Step Forward Feature Selection: A Practical Example in Python - Jun 18, 2018.
When it comes to disciplined approaches to feature selection, wrapper methods are those which marry the feature selection process to the type of model being built, evaluating feature subsets in order to detect the model performance between features, and subsequently select the best performing subset.
Feature Selection, Machine Learning, Python
- IoT on AWS: Machine Learning Models and Dashboards from Sensor Data - Jun 15, 2018.
I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.
AWS, Dashboard, IoT, Machine Learning, Rubens Zimbres
- 5 Machine Learning Projects You Should Not Overlook, June 2018 - Jun 12, 2018.
Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
Interpretability, Keras, Machine Learning, Model Performance, NLP, Overlook, Recurrent Neural Networks, Visualization
- How (dis)similar are my train and test data? - Jun 7, 2018.
This articles examines a scenario where your machine learning model can fail.
Data Science, Datasets, Feature Selection, Machine Learning, Training Data
- The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R? - Jun 6, 2018.
We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.
Anaconda, Apache Spark, Data Science, Keras, Machine Learning, Open Source, Poll, Python, R, RapidMiner, Scala, scikit-learn, TensorFlow
- Three techniques to improve machine learning model performance with imbalanced datasets - Jun 5, 2018.
The primary objective of this project was to handle data imbalance issue. In the following subsections, I describe three techniques I used to overcome the data imbalance problem.
Balancing Classes, Machine Learning, Unbalanced
- How To Build Intelligent Dashboards Powered by Machine Learning - Jun 1, 2018.
In this webinar on Jun 5, 1:00 pm ET, analytics industry expert Jen Underwood will demonstrate how to visualize machine learning results with dashboard tools.
Business Intelligence, Dashboard, DataRobot, Machine Learning
- Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM - May 29, 2018.
CRISP-DM methodology is a must teach to explain analytics project steps. This article purpose it to complement it with specific chart flow that explain as simply as possible how it is more likely used in descriptive analytics, classic machine learning or deep learning.
CRISP-DM, Deep Learning, Descriptive Analytics, Machine Learning
- 10 More Free Must-Read Books for Machine Learning and Data Science - May 28, 2018.
Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started.
Books, Data Science, ebook, Free ebook, Machine Learning
- How to Organize Data Labeling for Machine Learning: Approaches and Tools - May 16, 2018.
The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.
Pages: 1 2
Altexsoft, Crowdsourcing, Data Labeling, Data Preparation, Image Recognition, Machine Learning, Training Data
- The Executive Guide to Data Science and Machine Learning - May 10, 2018.
This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.
Big Data, Business, Data Science, Machine Learning
- Deep learning scaling is predictable, empirically - May 10, 2018.
This study starts with a simple question: “how can we improve the state of the art in deep learning?”
Deep Learning, Machine Learning, Scalability