- 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.
- “Auto-What?” – A Taxonomy of Automated Machine Learning - Sep 25, 2018.
Automated machine learning is a rapidly developing segment of artificial intelligence - it’s time to define what an AutoML product is so end-users can compare product capabilities intelligently.
- Highlight Sessions from Alibaba, Uber, The Washington Post – at Predictive Analytics World London - Sep 24, 2018.
The Predictive Analytics World London 2018 (Sep 17-18) agenda is now live. Have a look at what all the excitement is about!
- 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.
- 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.
- 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.
- Top KDnuggets tweets, Sep 12-18: Machine Learning Cheat Sheets - Sep 19, 2018.
Also: You Aren't So Smart: Cognitive Biases are Making Sure of It; 6 Books Every Data Scientist Should Keep Nearby.
- [Live Webinar] MLOps: Machine Learning Operationalization, Sep 27 - Sep 19, 2018.
Successfully pushing ML to production requires a shift in your DevOps practices to become MLOps, machine learning operationalization. Learn how to do it in this Sep 27 webinar.
- KDnuggets™ News 18:n35, Sep 19: How Many Data Scientists Out There? Hadoop for Beginners; Data Science of Adele - Sep 19, 2018.
Also Top /r/MachineLearning posts, August 2018: Everybody Dance Now; 10 Big Data Trends You Should Know; You Aren't So Smart: Cognitive Biases are Making Sure of It.
- 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.
- How to Put Active Learning to Work for Your Enterprise - Sep 17, 2018.
In this eBook from Figure Eight and AWS you'll learn what active learning is and how it works, the areas in which active learning can be particularly effective, and how active learning iteratively improves your model.
- Webinar: How Google BigQuery and Looker Can Accelerate Your Data Science Workflow, Sep 19. - Sep 17, 2018.
Join Looker for this webcast, Sep 19, 2 PM EST, where you will learn how you can leverage Looker with the power of BigQuery Machine Learning (BQML) to build machine learning (ML) models directly where your data lives.
- Top /r/MachineLearning posts, August 2018: Everybody Dance Now; Stanford class Machine Learning cheat sheets; Academic Torrents for sharing enormous datasets - Sep 15, 2018.
A range of interesting posts from the /r/MachineLearning Reddit group for the month of August, including: Everybody Dance Now; Stanford class Machine Learning cheat sheets; Academic Torrents; Getting Alexa to respond to sign language using TensorFlow; PyCharm IDE.
- KDnuggets™ News 18:n34, Sep 12: Essential Math for Data Science; 100 Days of Machine Learning Code; Drop Dropout - Sep 12, 2018.
Also: Neural Networks and Deep Learning: A Textbook; Don't Use Dropout in Convolutional Networks; Ultimate Guide to Getting Started with TensorFlow.
- Key Takeaways from KDD 2018: a Deconfounder, Machine Learning at Pinterest, Knowledge Graph - Sep 11, 2018.
Highlights and key takeaways from KDD 2018, 24th ACM SIGKDD conference on Data Science and Data Mining: including what is a deconfounder, how Pinterest approaches Machine Learning, Knowledge Graph for Products, and Differential Privacy.
- 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.
- Webinar: How Google BigQuery and Looker Can Accelerate Your Data Science Workflow, Sep 19 - Sep 10, 2018.
Join Looker for this webcast, Sep 19, 2 PM EST, where you will learn how you can leverage Looker with the power of BigQuery Machine Learning (BQML) to build machine learning (ML) models directly where your data lives.
- 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.
- The Forrester Wave™: Notebook-Based Predictive Analytics And Machine Learning Solutions, Q3 2018 - Sep 5, 2018.
Read this report to understand the top nine Predictive Analytics and Machine Learning solution providers in the market, and Forrester's 24-criteria evaluation of their strengths and weaknesses.
- Three Ways Big Data and Machine Learning Reinvent Online Video Experience - Aug 31, 2018.
With traditional TV viewing on the decline, we discuss several ways Big Data and Machine Learning can assist with online video, including redefining user recommendations, improving video buffering and leveraging MAM orchestration.
- 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.
- Optimus v2: Agile Data Science Workflows Made Easy - Aug 30, 2018.
Looking for a library to skyrocket your productivity as Data Scientist? Check this out!
