Search results for How to Invest

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• 5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist">5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist

  Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.

  https://www.kdnuggets.com/2018/05/5-reasons-logistic-regression-first-data-scientist.html

• To Kaggle Or Not

  Kaggle is the most well known competition platform for predictive modeling and analytics. This article looks into the different aspects of Kaggle and the benefits it can bring to data scientists.

  https://www.kdnuggets.com/2018/05/to-kaggle-or-not.html

• Getting Started with spaCy for Natural Language Processing

  spaCy is a Python natural language processing library specifically designed with the goal of being a useful library for implementing production-ready systems. It is particularly fast and intuitive, making it a top contender for NLP tasks.

  https://www.kdnuggets.com/2018/05/getting-started-spacy-natural-language-processing.html

• Jupyter Notebook for Beginners: A Tutorial

  The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. Although it is possible to use many different programming languages within Jupyter Notebooks, this article will focus on Python as it is the most common use case.

  https://www.kdnuggets.com/2018/05/jupyter-notebook-beginners-tutorial.html

• Operational Machine Learning: Seven Considerations for Successful MLOps

  In this article, we describe seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives

  https://www.kdnuggets.com/2018/04/operational-machine-learning-successful-mlops.html

• How to Make AI More Accessible

  I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here.

  https://www.kdnuggets.com/2018/04/make-ai-more-accessible.html

• The Dirty Little Secret Every Data Scientist Knows (but won’t admit)

  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!

  https://www.kdnuggets.com/2018/04/dirty-little-secret-data-scientist.html

• Building Convolutional Neural Network using NumPy from Scratch">Building Convolutional Neural Network using NumPy from Scratch

  In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.

  https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html

• Data Science Interview Guide

  Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course).

  https://www.kdnuggets.com/2018/04/data-science-interview-guide.html

• Are High Level APIs Dumbing Down Machine Learning?

  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?

  https://www.kdnuggets.com/2018/04/high-level-apis-dumbing-down-machine-learning.html

• Onboarding Your Machine Learning Program

  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.

  https://www.kdnuggets.com/2018/04/onboarding-machine-learning.html

• Comet.ml – Machine Learning Experiment Management

  This article presents comet.ml – a platform that allows tracking machine learning experiments with an emphasis on collaboration and knowledge sharing.

  https://www.kdnuggets.com/2018/04/comet-ml-machine-learning-experiment-management.html

• Descriptive Statistics: The Mighty Dwarf of Data Science – Crest Factor

  No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.

  https://www.kdnuggets.com/2018/04/descriptive-statistics-mighty-dwarf-data-science-crest-factor.html

• How To Choose The Right Chart Type For Your Data

  The power of charts to assist in accurate interpretation is massive and that's why it is vital to select the correct type when you are trying to visualize data.

  https://www.kdnuggets.com/2018/04/right-chart-your-data.html

• How Do I Get My First Data Science Job?">How Do I Get My First Data Science Job?

  Here are the steps you need to obtain your first job in data science, including details on how to create a good portfolio, key networking tips, getting the right education and managing expectations.

  https://www.kdnuggets.com/2018/04/first-data-science-job.html

• A “Weird” Introduction to Deep Learning">A “Weird” Introduction to Deep Learning

  There are amazing introductions, courses and blog posts on Deep Learning. But this is a different kind of introduction.

  https://www.kdnuggets.com/2018/03/weird-introduction-deep-learning.html

• Exploring DeepFakes">Exploring DeepFakes

  In this post, I explore the capabilities of this tech, describe how it works, and discuss potential applications.

  https://www.kdnuggets.com/2018/03/exploring-deepfakes.html

• Descriptive Statistics: The Mighty Dwarf of Data Science

  No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.

  https://www.kdnuggets.com/2018/03/descriptive-statistics-mighty-dwarf-data-science.html

• Multiscale Methods and Machine Learning

  We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.

  https://www.kdnuggets.com/2018/03/multiscale-methods-machine-learning.html

• Web Scraping with Python: Illustration with CIA World Factbook

  In this article, we show how to use Python libraries and HTML parsing to extract useful information from a website and answer some important analytics questions afterwards.

  https://www.kdnuggets.com/2018/03/web-scraping-python-cia-world-factbook.html

• Introduction to Optimization with Genetic Algorithm">Introduction to Optimization with Genetic Algorithm

  This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.

  https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html

• Predictive and Preventive Maintenance

  Analytics is becoming important part of maintenance, with applications to analyzing part failures, using failure distributions to simulate product life, and determining the root cause of failures. We provide an overview of predictive maintenance, its usage and key issues to be considered.