- Top KDnuggets tweets, Aug 22-28: AI Knowledge Map: How To Classify AI Technologies; 100 Days of #MachineLearning Coding with #Python - Aug 29, 2018.
Also 25 fun questions for a machine learning interview; Data Visualization Cheat Sheet
- Deploying scikit-learn Models at Scale - Aug 29, 2018.
Find out how to serve your scikit-learn model in an auto-scaling, serverless environment! Today, we’ll take a trained scikit-learn model and deploy it on Cloud ML Engine.
- 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.
- 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.
- The future of Big Data, Machine Learning and Data Visualization in Europe - Aug 21, 2018.
Learn more about the hottest trends that are shaping the future and beyond at Big Data Summits in London and Barcelona. Deep dive into the topics that will shake up your industry and encourage innovation at your company. Enjoy £250 off all two-day events with code KD250.
- Kinetica: Sr. Software Engineer (Machine Learning) [Arlington, VA] - Aug 21, 2018.
Join an accomplished team to help build out a new scalable, distributed machine learning and data science platform with tight integrations and pipelines to a distributed, sharded GPU-powered database.
- One-Click Machine Learning Deployments with Anaconda Enterprise - Aug 20, 2018.
With Anaconda Enterprise, your organization can develop, govern, and automate machine learning pipelines, while scaling with ease.
- 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.
- 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.
- 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.
- Machine Learning with TensorFlow - Aug 16, 2018.
In this on-demand webinar, you’ll get a general introduction to working with Tensorflow and its surrounding ecosystem, general problem classes, where you can get big acceleration, and why you should be running on a CPU.
- 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.
- 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.
- Unsupervised Learning Demystified - Aug 13, 2018.
Unsupervised learning is a pattern-finding technique for mining inspiration from your data. Let's demystify!
- The Essential Guide to Training Data for Machine Learning - Aug 9, 2018.
Download Figure Eight's new ebook, The Essential Guide to Training Data, and you'll learn about the advantages of using more data, the differences between having lots of big data and having labeled data, and some great open datasets to bootstrap your model.
- 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.
- 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.
- AI and ML Day in Australia with Alteryx, Tableau, Amazon, Snowflake, Commonwealth Bank, and IAPA - Aug 8, 2018.
Key information regarding The Alteryx Analytics Revolution Summit roadshow in Australia, including dates, guest speakers, livestream information and how you can register for the roadshow closest to you.
- Production ML for Data Scientists: What You Can Do and How to Make It Easy, August 22 Webinar - Aug 8, 2018.
Learn about MLOps –machine learning operationalization that breaks down the silos between data science and IT; Streamlines deployment and orchestration, and adds advanced functionality.
- GitHub Python Data Science Spotlight: AutoML, NLP, Visualization, ML Workflows - Aug 8, 2018.
This post includes a wide spectrum of data science projects, all of which are open source and are present on GitHub repositories.
- KDnuggets™ News 18:n30, Aug 8: Iconic Data Visualisation; Data Scientist Interviews Demystified; Simple Statistics in Python - Aug 8, 2018.
Also: Selecting the Best Machine Learning Algorithm for Your Regression Problem; From Data to Viz: how to select the the right chart for your data; Only Numpy: Implementing GANs and Adam Optimizer using Numpy; Programming Best Practices for Data Science
- StrategyWise: Data Scientist – Machine Learning (Atlanta, GA) - Aug 7, 2018.
Seeking a Data Scientist with Machine Learning and AI experience. Candidates who are an excellent fit for this job possess a combination of business acumen, analytical problem-solving skills, programming and statistical computing, and are adept at learning new skills and grasping complex concepts.
- 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!
- Weapons of Math Destruction, Ethical Matrix, Nate Silver and more Highlights from the Data Science Leaders Summit - Jul 31, 2018.
Domino Data Lab hosted its first ever Data Science Leaders Summit at the lovely Yerba Buena Center for the Arts in San Francisco on May 30-31, 2018. Cathy O'Neil, Nate Silver, Cassie Kozyrkov and Eric Colson were some of the speakers at this event.
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- 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.
- 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.
- The Washington Post, Alibaba.com & ING – Learn from the best at Predictive Analytics World London - Jul 23, 2018.
Predictive Analytics World London, Oct 17-18 - the leading vendor-neutral machine learning conference - is close to a finalized agenda, packed with cutting edge insights.