  https://www.kdnuggets.com/2018/03/predictive-preventive-maintenance.html

• How to do Machine Learning Efficiently

  I now believe that there is an art, or craftsmanship, to structuring machine learning work and none of the math heavy books I tended to binge on seem to mention this.

  https://www.kdnuggets.com/2018/03/machine-learning-efficiently.html

• How StockTwits Applies Social and Sentiment Data Science

  StockTwits is a social network for investors and traders, giving them a platform to share assertions and perceptions, analyses and predictions.

  https://www.kdnuggets.com/2018/03/stocktwits-social-sentiment-data-science.html

• Time Series for Dummies – The 3 Step Process">Time Series for Dummies – The 3 Step Process

  Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.

  https://www.kdnuggets.com/2018/03/time-series-dummies-3-step-process.html

• The Current Hype Cycle in Artificial Intelligence

  Over the past decade, the field of artificial intelligence (AI) has seen striking developments. As surveyed in, there now exist over twenty domains in which AI programs are performing at least as well as (if not better than) humans.

  https://www.kdnuggets.com/2018/02/current-hype-cycle-artificial-intelligence.html

• 5 Fantastic Practical Natural Language Processing Resources

  This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.

  https://www.kdnuggets.com/2018/02/5-fantastic-practical-natural-language-processing-resources.html

• A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018">A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018

  In this article, we will compare the most commonly used platforms and analyze their main features to help you choose one or several platforms that will provide indispensable aid for your work communication.

  https://www.kdnuggets.com/2018/02/comparative-analysis-top-6-bi-data-visualization-tools-2018.html

• Where AI is already rivaling humans

  Since 2011, AI hit hypergrowth, and researchers have created several AI solutions that are almost as good as – or better than – humans in several domains, including games, healthcare, computer vision and object recognition, speech to text conversion, speaker recognition, and improved robots and chat-bots for solving specific problems.

  https://www.kdnuggets.com/2018/02/domains-ai-rivaling-humans.html

• Graph Databases Burst into the Mainstream

  What do Amazon, Facebook, Google, IBM, Microsoft and Twitter have in common? They're all adopters of graph databases - a hot technology that continues to evolve.

  https://www.kdnuggets.com/2018/02/graph-databases-burst-into-the-mainstream.html

• The Birth of AI and The First AI Hype Cycle

  A dazzling review of AI History, from Alan Turing and Turing Test, to Simon and Newell and Logic Theorist, to Marvin Minsky and Perceptron, birth of Rule-based systems and Machine Learning, Eliza - first chatbot, Robotics, and the bust which led to first AI Winter.

  https://www.kdnuggets.com/2018/02/birth-ai-first-hype-cycle.html

• A Basic Recipe for Machine Learning">A Basic Recipe for Machine Learning

  One of the gems that I felt needed to be written down from Ng's deep learning courses is his general recipe to approaching a deep learning algorithm/model.

  https://www.kdnuggets.com/2018/02/basic-recipe-machine-learning.html

• Interview: Bill Moreau, USOC on Empowering World’s Best Athletes through Analytics.

  CNBC recently quoted this KDnuggets interview which discussed how United States Olympic Committee uses Big Data, how athletes respond to Analytical insights, integration of sports medicine into sports performance and sports injury.

  https://www.kdnuggets.com/2018/02/interview-bill-moreau-usoc.html

• Top 15 Scala Libraries for Data Science in 2018

  For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.

  https://www.kdnuggets.com/2018/02/top-15-scala-libraries-data-science-2018.html

• Web Scraping Tutorial with Python: Tips and Tricks">Web Scraping Tutorial with Python: Tips and Tricks

  This post is intended for people who are interested to know about the common design patterns, pitfalls and rules related to the web scraping.

  https://www.kdnuggets.com/2018/02/web-scraping-tutorial-python.html

• Automated Text Classification Using Machine Learning

  In this post, we talk about the technology, applications, customization, and segmentation related to our automated text classification API.

  https://www.kdnuggets.com/2018/01/automated-text-classification-machine-learning.html

• Error Analysis to your Rescue – Lessons from Andrew Ng, part 3

  The last entry in a series of posts about Andrew Ng's lessons on strategies to follow when fixing errors in your algorithm

  https://www.kdnuggets.com/2018/01/error-analysis-your-rescue.html

• Using AutoML to Generate Machine Learning Pipelines with TPOT

  This post will take a different approach to constructing pipelines. Certainly the title gives away this difference: instead of hand-crafting pipelines and hyperparameter optimization, and performing model selection ourselves, we will instead automate these processes.