- Ready your Skills for a Cloud-First World with Google - Jul 20, 2018.
The Machine Learning with TensorFlow on Google Cloud Platform Specialization on Coursera will help you jumpstart your career, includes hands-on labs, and takes you from a strategic overview to practical skills in building real-world, accurate ML models.
- Chaos is needed to keep us smart with Machine Learning - Jul 20, 2018.
This post analyses why the chaotic nature of our lives can be used to improve machine learning algorithms.
- 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.
- Charles River Analytics: Software Engineer – Physiological Sensing and Machine Learning - Jul 20, 2018.
Seeking an engineer experienced in signal processing, time series analysis, and/or machine learning who can contribute to the development of software aimed at interpreting information provided by wearable devices such as Fitbits and iWatches.
- 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.
- Efficient Graph-based Word Sense Induction - Jul 18, 2018.
This paper describes a set of algorithms for Natural Language Processing (NLP) that match or exceed the state of the art on several evaluation tasks, while also being much more computationally efficient.
- KDnuggets™ News 18:n27, Jul 18: Data Scientist was the sexiest job until…; Text Mining on the Command Line; Does PCA Really Work? - Jul 18, 2018.
Also: What is Minimum Viable (Data) Product?; Beating the 4-Year Slump: Mid-Career Growth in Data Science; GDPR after 2 months - What does it mean for Machine Learning?; Basic Image Data Analysis Using Numpy and OpenCV; fast.ai Deep Learning Part 2 Complete Course Notes
- Key Takeaways from the Strata San Jose 2018 - Jul 16, 2018.
By dropping 'Hadoop' from its name, the @strataconf 2018 in San Jose signaled the emphasis on machine learning, cloud, streaming and real-time applications.
- Dimensionality Reduction : Does PCA really improve classification outcome? - Jul 13, 2018.
In this post, I am going to verify this statement using a Principal Component Analysis ( PCA ) to try to improve the classification performance of a neural network over a dataset.
- New eBook: Machine Learning for Fraud Prevention - Jul 12, 2018.
Get best practices on incorporating machine learning to automate your fraud prevention process and optimize workflows. Download this free ebook now.
- 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.
- AI Solutionism - Jul 12, 2018.
Machine learning has huge potential for the future of humanity — but it won’t solve all our problems.
- Top KDnuggets tweets, Jul 4-10: Fantastic notes on the freely available @fastdotai machine learning course - Jul 11, 2018.
Also: Analyze a Soccer (Football) Game Using #Tensorflow Object Detection; 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning; Timsort - the fastest #sorting #algorithm you've never heard of.
- GDPR after 2 months – What does it mean for Machine Learning? - Jul 11, 2018.
Almost 2 months on from the GDPR introduction, how was machine learning affected? What does the future hold?
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: July and Beyond - Jul 9, 2018.
Coming soon: ICDM/MLDM New York, Data Innovation Summits Las Vegas, ICML Stockholm, IJCAI/ECAI Stockholm, TDWI Anaheim, KDD-2018 London, JupyterCon NYC, and many more.
- Weak and Strong Bias in Machine Learning - Jul 6, 2018.
With the arrival of the GDPR there has been increased focus on non-discrimination in machine learning. This post explores different forms of model bias and suggests some practical steps to improve fairness in machine learning.
- 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.
- 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.
- KDnuggets™ News 18:n25, Jun 27: 5 Clustering Algorithms Data Scientists Need to Know; Detecting Sarcasm with Deep Convolutional Neural Networks? - Jun 27, 2018.
Also 30 Free Resources for Machine Learning, Deep Learning, NLP ; 7 Simple Data Visualizations You Should Know in R.
- 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.
- 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.
- 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.
- 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.
- 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!
- Get Packt Skill Up Developer Skills Report - Jun 19, 2018.
Find the top tools for 4 distinct industries, learn what do developers in different sectors say is the next big thing, and more. Also get any Packt book or video for just $10.
- 5 Key Takeaways from Strata London 2018 - Jun 19, 2018.
5 highlights and thoughts from my attendance to Strata London 2018.
- 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.
- Choosing the Right Metric for Evaluating Machine Learning Models — Part 2 - Jun 19, 2018.
This will focus on commonly used metrics in classification, why should we prefer some over others with context.
- 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.
- Apple: Sr Software Engineer – Applied Machine Learning - Jun 15, 2018.