  https://www.kdnuggets.com/2018/01/managing-machine-learning-workflows-scikit-learn-pipelines-part-4.html

• Four Big Data Trends for 2018

  Curious about the future of Big Data and AI? Here’s what the trends have it in 2018 for innovations.

  https://www.kdnuggets.com/2018/01/four-big-data-trends-2018.html

• How To Grow As A Data Scientist">How To Grow As A Data Scientist

  In order for a data scientist to grow, they need to be challenged beyond the technical aspects of their jobs. They need to question their data sources, be concise in their insights, know their business and help guide their leaders.

  https://www.kdnuggets.com/2018/01/how-grow-data-scientist.html

• Want to Become a Data Scientist? Try Feynman Technique">Want to Become a Data Scientist? Try Feynman Technique

  Get over the impostor syndrome by developing a strong understanding about the various Data Science topics using the Feynman Technique

  https://www.kdnuggets.com/2018/01/data-scientist-feynman-technique.html

• Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI">Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI

  A complete and unbiased comparison of the three most common Cloud Technologies for Machine Learning as a Service.

  https://www.kdnuggets.com/2018/01/mlaas-amazon-microsoft-azure-google-cloud-ai.html

• Gradient Boosting in TensorFlow vs XGBoost

  For many Kaggle-style data mining problems, XGBoost has been the go-to solution since its release in 2016. It's probably as close to an out-of-the-box machine learning algorithm as you can get today.

  https://www.kdnuggets.com/2018/01/gradient-boosting-tensorflow-vs-xgboost.html

• Topological Data Analysis for Data Professionals: Beyond Ayasdi

  We review recent developments and tools in topological data analysis, including applications of persistent homology to psychometrics and a recent extension of piecewise regression, called Morse-Smale regression.

  https://www.kdnuggets.com/2018/01/topological-data-analysis.html

• Supercharging Visualization with Apache Arrow

  Interactive visualization of large datasets on the web has traditionally been impractical. Apache Arrow provides a new way to exchange and visualize data at unprecedented speed and scale.

  https://www.kdnuggets.com/2018/01/supercharging-visualization-apache-arrow.html

• How to build a Successful Advanced Analytics Department">How to build a Successful Advanced Analytics Department

  This article presents our opinions and suggestions on how an Advanced Analytics department should operate. We hope this will be useful for those who want to implement analytics work in their company, as well as for existing departments.

  https://www.kdnuggets.com/2018/01/build-successful-advanced-analytics-department.html

• Can I Become a Data Scientist: Research into 1,001 Data Scientist Profiles">Can I Become a Data Scientist: Research into 1,001 Data Scientist Profiles

  Results from a survey include: the average data scientist is a male, with median experience on the job is 2 years. He uses R, Python, and SQL. Read for more details.

  https://www.kdnuggets.com/2017/12/research-1001-data-scientist-profiles.html

• Transitioning to Data Science: How to become a data scientist, and how to create a data science team">Transitioning to Data Science: How to become a data scientist, and how to create a data science team

  "A good data scientist in my mind is the person that takes the science part in data science very seriously; a person who is able to find problems and solve them using statistics, machine learning, and distributed computing."

  https://www.kdnuggets.com/2017/12/transitioning-data-science-become-data-scientist-data-science-team.html

• Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018">Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 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 2017 and their 2018 key trend predictions.

  https://www.kdnuggets.com/2017/12/machine-learning-ai-main-developments-2017-key-trends-2018.html

• Best Masters in Data Science and Analytics – Asia and Australia Edition

  The fourth edition of our comprehensive, unbiased survey on graduate degrees in Data Science and Analytics from around the world.

  https://www.kdnuggets.com/2017/12/best-masters-data-science-analytics-asia-australia.html

• How to Improve Machine Learning Performance? Lessons from Andrew Ng

  5 useful tips and lessons from Andrew Ng on how to improve your Machine Learning performance, including Orthogonalisation, Single Number Evaluation Metric, and Satisfying and Optimizing Metric.

  https://www.kdnuggets.com/2017/12/improve-machine-learning-performance-lessons-andrew-ng.html

• Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018">Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018

  The leading experts in the field on the main Data Science, Machine Learning, Predictive Analytics developments in 2017 and key trends in 2018.

  https://www.kdnuggets.com/2017/12/data-science-machine-learning-main-developments-trends.html

• TensorFlow for Short-Term Stocks Prediction

  In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.

  https://www.kdnuggets.com/2017/12/tensorflow-short-term-stocks-prediction.html

• Big Data: Main Developments in 2017 and Key Trends in 2018">Big Data: Main Developments in 2017 and Key Trends in 2018

  As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2017 and their 2018 key trend predictions.

  https://www.kdnuggets.com/2017/12/big-data-main-developments-2017-key-trends-2018.html

• Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras">Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras

  We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.

  https://www.kdnuggets.com/2017/11/understanding-deep-convolutional-neural-networks-tensorflow-keras.html

• Understanding Objective Functions in Neural Networks

  This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.

  https://www.kdnuggets.com/2017/11/understanding-objective-functions-neural-networks.html

• The Qualitative Side of Quantitative Research

  Kevin and Koen may buy the same brand for the same reasons. On the other hand, they may buy the same brand for different reasons, or buy different brands for the same reasons, or even different brands for different reasons. The brands they purchase and the reasons why may vary by occasion, too.

  https://www.kdnuggets.com/2017/11/qualitative-side-quantitative-research.html

• Interpreting Machine Learning Models: An Overview">Interpreting Machine Learning Models: An Overview

  This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.

  https://www.kdnuggets.com/2017/11/interpreting-machine-learning-models-overview.html

• More than the Hype: Beyond Gartner’s Hype Cycle

  Gartner publishes hype cycles across different technologies and sectors. Here we conduct detailed analysis of Gartner’s Hype Cycles.

  https://www.kdnuggets.com/2017/11/beyond-gartners-hype-cycle.html

• Getting Started with Machine Learning in One Hour!

  Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.

  https://www.kdnuggets.com/2017/11/getting-started-machine-learning-one-hour.html

• Business intuition in data science

  Data Science projects are not just use of algorithms & building models; there are other steps of the project which are equally important. Here we explain them in detail.

  https://www.kdnuggets.com/2017/10/business-intuition-data-science.html

• 5 Free Resources for Furthering Your Understanding of Deep Learning

  This post includes 5 specific video-based options for furthering your understanding of neural networks and deep learning, collectively consisting of many, many hours of insights.

  https://www.kdnuggets.com/2017/10/5-free-resources-furthering-understanding-deep-learning.html

• How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool">How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool

  In this article we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users.

  https://www.kdnuggets.com/2017/10/linkedin-personalized-recommendations-photon-ml.html

• Edge Analytics – What, Why, When, Who, Where, How?

  Edge analytics is the collection, processing, and analysis of data at the edge of a network either at or close to a sensor, a network switch or some other connected device.

  https://www.kdnuggets.com/2017/10/edge-analytics.html

• Data Science –The need for a Systems Engineering approach

  We need a greater emphasis on the Systems Engineering aspects of Data Science. I am exploring these ideas as part of my course "Data Science for Internet of Things" at the University of Oxford.

  https://www.kdnuggets.com/2017/10/data-science-systems-engineering-approach.html

• Using Machine Learning to Predict and Explain Employee Attrition">Using Machine Learning to Predict and Explain Employee Attrition

  Employee attrition (churn) is a major cost to an organization. We recently used two new techniques to predict and explain employee turnover: automated ML with H2O and variable importance analysis with LIME.

  https://www.kdnuggets.com/2017/10/machine-learning-predict-employee-attrition.html

• Top 10 Videos on Machine Learning in Finance">Top 10 Videos on Machine Learning in Finance

  Talks, tutorials and playlists – you could not get a more gentle introduction to Machine Learning (ML) in Finance. Got a quick 4 minutes or ready to study for hours on end? These videos cover all skill levels and time constraints!

  https://www.kdnuggets.com/2017/09/top-10-videos-machine-learning-finance.html

• Machine Learning Translation and the Google Translate Algorithm

  Today, we’ve decided to explore machine translators and explain how the Google Translate algorithm works.

  https://www.kdnuggets.com/2017/09/machine-learning-translation-google-translate-algorithm.html

• New-Age Machine Learning Algorithms in Retail Lending">New-Age Machine Learning Algorithms in Retail Lending

  We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.

  https://www.kdnuggets.com/2017/09/machine-learning-algorithms-lending.html

• I built a chatbot in 2 hours and this is what I learned">I built a chatbot in 2 hours and this is what I learned

  I set out to test two things: 1) building a bot is useless from a business perspective and 2) building bots is crazy tough. Here is what I learned.

  https://www.kdnuggets.com/2017/09/chatbot-2-hours-what-i-learned.html

• Putting the “Science” Back in Data Science">Putting the “Science” Back in Data Science

  The scientific method to approach a problem, in my point of view, is the best way to tackle a problem and offer the best solution. If you start your data analysis by simply stating hypotheses and applying Machine Learning algorithms, this is the wrong way.

  https://www.kdnuggets.com/2017/09/science-data-science.html

• Using GRAKN.AI to Detect Patterns in Credit Fraud Data

  The term Horn Clause Mining, similar to Rule Based Machine Learning or Inductive Logic Programming, is used to describe the inverse of this functionality. Given a large enough knowledge base, can we infer rules that describe the data accurately?

  https://www.kdnuggets.com/2017/08/grakn-ai-detect-patterns-credit-fraud-data.html

• Vital Statistics You Never Learned… Because They’re Never Taught

  Marketing scientist Kevin Gray asks Professor Frank Harrell about some important things we often get wrong about statistics.

  https://www.kdnuggets.com/2017/08/vital-statistics-never-learned-never-taught.html

• How To Write Better SQL Queries: The Definitive Guide – Part 1

  Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.

  https://www.kdnuggets.com/2017/08/write-better-sql-queries-definitive-guide-part-1.html

• Recommendation System Algorithms: An Overview

  This post presents an overview of the main existing recommendation system algorithms, in order for data scientists to choose the best one according a business’s limitations and requirements.

  https://www.kdnuggets.com/2017/08/recommendation-system-algorithms-overview.html

• What is the most important step in a machine learning project?">What is the most important step in a machine learning project?

  In any machine learning project, business understanding is very important. But in practice, it does not get enough attention. Here we explain what questions should be asked.

  https://www.kdnuggets.com/2017/08/most-important-step-machine-learning-project.html

• Deep Learning and Neural Networks Primer: Basic Concepts for Beginners

  This is a collection of introductory posts which present a basic overview of neural networks and deep learning. Start by learning some key terminology and gaining an understanding through some curated resources. Then look at summarized important research in the field before looking at a pair of concise case studies.

  https://www.kdnuggets.com/2017/08/deep-learning-neural-networks-primer-basic-concepts-beginners.html

• The Rise of GPU Databases">The Rise of GPU Databases

  The recent but noticeable shift from CPUs to GPUs is mainly due to the unique benefits they bring to sectors like AdTech, finance, telco, retail, or security/IT . We examine where GPU databases shine.

  https://www.kdnuggets.com/2017/08/rise-gpu-databases.html

• Lessons Learned From Benchmarking Fast Machine Learning Algorithms

  Boosted decision trees are responsible for more than half of the winning solutions in machine learning challenges hosted at Kaggle, and require minimal tuning. We evaluate two popular tree boosting software packages: XGBoost and LightGBM and draw 4 important lessons.

  https://www.kdnuggets.com/2017/08/lessons-benchmarking-fast-machine-learning-algorithms.html

• What Artificial Intelligence and Machine Learning Can Do—And What It Can’t">What Artificial Intelligence and Machine Learning Can Do—And What It Can’t

  I have seen situations where AI (or at least machine learning) had an incredible impact on a business—I also have seen situations where this was not the case. So, what was the difference?

  https://www.kdnuggets.com/2017/08/rapidminer-ai-machine-learning-can-do.html

• How I Used Deep Learning To Train A Chatbot To Talk Like Me">How I Used Deep Learning To Train A Chatbot To Talk Like Me

  In this post, we’ll be looking at how we can use a deep learning model to train a chatbot on my past social media conversations in hope of getting the chatbot to respond to messages the way that I would.

  https://www.kdnuggets.com/2017/08/deep-learning-train-chatbot-talk-like-me.html

• Visualizing Convolutional Neural Networks with Open-source Picasso

  Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?

  https://www.kdnuggets.com/2017/08/visualizing-convolutional-neural-networks-open-source-picasso.html

• The Key to Data Monetization

  While I have talked frequently about the concept of Analytic Profiles, I’ve never written a blog that details how Analytic Profiles work. So let’s create a “Day in the Life” of an Analytic Profile to explain how an Analytic Profile works to capture and “monetize” your analytic assets.

  https://www.kdnuggets.com/2017/07/key-data-monetization.html

• The Truth About Bayesian Priors and Overfitting

  Many of the considerations we will run through will be directly applicable to your everyday life of applying Bayesian methods to your specific domain.

  https://www.kdnuggets.com/2017/07/truth-about-bayesian-priors-overfitting.html

• Top Quora Data Science Writers and Their Best Advice, Updated

  Get some insight into tips and tricks, the future of the field, career advice, code snippets, and more from the top data science writers on Quora.

  https://www.kdnuggets.com/2017/07/top-quora-data-science-writers-best-advice-updated.html

• AI and Deep Learning, Explained Simply">AI and Deep Learning, Explained Simply

  AI can now see, hear, and even bluff better than most people. We look into what is new and real about AI and Deep Learning, and what is hype or misinformation.
   

  https://www.kdnuggets.com/2017/07/ai-deep-learning-explained-simply.html

• Emotional Intelligence for Data Science Teams

  Here are three lessons for making and demonstrating a greater business impact to your organization, according to Domino Labs most successful customers.

  https://www.kdnuggets.com/2017/07/emotional-intelligence-data-science-teams.html

• Marketing Analytics for Data Rich Environments

  A lot is changing in the world of marketing analytics. Marketing scientist Kevin Gray asks Professor Michel Wedel, a leading authority on this topic from the Robert H. Smith School of Business at the University of Maryland, what marketing researchers and data scientists most need to know about it.

  https://www.kdnuggets.com/2017/07/marketing-analytics-data-rich-environments.html

• Spotlight on the Remarkable Potential of AI in KYC (Know Your Customer)

  Most people would have heard of the headline-making tremendous achievements in artificial intelligence (AI): Systems defeating world champions in board games like GO and winning quiz shows. These are small realizations of AI, but there is a silent revolution taking place in other areas, including Regulatory Compliance in Financial Services.

  https://www.kdnuggets.com/2017/07/spotlight-remarkable-potential-ai-kyc.html

• Optimization in Machine Learning: Robust or global minimum?

  Here we discuss how convex problems are solved and optimised in machine learning/deep learning.

  https://www.kdnuggets.com/2017/06/robust-global-minimum.html

• Applying Deep Learning to Real-world Problems">Applying Deep Learning to Real-world Problems

  In this blog post I shared three learnings that are important to us at Merantix when applying deep learning to real-world problems. I hope that these ideas are helpful for other people who plan to use deep learning in their business.

  https://www.kdnuggets.com/2017/06/applying-deep-learning-real-world-problems.html

• For data scientists, now is the time to act; Forrester has insights to help you get started

  IBM, a leader in 2017 Forrester Wave Report for Predictive Analytics and Machine Learning Solutions, offers data scientists a complete toolkit, including predictive analytics and machine learning capabilities and more.

  https://www.kdnuggets.com/2017/06/ibm-forrester-insights.html

• Does Machine Learning Have a Future Role in Cyber Security?

  In the past, ML learning hasn't had as much success in cyber security as in other fields. Many early attempts struggled with problems such as generating too many false positives, which resulted mixed attitudes towards it.

  https://www.kdnuggets.com/2017/06/machine-learning-future-role-cyber-security.html

• Understanding Deep Learning Requires Re-thinking Generalization">Understanding Deep Learning Requires Re-thinking Generalization

  What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.

  https://www.kdnuggets.com/2017/06/understanding-deep-learning-rethinking-generalization.html

• How HR Managers Use Data Science to Manage Talent for Their Companies

  Data sciences can also be used by HR manager to create several estimates like the investment on talent pool, cost per hire, cost on training, and cost per employee. It provides better techniques for optimization, forecasting, and reporting.

  https://www.kdnuggets.com/2017/06/hr-managers-data-science-manage-talent.html

• Stay ahead of cyberattacks and fraud with predictive analytics

  Even as cyber criminals and swindlers step up their game, companies can use predictive analytics to stay ahead. Discover the full scope of IBM SPSS predictive analytics capabilities.

  https://www.kdnuggets.com/2017/06/ibm-spss-fraud-predictive-analytics.html

• The Artificial ‘Artificial Intelligence’ Bubble and the Future of Cybersecurity

  What’s going on now in the field of ‘AI’ resembles a soap bubble. And we all know what happens to soap bubbles eventually if they keep getting blown up by the circus clowns (no pun intended!): they burst.

  https://www.kdnuggets.com/2017/06/kaspersky-artificial-intelligence-bubble-future-cybersecurity.html

• Simplifying Data Pipelines in Hadoop: Overcoming the Growing Pains

  Moving to Hadoop is not without its challenges—there are so many options, from tools to approaches, that can have a significant impact on the future success of a business’ strategy. Data management and data pipelining can be particularly difficult.

  https://www.kdnuggets.com/2017/05/simplify-data-pipelines-hadoop.html

• Teaching the Data Science Process

  Understanding the process requires not only wide technical background in machine learning but also basic notions of businesses administration; here I will share my experience on teaching the data science process.

  https://www.kdnuggets.com/2017/05/teaching-data-science-process.html

• Must-Know: What are common data quality issues for Big Data and how to handle them?">Must-Know: What are common data quality issues for Big Data and how to handle them?

  Let's have a look at common quality issues facing Big Data in terms of the key characteristics of Big Data – Volume, Velocity, Variety, Veracity, and Value.

  https://www.kdnuggets.com/2017/05/must-know-common-data-quality-issues-big-data.html

• The Internet of Things in the Cloud

  Cloud computing is the next evolutionary step in Internet-based computing, which provides the means for delivering ICT resources as a service. Internet-of-Things can benefit from the scalability, performance and pay-as-you-go nature of cloud computing infrastructures.

  https://www.kdnuggets.com/2017/05/internet-of-things-iot-cloud.html

• Data Science & Machine Learning Platforms for the Enterprise

  A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.

  https://www.kdnuggets.com/2017/05/data-science-machine-learning-platforms-enterprise.html

• Building, Training, and Improving on Existing Recurrent Neural Networks

  In this post, we’ll provide a short tutorial for training a RNN for speech recognition, including code snippets throughout.

  https://www.kdnuggets.com/2017/05/building-training-improving-existing-recurrent-neural-networks.html

• Machine Learning overtaking Big Data?">Machine Learning overtaking Big Data?

  Is Machine Learning is overtaking Big Data?! We also examine trends for several more related and popular buzzwords, and see how BD, ML. Artificial Intelligence, Data Science, and Deep Learning rank.

  https://www.kdnuggets.com/2017/05/machine-learning-overtaking-big-data.html

• 42 Essential Quotes by Data Science Thought Leaders

  42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.

  https://www.kdnuggets.com/2017/05/42-essential-quotes-data-science-thought-leaders.html

• How to Fail with Artificial Intelligence: 9 creative ways to make your AI startup fail

  This post summarizes nine creative ways to condemn almost any AI startup to bankruptcy. I focus on data science and machine learning startups, but the lessons on what to avoid can easily be applied to other industries.

  https://www.kdnuggets.com/2017/05/fail-artificial-intelligence-creative-ways.html

• Deep Learning – Past, Present, and Future">Deep Learning – Past, Present, and Future

  There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.

  https://www.kdnuggets.com/2017/05/deep-learning-big-deal.html

• The Analytics of Emotion and Depression

  Analytics can be used to provide a boost to the cure of depression. How analytics is being adopted by companies like Microsoft, Facebook to handle and detect vulnerable targets of depression.

  https://www.kdnuggets.com/2017/04/analytics-emotion-depression.html

• AI & Machine Learning Black Boxes: The Need for Transparency and Accountability

  When something goes wrong, as it inevitably does, it can be a daunting task discovering the behavior that caused an event that is locked away inside a black box where discoverability is virtually impossible.

  https://www.kdnuggets.com/2017/04/ai-machine-learning-black-boxes-transparency-accountability.html

• Must-Know: When can parallelism make your algorithms run faster? When could it make your algorithms run slower?

  Efficient implementation is key to achieving the benefits of parallelization, even though parallelism is a good idea when the task can be divided into sub-tasks that can be executed independent of each other without communication or shared resources.

  https://www.kdnuggets.com/2017/04/must-know-parallelism-algorithms.html

• Difference Between Big Data and Internet of Things

  If you cannot manage real-time streaming data and make real-time analytics and real-time decisions at the edge, then you are not doing IOT or IOT analytics, in my humble opinion. So what is required to support these IOT data management and analytic requirements?

  https://www.kdnuggets.com/2017/04/difference-big-data-internet-of-things.html

• Awesome Deep Learning: Most Cited Deep Learning Papers">Awesome Deep Learning: Most Cited Deep Learning Papers

  This post introduces a curated list of the most cited deep learning papers (since 2012), provides the inclusion criteria, shares a few entry examples, and points to the full listing for those interested in investigating further.

  https://www.kdnuggets.com/2017/04/awesome-deep-learning-most-cited-papers.html

• The Value of Exploratory Data Analysis

  In this post, we will give a high level overview of what exploratory data analysis (EDA) typically entails and then describe three of the major ways EDA is critical to successfully model and interpret its results.

  https://www.kdnuggets.com/2017/04/value-exploratory-data-analysis.html

• How Big Data Helps Today’s Airlines Operate

  Companies all over the world have placed a lot of value on getting more insights from big data analytics. That’s not without good reason.

  https://www.kdnuggets.com/2017/04/big-data-airlines-operate.html

• Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions">Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions

  Who leads in Data Science, Machine Learning, and Predictive Analytics? We compare the latest Forrester and Gartner reports for this industry for 2017 Q1, identify gainers and losers, and strong leaders vs contenders.

  https://www.kdnuggets.com/2017/04/forrester-gartner-data-science-platforms-machine-learning.html

• Top mistakes data scientists make when dealing with business people">Top mistakes data scientists make when dealing with business people

  There are no cover articles praising the fails of the many data scientists that don’t live up to the hype. Here we examine 3 typical mistakes and how to avoid them.

  https://www.kdnuggets.com/2017/04/top-mistakes-data-scientists-make-business.html

• A Brief History of Artificial Intelligence">A Brief History of Artificial Intelligence

  This post is a brief outline of what happened in artificial intelligence in the last 60 years. A great place to start or brush up on your history.
   
   

  https://www.kdnuggets.com/2017/04/brief-history-artificial-intelligence.html

• Putting Together A Full-Blooded AI Maturity Model

  Here is a proposed “7A” model that is useful enough to capture of the core of what AI offers without falsely implying there is a static body of best practices in this area.

  https://www.kdnuggets.com/2017/04/ai-maturity-model.html

• Getting Started with Deep Learning

  This post approaches getting started with deep learning from a framework perspective. Gain a quick overview and comparison of available tools for implementing neural networks to help choose what's right for you.

  https://www.kdnuggets.com/2017/03/getting-started-deep-learning.html

• Key Takeaways from Strata + Hadoop World 2017 San Jose, Day 1">Key Takeaways from Strata + Hadoop World 2017 San Jose, Day 1

  The focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand.

  https://www.kdnuggets.com/2017/03/strata-hadoop-san-jose-key-takeaways.html

• How to think like a data scientist to become one

  The author went from securities analyst to Head of Data Science at Amazon. He describes what he learned in his journey and gives 4 useful rules based on his experience.

  https://www.kdnuggets.com/2017/03/think-like-data-scientist-become-one.html

• What Is Data Science, and What Does a Data Scientist Do?">What Is Data Science, and What Does a Data Scientist Do?

  This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose, and given that the ideal experience and skill set is relatively rare to find in one individual.

  https://www.kdnuggets.com/2017/03/data-science-data-scientist-do.html

• What Top Firms Ask: 100+ Data Science Interview Questions

  Check this out: A topic wise collection of 100+ data science interview questions from top companies.

  https://www.kdnuggets.com/2017/03/top-firms-100-data-science-interview-questions.html

• Analytics 101: Comparing KPIs

  Different business units in the organisation have different behaviours (e.g. turnover rate) and they can’t be compared with each other. So, how can we tell whether the changes in their behaviour are reasons for concern?

  https://www.kdnuggets.com/2017/03/analytics-101-comparing-kpis.html

• 17 More Must-Know Data Science Interview Questions and Answers, Part 3">17 More Must-Know Data Science Interview Questions and Answers, Part 3

  The third and final part of 17 new must-know Data Science interview questions and answers covers A/B testing, data visualization, Twitter influence evaluation, and Big Data quality.
   
   

  https://www.kdnuggets.com/2017/03/17-data-science-interview-questions-answers-part-3.html

• Toward Increased k-means Clustering Efficiency with the Naive Sharding Centroid Initialization Method

  What if a simple, deterministic approach which did not rely on randomization could be used for centroid initialization? Naive sharding is such a method, and its time-saving and efficient results, though preliminary, are promising.

  https://www.kdnuggets.com/2017/03/naive-sharding-centroid-initialization-method.html

• The Challenges of Building a Predictive Churn Model

  Unlike other data science problems, there is no one method for predicting which customers are likely to churn in the next month. Here we review the most popular approaches.

  https://www.kdnuggets.com/2017/03/datascience-building-predictive-churn-model.html

• Neuroscience for Data Scientists: Understanding Human Behaviour

  Neuroscience is very complex and advanced study of brain and people often misuse this term. Here we try to explain neuroscience terminologies and use of data science for such studies.

  https://www.kdnuggets.com/2017/03/neuroscience-data-science-human-behaviour.html

• Gartner Data Science Platforms – A Deeper Look

  Thomas Dinsmore critical examination of Gartner 2017 MQ of Data Science Platforms, including vendors who out, in, have big changes, Hadoop and Spark integration, open source software, and what Data Scientists actually use.

  https://www.kdnuggets.com/2017/03/thomaswdinsmore-gartner-data-science-platforms.html

• Greed, Fear, Game Theory and Deep Learning

  The most advanced kind of Deep Learning system will involve multiple neural networks that either cooperate or compete to solve problems. The core problem of a multi-agent approach is how to control its behavior.

  https://www.kdnuggets.com/2017/03/greed-fear-game-theory-deep-learning.html