Seeking a Senior Software Engineer, to help build innovative software applications. You should have development and implementation experience on large scale critical applications.
- 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.
- KDnuggets™ News 18:n23, Jun 13: Did Python declare victory over R?; Master the Netflix Interview; Deep Learning Projects DIY Style - Jun 13, 2018.
Also: Command Line Tricks For Data Scientists; How (dis)similar are my train and test data?; 5 Machine Learning Projects You Should Not Overlook, June 2018; Introduction to Game Theory; Human Interpretable Machine Learning
- A Better Stats 101 - Jun 12, 2018.
Statistics encourages us to think systemically and recognize that variables normally do not operate in isolation, and that an effect usually has multiple causes. Some call this multivariate thinking. Statistics is particularly useful for uncovering the Why.
- 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!
- Why you need to improve your training data, and how to do it - Jun 11, 2018.
This article examines the way you need to improve your training data and how it can be accomplished, including speech commands, choosing the right data, picking a model fast and more.
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- How (dis)similar are my train and test data? - Jun 7, 2018.
This articles examines a scenario where your machine learning model can fail.
- 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.
- Human Interpretable Machine Learning (Part 1) — The Need and Importance of Model Interpretation - Jun 6, 2018.
A brief introduction into machine learning model interpretation.
- ioModel Machine Learning Research Platform – Open Source - Jun 5, 2018.
This article introduces ioModel, an open source research platform that ingests data and automatically generates descriptive statistics on that data.
- 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.
- Apple: Sr Software Engineer, Applied Machine Learning - Jun 5, 2018.
Seeking an energetic senior software engineer to help us develop, improve, and expand our cutting edge platform to ensure that the performance of our machine learning environment is second-to-none.
- 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.
- 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.
- 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.
- Machine Learning Breaking Bad – addressing Bias and Fairness in ML models - May 25, 2018.
As the use of analytics proliferate, companies will need to be able to identify models that are breaking bad.
- How Not to Regulate the Data Economy - May 24, 2018.
The GDPR will affect not just tech companies but any company that handles customer data — in other words, every company. And it will affect the use of data throughout the world, not just in Europe...
- Mastering Advanced Analytics with Apache Spark - May 22, 2018.
Get ebook with a collection of the most popular technical blog posts that introduce you to machine learning on Apache Spark, and highlight many of the major developments around Spark MLlib and GraphX.
- Frameworks for Approaching the Machine Learning Process - May 21, 2018.
This post is a summary of 2 distinct frameworks for approaching machine learning tasks, followed by a distilled third. Do they differ considerably (or at all) from each other, or from other such processes available?
- Kernel Machine Learning (KernelML) - Generalized Machine Learning Algorithm - May 18, 2018.
This article introduces a pip Python package called KernelML, created to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients.
- 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.
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- 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.
- 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?”
- What’s Hot in Machine Learning? Just Ask PAW Founder Eric Siegel - May 9, 2018.
What will 2018's key trends for machine learning be? Read what Predictive Analytics World Founder Eric Siegel has to say on the subject. And don't forget to register for Mega-PAW in Las Vegas, Jun 3-7!
- 7 Useful Suggestions from Andrew Ng “Machine Learning Yearning” - May 8, 2018.
Machine Learning Yearning is a book by AI and Deep Learning guru Andrew Ng, focusing on how to make machine learning algorithms work and how to structure machine learning projects. Here we present 7 very useful suggestions from the book.
- Top Data Science, Machine Learning Courses from Udemy – May 2018 - May 8, 2018.
Learn Machine Learning, Data Science, Python, Azure Machine Learning, and more with Udemy Mother's Day $9.99 sale - get top courses from leading instructors.
- 5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist - May 8, 2018.
Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.
- 2018 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 7, 2018.
Vote in KDnuggets 19th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?
- Deep Conversations: Lisha Li, Principal at Amplify Partners - May 3, 2018.
Mathematician Lisha Li expounds on how she thrives as a Venture Capitalist at Amplify Partners to identify, invest and nurture the right startups in Machine Learning and Distributed Systems.
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- C3 IoT: Sr Software Engineer, Machine Learning - May 2, 2018.
Seeking a Senior Software Engineer, Machine Learning to build and enhance tools for the Data Science team to mine data at scale, and enable the integration of Machine Learning models in C3 IoT Platform.
- KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric - May 2, 2018.
Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial
- 50+ Useful Machine Learning & Prediction APIs, 2018 Edition - May 1, 2018.
Extensive list of 50+ APIs in Face and Image Recognition ,Text Analysis, NLP, Sentiment Analysis, Language Translation, Machine Learning and prediction.
- Data Science vs Machine Learning vs Data Analytics vs Business Analytics - May 1, 2018.
This article gives a broad overview of data science and the various fields within it, including business analytics, data analytics, business intelligence, advanced analytics, machine learning, and AI.
- Operational Machine Learning: Seven Considerations for Successful MLOps - Apr 30, 2018.
In this article, we describe seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives
- What should be focus areas for Machine Learning / AI in 2018? - Apr 27, 2018.
This article looks at what are the recent trends in data science/ML/AI and suggests subareas DS groups need to focus on.
- Choosing the Right Metric for Evaluating Machine Learning Models – Part 1 - Apr 27, 2018.
Each machine learning model is trying to solve a problem with a different objective using a different dataset and hence, it is important to understand the context before choosing a metric.
- The Dirty Little Secret Every Data Scientist Knows (but won’t admit) - Apr 26, 2018.
Most people don’t realize, but the actual “fancy” machine learning algorithm is like the last mile of the marathon. There is so much that must be done before you get there!
- ML Powering Marketing Automation: New Guidebook - Apr 24, 2018.
Understanding and quantifying a customer's journey - otherwise known as marketing attribution - is essential for marketers to analyze the ROI from campaigns. Get the latest guidebook to understand how its done!
- Top 16 Open Source Deep Learning Libraries and Platforms - Apr 24, 2018.
We bring to you the top 16 open source deep learning libraries and platforms. TensorFlow is out in front as the undisputed number one, with Keras and Caffe completing the top three.
- Expedia: Senior Applied Researcher – Machine Learning - Apr 23, 2018.
Seeking top-notch applied researchers and scientists interested in breaking new grounds to solve some of the most complex computational problems in the marketing domain.
- Let’s Admit It: We’re a Long Way from Using “Real Intelligence” in AI - Apr 19, 2018.
With the growth of AI systems and unstructured data, there is a need for an independent means of data curation, evaluation and measurement of output that does not depend on the natural language constructs of AI and creates a comparative method of how the data is processed.
- KDnuggets™ News 18:n16, Apr 18: Key Algorithms and Statistical Models; Don’t learn Machine Learning in 24 hours; Data Scientist among the best US Jobs in 2018 - Apr 18, 2018.
Also: Top 10 Technology Trends of 2018; 12 Useful Things to Know About Machine Learning; Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks; Understanding What is Behind Sentiment Analysis - Part 1; Getting Started with PyTorch
- 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning - Apr 17, 2018.
It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.
- Key Algorithms and Statistical Models for Aspiring Data Scientists - Apr 16, 2018.
This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.
- Are High Level APIs Dumbing Down Machine Learning? - Apr 16, 2018.
Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?
- Don’t learn Machine Learning in 24 hours - Apr 13, 2018.
When it comes to machine learning, there's no quick way of teaching yourself - you're in it for the long haul.
- Unlock the Next Era of Analytics – AI and Machine Learning at Scale - Apr 12, 2018.
Join us on Apr 19 for an interactive virtual event to hear from a panel of analytic experts as they dispel the myths and dive into the nitty-gritty of how AI and machine learning will impact analytic teams.
- Onboarding Your Machine Learning Program - Apr 12, 2018.
Machine Learning's popularity is continuing to grow and has engraved itself in pretty much every industry. This article contains lessons from a data scientist on how to unlock it's full potential.
- 12 Useful Things to Know About Machine Learning - Apr 12, 2018.
This is a summary of 12 key lessons that machine learning researchers and practitioners have learned include pitfalls to avoid, important issues to focus on and answers to common questions.
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- Ten Machine Learning Algorithms You Should Know to Become a Data Scientist - Apr 11, 2018.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
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- Managing model complexity in the fight against fraud, Apr 18 Webinar - Apr 10, 2018.
Learn how to optimize your models by leveraging robust data sets that improve performance; avoiding endless feature engineering and overfitting; and other useful steps.
- Top 8 Free Must-Read Books on Deep Learning - Apr 10, 2018.
Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning.