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Search results for Programming Genetic Algorithms

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  • Data Science Programming: Python vs R

    …for statistical computing. At DeZyre, our career counsellors often get questions from prospective students as to what should they learn first Python programming or R programming. If you are unsure on which programming language to learn first then you are on the right page. Python and R language…

    https://www.kdnuggets.com/2015/10/data-science-programming-python-vs-r.html

  • The Machine Learning Algorithms Used in Self-Driving Cars">Gold Blog, May 2017The Machine Learning Algorithms Used in Self-Driving Cars

    …classification The Object Localization and Prediction of Movement The machine learning algorithms are loosely divided into 4 classes: decision matrix algorithms, cluster algorithms, pattern recognition algorithms and regression algorithms. One category of the machine learning algorithms can be…

    https://www.kdnuggets.com/2017/06/machine-learning-algorithms-used-self-driving-cars.html

  • The Foundations of Algorithmic Bias

    …ed with this information, we’ll then introduce a catalogue of fundamental ways that things can go wrong. [ALGORITHMS] To start, let’s briefly explain algorithms. Algorithms are the instructions that tell your computer precisely how to accomplish some task. Typically, this means how to take some…

    https://www.kdnuggets.com/2016/11/foundations-algorithmic-bias.html

  • Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms

    ...iment with different variations of the core idea we circumvent licensing issues (e.g., Linux vs. Unix) or platform restrictions we want to invent new algorithms or implement algorithms no one has implemented/shared yet we are not satisfied with the API and/or we want to integrate it more...

    https://www.kdnuggets.com/2017/01/great-collection-clean-machine-learning-algorithms.html

  • Time Complexity: How to measure the efficiency of algorithms

    ...need to compare the efficiency of different approaches to pick up the right one. Now, as you may know, computers are able to solve problems based on algorithms. Algorithms are procedures or instructions (set of steps) that tell a computer what to do and how to do it. Nowadays, they evolved so much...

    https://www.kdnuggets.com/2020/06/time-complexity-measure-efficiency-algorithms.html

  • Top Algorithms and Methods Used by Data Scientists">Gold BlogTop Algorithms and Methods Used by Data Scientists

    ...-47% 0.50 22 Graph / Link / Social Network Analysis Z 15% 14% 8.0% -0.08 23 Factor Analysis U 14% 19% -23.8% 0.14 24 Bayesian networks S 13% -0.10 25 Genetic algorithms Z 8.8% 9.3% -6.0% 0.83 26 Survival Analysis Z 7.9% 9.3% -14.9% -0.15 27 EM U 6.6% -0.19 28 Other methods Z 4.6% -0.06 29 Uplift...

    https://www.kdnuggets.com/2016/09/poll-algorithms-used-data-scientists.html

  • Introduction to Functional Programming in Python">Gold BlogIntroduction to Functional Programming in Python

    ...ional functions, and the concept of partials. Overall, we showed that Python provides a programmer with the tools to easily switch between functional programming and object-oriented programming. If you’ve enjoyed functional programming, our newest course: Building a Data Pipeline uses...

    https://www.kdnuggets.com/2018/02/introduction-functional-programming-python.html

  • Linear Programming and Discrete Optimization with Python using PuLP

    ...were allowed to assume any real number value. Integer programming forces some or all of the variables to assume only integer values. In fact, integer programming is a harder computational problem than linear programming. Integer variables make an optimization problem non-convex, and therefore far...

    https://www.kdnuggets.com/2019/05/linear-programming-discrete-optimization-python-pulp.html

  • Genetic Algorithm Key Terms, Explained

    ...on the other hand, would take an amount of time so unreasonable as to never complete. It turns out that approximating such optimization problems with genetic algorithms is a sensible approach, resulting in reasonable approximations. Genetic algorithms have had a place in the machine learning...

    https://www.kdnuggets.com/2018/04/genetic-algorithm-key-terms-explained.html

  • Mathematical programming —  Key Habit to Build Up for Advancing Data Science">Gold BlogMathematical programming —  Key Habit to Build Up for Advancing Data Science

    ...a statistically sound manner.   Summary (and a challenge for the reader)   We demonstrate what it means to develop a habit of mathematical programming. Essentially, it is thinking in terms of programming to test out the mathematical properties or data patterns that you are developing in...

    https://www.kdnuggets.com/2019/05/mathematical-programming-key-habit-advancing-data-science.html

  • Gold Mine or Blind Alley? Functional Programming for Big Data & Machine Learning

    ...s of its opposite, imperative programming, and introduces a few new ideas, several of which have been subsequently adopted by many popular imperative programming languages. What You Can Do First-Order Functions Functional programming supports first order functions. These functions can be passed as...

    https://www.kdnuggets.com/2015/04/functional-programming-big-data-machine-learning.html

  • Top 10 Machine Learning Algorithms for Beginners">Platinum BlogTop 10 Machine Learning Algorithms for Beginners

    ...training data, which were stored in memory. ‘Instance-based learning’ does not create an abstraction from specific instances.   II. Types of ML algorithms   There are 3 types of ML algorithms: 1. Supervised learning: Supervised learning can be explained as follows: use labeled training...

    https://www.kdnuggets.com/2017/10/top-10-machine-learning-algorithms-beginners.html

  • Feature Reduction using Genetic Algorithm with Python

    ...-algorithm-ahmed-gad/ /2018/03/introduction-optimization-with-genetic-algorithm.html https://towardsdatascience.com/introduction-to-optimization-with-genetic-algorithm-2f5001d9964b Genetic Algorithm (GA) Optimization – Step-by-Step Example...

    https://www.kdnuggets.com/2019/03/feature-reduction-genetic-algorithm-python.html

  • What Do Frameworks Offer Data Scientists that Programming Languages Lack?

    ...ithms   One of the most important parts of using a programming language is understanding the algorithms and making sure the code fits into those algorithms. However, algorithms can be limited by language because they are actually defined by the frameworks. Changing and establishing algorithms...

    https://www.kdnuggets.com/2017/05/frameworks-offer-data-scientists-programming-languages-lack.html

  • An easy guide to choose the right Machine Learning algorithm">Silver BlogAn easy guide to choose the right Machine Learning algorithm

    ...not have a response variable. Such algorithms try to find the intrinsic pattern and hidden structures in the data. Clustering and Dimension Reduction algorithms are types of unsupervised learning algorithms. The below infographic simply explains Regression, classification, anomaly detection, and...

    https://www.kdnuggets.com/2020/05/guide-choose-right-machine-learning-algorithm.html

  • Free From MIT: Intro to Computer Science and Programming in Python">Gold BlogFree From MIT: Intro to Computer Science and Programming in Python

    ...yone else learning to program). An understanding of computer science principles, computational approaches to problem solving, and the fundamentals of programming, all independent of implementation programming language, should be the goal of anyone with a true desire to really learn how to code....

    https://www.kdnuggets.com/2020/09/free-mit-intro-computer-science-programming-python.html

  • Artificial Neural Networks Optimization using Genetic Algorithm with Python">Platinum BlogArtificial Neural Networks Optimization using Genetic Algorithm with Python

    ...can find all details within this book. The source code used in this tutorial is available in my GitHub page here: https://github.com/ahmedfgad/NeuralGenetic   Read More about Genetic Algorithm   Before starting this tutorial, I recommended reading about how the genetic algorithm works...

    https://www.kdnuggets.com/2019/03/artificial-neural-networks-optimization-genetic-algorithm-python.html

  • Key Algorithms and Statistical Models for Aspiring Data Scientists">Gold BlogKey Algorithms and Statistical Models for Aspiring Data Scientists

    ...ting ensembles (which form the basis of gradient boosting and XGBoost algorithms) 8) Optimization algorithms for parameter tuning or design projects (genetic algorithms, quantum-inspired evolutionary algorithms, simulated annealing, particle-swarm optimization) 9) Topological data analysis tools,...

    https://www.kdnuggets.com/2018/04/key-algorithms-statistical-models-aspiring-data-scientists.html

  • Machine Learning Algorithms: Which One to Choose for Your Problem">Silver BlogMachine Learning Algorithms: Which One to Choose for Your Problem

    ...YouTube channel about AI for beginners with great tutorials and examples   Bio: Daniil Korbut is a Junior Data Scientist at Statsbot. Original. Reposted with permission. Related: Top 10 Machine Learning Algorithms for Beginners Understanding Machine Learning Algorithms Machine Learning...

    https://www.kdnuggets.com/2017/11/machine-learning-algorithms-choose-your-problem.html

  • The 10 Algorithms Machine Learning Engineers Need to Know">2016 Gold BlogThe 10 Algorithms Machine Learning Engineers Need to Know

    ...er groups. Clustering Algorithms Every clustering algorithm is different, and here are a couple of them: Centroid-based algorithms Connectivity-based algorithms Density-based algorithms Probabilistic Dimensionality Reduction Neural networks / Deep Learning 8. Principal Component Analysis: PCA is a...

    https://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html

  • Which Machine Learning Algorithm Should I Use?">Gold Blog, May 2017Which Machine Learning Algorithm Should I Use?

    ...e sheet. Since the cheat sheet is designed for beginner data scientists and analysts, we will make some simplified assumptions when talking about the algorithms. The algorithms recommended here result from compiled feedback and tips from several data scientists and machine learning experts and...

    https://www.kdnuggets.com/2017/06/which-machine-learning-algorithm.html

  • Object-oriented programming for data scientists: Build your ML estimator">Gold BlogObject-oriented programming for data scientists: Build your ML estimator

    ...ience/AI/ML do not help either. They try to give the budding data scientists the flavor of a mixed soup of statistics, numerical analysis, scientific programming, machine learning (ML) algorithms, visualization, and perhaps even a bit of web framework to deploy those ML models. Almost all of these...

    https://www.kdnuggets.com/2019/08/object-oriented-programming-data-scientists-estimator.html

  • 10 Algorithm Categories for AI, Big Data, and Data Science

    ...ertise for how our business works. They manage us and our efforts so that we and the business stay healthy, productive, and financially strong. These algorithms orchestrate us and all the other algorithms to help us meet our strategic long-term objectives. Bio: Chris Pehura is a management...

    https://www.kdnuggets.com/2016/07/10-algorithm-categories-data-science.html

  • How to count Big Data: Probabilistic data structures and algorithms

    ...want to read the original papers, please take a look at the list of references that follows this article. Resources Probabilistic Data Structures and Algorithms for Big Data Applications book Probabilistic Counting Algorithms for Data Base Applications article by Philippe Flajolet and G. Nigel...

    https://www.kdnuggets.com/2019/08/count-big-data-probabilistic-data-structures-algorithms.html

  • How The Algorithm Economy And Containers Are Changing The Apps

    …tracting actionable insights from that data. As a result, these companies are using algorithms to create value, and impact millions of people a day. “Algorithms are where the real value lies,” Sondergaard said. “Algorithms define action.” For many technology companies, they’ve done a good job of…

    https://www.kdnuggets.com/2016/02/how-algorithm-economy-containers-are-changing-apps.html

  • 50 Must-Read Free Books For Every Data Scientist in 2020">Silver Blog50 Must-Read Free Books For Every Data Scientist in 2020

    ...ort to implement, but they exhaust very fast with the amount of data added. This book is probably the best introduction to metaheuristic methods like Genetic Algorithms, Hill Climbing, Co-Evolution, and (basic) Reinforcement Learning.   11. Machine Learning in Python: Main Developments and...

    https://www.kdnuggets.com/2020/03/50-must-read-free-books-every-data-scientist-2020.html

  • Ensemble Methods: Elegant Techniques to Produce Improved Machine Learning Results

    ...entations of stacking where training dataset is splitted. Below you can see a pseudocode where the training dataset is split before training the base algorithms: base_algorithms = [logistic_regression, decision_tree_classification, . . . ] #for classification stacking_train_dataset =...

    https://www.kdnuggets.com/2016/02/ensemble-methods-techniques-produce-improved-machine-learning.html

  • Genetic Algorithm Implementation in Python">Silver BlogGenetic Algorithm Implementation in Python

    ...titled “Introduction to Optimization with Genetic Algorithm” found in these links: LinkedIn: https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad/ KDnuggets: https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html TowardsDataScience:...

    https://www.kdnuggets.com/2018/07/genetic-algorithm-implementation-python.html

  • Using Genetic Algorithm for Optimizing Recurrent Neural Networks

    ...in the comment section below.   References: Understanding LSTM Networks, by Christopher Olah Recurrent Neural Networks in Tensorflow I, by R2RT Genetic Algorithms: Theory and Applications, by Ulrich Bodenhofer Chapter 9, Genetic Algorithms of Machine Learning book, by Tom M. Mitchell  ...

    https://www.kdnuggets.com/2018/01/genetic-algorithm-optimizing-recurrent-neural-network.html

  • 2 Things You Need to Know about Reinforcement Learning – Computational Efficiency and Sample Efficiency

    ...3 million timesteps, a far cry from Levine’s estimate of 1 billion.   On the other hand, a related paper published by Uber AI labs that applied genetic algorithms to Atari games found sample efficiencies that were around the 1 billion time steps mark, about 5 times greater than the...

    https://www.kdnuggets.com/2020/04/2-things-reinforcement-learning.html

  • Netflix: Manager, Content Programming Science & Algorithms

    ...films, stand-up, and documentaries, in addition to licensing thousands of titles, for its 90 million global users. Our data scientists on the Content Programming Science & Algorithms Team have the goal of optimizing this huge catalog so as to always have ‘something for everyone’ on the service....

    https://www.kdnuggets.com/jobs/17/02-27-netflix-manager-content-programming-science-algorithms.html

  • The Great Algorithm Tutorial Roundup

    ...ions, etc. As a result, we thought that the following resources may be useful to readers looking to plug holes in their knowledge of these particular algorithms, as well as machine learning algorithms in general. Algorithm Basics   For algorithms basics, including many of the top reported...

    https://www.kdnuggets.com/2016/09/great-algorithm-tutorial-roundup.html

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

    ...ns. As a result, individual solutions will undergo a number of variations to generate new solutions. We will move to GA and apply these terms.   Genetic Algorithm (GA) The genetic algorithm is a random-based classical evolutionary algorithm. By random here we mean that in order to find a...

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

  • Artificial Intelligence for Precision Medicine and Better Healthcare

    ...specifically in autism spectrum disorder, epileptic encephalopathy, intellectual disability, attention deficit hyperactivity disorder (ADHD) and rare genetic disorders. (Mohammed et al., 2019). AI algorithms can create an impact in 4 complex unresolved problems in neurodevelopmental disorders as...

    https://www.kdnuggets.com/2020/09/artificial-intelligence-precision-medicine-better-healthcare.html

  • Why Learn Python? Here Are 8 Data-Driven Reasons

    ...u prefer Python as your choice of study, you must be able to say why you prefer Python. Python is considered as one of the most in-demand and popular programming languages in the world of programming languages. In a recent Stack Overflow survey, Python has taken over C, C++, Java, and has made its...

    https://www.kdnuggets.com/2020/07/learn-python-8-data-driven-reasons.html

  • Data Science for Managers: Programming Languages">Silver BlogData Science for Managers: Programming Languages

    ...e for your specific project. In this article, we are going to talk about popular languages for Data Science and briefly describe each of them.   Programming languages   Python   Python is a modern, general-purpose, high-level, dynamic programming language. It can be used for...

    https://www.kdnuggets.com/2019/11/data-science-managers-programming-languages.html

  • Best Data Science Online Courses

    …b Apps in R with Shiny $119 Data Mining with R: Go from Beginner to Advanced! $99 Applied Multivariate Analysis with R $99 SAS Course Title Price SAS programming for beginners $19 Clinical SAS Programming(CDISC) $250 Certified SAS Base Programmer $299 Advanced SAS $29 Logistic Regression (Credit…

    https://www.kdnuggets.com/2015/10/best-data-science-online-courses.html

  • Evolving Deep Neural Networks

    ...n terms of accuracy, but it developed better mid-time performance and smaller models (further description on this case is given below). Even so, both genetic algorithms and DNN are known for demanding high resources, in the order of thousands of GPU days. Therefore, for NAS to be an affordable...

    https://www.kdnuggets.com/2019/06/evolving-deep-neural-networks.html

  • Top Stories, Feb 26 – Mar 4: Introduction to Functional Programming in Python; A Tour of The Top 10 Algorithms for Machine Learning Newbies

    ...urce Projects – Feb 20, 2018. 5 Fantastic Practical Machine Learning Resources – Feb 06, 2018. Introduction to Python Ensembles – Feb 09, 2018. Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch – Feb 20, 2018. Introduction to Functional...

    https://www.kdnuggets.com/2018/03/top-news-week-0226-0304.html

  • Ten Machine Learning Algorithms You Should Know to Become a Data Scientist">Silver BlogTen Machine Learning Algorithms You Should Know to Become a Data Scientist

    ...r versions like CART trees were once used for simple data, but with bigger and larger dataset, the bias-variance tradeoff needs to solved with better algorithms. The two common decision trees algorithms used nowadays are Random Forests (which build different classifiers on a random subset of...

    https://www.kdnuggets.com/2018/04/10-machine-learning-algorithms-data-scientist.html

  • How to Become a Data Scientist: The Definitive Guide">Silver Blog, Aug 2017How to Become a Data Scientist: The Definitive Guide

    ...ogramming, you’ll see that some libraries tend to handle a lot of the linear algebra tasks for you. But it is still important to understand how these algorithms work!   The Programming   “Measuring programming progress by lines of code is like measuring aircraft building progress by...

    https://www.kdnuggets.com/2017/08/become-data-scientist-definitive-guide.html

  • I Designed My Own Machine Learning and AI Degree

    ...zation by The University of Melbourne (Coursera) [$49] *optional Covers: how to solve complex search problems with discrete optimization concepts and algorithms, constraint programming, branch and bound, linear programming (LP), mixed-integer programming   Computer Science Foundation  ...

    https://www.kdnuggets.com/2020/05/designed-machine-learning-ai-degree.html

  • There is No Free Lunch in Data Science">Silver BlogThere is No Free Lunch in Data Science

    ...g bias) about the relationships between the predictor and target variables, introducing bias into the model. The assumptions made by machine learning algorithms mean that some algorithms will fit certain data sets better than others. It also (by definition) means that there will be as many data...

    https://www.kdnuggets.com/2019/09/no-free-lunch-data-science.html

  • Building Recommender systems with Azure Machine Learning service

    ...em, data scientists will often turn to more commonly known algorithms to alleviate the time and costs needed to choose and test more state-of-the-art algorithms, even if these more advanced algorithms may be a better fit for the project/data set. The recommender GitHub repository provides a library...

    https://www.kdnuggets.com/2019/05/recommender-systems-azure-machine-learning.html

  • Coding Random Forests® in 100 lines of code*

    ...and neural networks. At STATWORX we discuss algorithms daily to evaluate their benefits for a specific project. In any case, understanding these core algorithms is key to most machine learning algorithms in the literature.   Why bother writing from scratch?   While I like reading machine...

    https://www.kdnuggets.com/2019/08/coding-random-forests.html

  • Accelerating Algorithms: Considerations in Design, Algorithm Choice and Implementation

    ...h keeping in mind as part of your “efficiency arsenal”. Just be sure to quantify your algorithm’s performance first! To learn more about accelerating algorithms and get more insights into Intel’s MKL optimizations, watch my webinar, “Accelerating Your Algorithms in Production with Python and...

    https://www.kdnuggets.com/2017/12/accelerating-algorithms-design-choice-implementation.html

  • Top KDnuggets tweets, Jan 19-20: 15 programming languages you need to know in 2015; R Programming fun: writing a Twitter bot

    ...Pictures that State-of-the-Art #AI Can’t Recognize (yet) #Vision #DeepLearning t.co/OsUgXAjS8C t.co/4EHQybz6Wj Top 10 most engaging Tweets 15 #programming languages you need to know in 2015 – #Java #PHP #C++ #Python #SQL #R t.co/ZcScPzuevS #rstats t.co/E1PCvEUT7G #Facebook open sources...

    https://www.kdnuggets.com/2015/01/top-tweets-jan19-20.html

  • Summer School: Constraint Programming Data Mining, Sicily

    ...e hand, and constraint programming and optimization on the other hand. If successful, this would change the face of data mining as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to improve the formulation and solution...

    https://www.kdnuggets.com/2014/06/summer-school-constraint-programming-data-mining-sicily.html

  • Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference">Gold BlogIntroducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference

    ...ce efficiency. While many PPLs are syntactically rich that can be used to represent almost any model, they tend to support a limited set of inference algorithms that converge prohibitively slowly. Other PPLs are rich in inference algorithms but remained constrained to specific domains making it...

    https://www.kdnuggets.com/2019/07/introducing-gen-language-progammable-inference.html

  • Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills">Silver BlogModern Data Science Skills: 8 Categories, Core Skills, and Hot Skills

    ...g Software Development 33.9% 31.8% 0.94 Tableau Business & Communication 31.8% 35.5% 1.1 XGBoost Data Science & ML Tools 29.5% 34.6% 1.2 Java Programming Lang 22.2% 22.3% 1.0 C++ Programming Lang 21.0% 24.9% 1.2 MATLAB Programming Lang 18.8% 16.1% 0.86 Let us know what we missed and what...

    https://www.kdnuggets.com/2020/09/modern-data-science-skills.html

  • Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities

    ...nvestigation and implementation of k-nearest neighbors, naive Bayes, regression trees, and others, you’ll explore a variety of machine learning algorithms and practice selecting the best model, considering key principles of how to implement those models effectively. You will also have an...

    https://www.kdnuggets.com/2020/09/online-certificates-ai-data-science-machine-learning-top.html

  • Weapons of Math Destruction, Ethical Matrix, Nate Silver and more Highlights from the Data Science Leaders Summit

    ...pletely new and untapped way. Businesses can also tap into completely new ideas by harnessing Data Science in more effective ways. For example: Using genetic algorithms to design completely new clothes by recombining existing styles.     Provide the right environment for Data Science to...

    https://www.kdnuggets.com/2018/07/domino-data-science-leaders-summit-highlights.html

  • Using Predictive Algorithms to Track Real Time Health Trends

    ...-time health dashboard for tracking a person’s blood pressure readings, do time series analysis, and then graph the trends over time using predictive algorithms. This tutorial is the starting point for creating your own personal health dashboard using time series algorithms and predictive APIs....

    https://www.kdnuggets.com/2016/11/predictive-algorithms-track-real-time-health-trends.html

  • Getting Up Close and Personal with Algorithms">Silver Blog, March 2017Getting Up Close and Personal with Algorithms

    ...re or less, a way for computers to learn things without being specifically programmed. But how does that actually happen? The answer is, in one word, algorithms. Algorithms are sets of rules that a computer is able to follow. Think about how you learned to do long division — maybe you learned...

    https://www.kdnuggets.com/2017/03/dataiku-top-algorithms.html

  • Platinum Blog10 Great Python Resources for Aspiring Data Scientists">Silver BlogPlatinum Blog10 Great Python Resources for Aspiring Data Scientists

    ...as how to use functional programming in Python. You’ll also learn about list comprehensions and other forms of comprehensions.   4. Asynchronous Programming in Python: A Walkthrough Before asyncio (sometimes written as async IO), which is a concurrent programming design in Python, there were...

    https://www.kdnuggets.com/2019/09/10-great-python-resources-aspiring-data-scientists.html

  • Python Data Science for Beginners">Silver BlogPython Data Science for Beginners

    ...aring problem. He is interested in product marketing, and analytics. His latest venture Hackr.io recommends the best Data Science tutorial and online programming courses for every programming language. All the tutorials are submitted and voted by the programming community. Original. Reposted with...

    https://www.kdnuggets.com/2019/02/python-data-science-beginners.html

  • Platinum BlogHow to Become a (Good) Data Scientist – Beginner Guide">Silver BlogPlatinum BlogHow to Become a (Good) Data Scientist – Beginner Guide

    ...to explain values, but to predict future trends. In other words, we use Data Science for: Data Science is a newly developed blend of machine learning algorithms, statistics, business intelligence, and programming. This blend helps us reveal hidden patterns from the raw data, which in turn provides...

    https://www.kdnuggets.com/2019/10/good-data-scientist-beginner-guide.html

  • Top 10 Data Mining Algorithms, Explained

    ...n and regression trees AdaBoost Tutorial Ton of References Now it’s your turn… Now that I’ve shared my thoughts and research around these data mining algorithms, I want to turn it over to you. Are you going to give data mining a try? Which data mining algorithms have you heard of but weren’t on the...

    https://www.kdnuggets.com/2015/05/top-10-data-mining-algorithms-explained.html

  • Artificial Intelligence, Deep Learning, and Neural Networks, Explained">Silver BlogArtificial Intelligence, Deep Learning, and Neural Networks, Explained

    ...semi-supervised learning problems. For neural network-based deep learning models, the number of layers are greater than in so-called shallow learning algorithms. Shallow algorithms tend to be less complex and require more up-front knowledge of optimal features to use, which typically involves...

    https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html

  • Practical Hyperparameter Optimization

    ...call f1-score support 0 0.91 0.98 0.95 116 1 0.98 0.91 0.95 124 accuracy 0.95 240 macro avg 0.95 0.95 0.95 240 weighted avg 0.95 0.95 0.95 240   Genetic Algorithms Genetic Algorithms tries to apply natural selection mechanisms to Machine Learning contexts. They are inspired by the...

    https://www.kdnuggets.com/2020/02/practical-hyperparameter-optimization.html

  • Software engineering fundamentals for Data Scientists

    ...ready using Python, but have none or little knowledge about object-oriented programming, I strongly recommend these two free courses: Object-Oriented Programming in Python at Datacamp Intro to Object-Oriented Programming (OOP) in Python at https://realpython.com/   The importance of...

    https://www.kdnuggets.com/2020/06/software-engineering-fundamentals-data-scientists.html

  • Autograd: The Best Machine Learning Library You’re Not Using?">Silver BlogAutograd: The Best Machine Learning Library You’re Not Using?

    ...euroevolution refers to the optimization of neural networks by selection, without explicit differentiation or gradient descent.   Differentiable programming is a broader programming paradigm that encompasses most of deep learning, excepting gradient-free optimization methods such as...

    https://www.kdnuggets.com/2020/09/autograd-best-machine-learning-library-not-using.html

  • Why Implement Machine Learning Algorithms From Scratch?

    ...iment with different variations of the core idea we circumvent licensing issues (e.g., Linux vs. Unix) or platform restrictions we want to invent new algorithms or implement algorithms no one has implemented/shared yet we are not satisfied with the API and/or we want to integrate it more...

    https://www.kdnuggets.com/2016/05/implement-machine-learning-algorithms-scratch.html

  • 3 Reasons Why We Are Far From Achieving Artificial General Intelligence

    ...arning to learn when its performance at a new task improves with experience and the number of task. The goal of meta-learning is therefore to develop algorithms that are able to rapidly and efficiently adapt to new tasks. As a result, meta-learning algorithms are usually better at generalizing out...

    https://www.kdnuggets.com/2020/04/3-reasons-far-from-artificial-general-intelligence.html

  • 5 Things You Need To Know About Data Science

    I am frequently asked questions about Data Science, so here my answers to some frequent questions and 5 useful things to know about Data Science and Data Scientists. 1. Business Intelligence, Business Analytics, Data Science, Data Analytics, Data Mining, Predictive Analytics – what are the...

    https://www.kdnuggets.com/2018/02/5-things-about-data-science.html

  • Genetic Programming, Rough Sets, Fuzzy Logic, and other classification software

    ...optimization for Excel. GAtree, genetic induction and visualization of decision trees (free and commercial versions available). Genalytics GA3, uses genetic programming to dynamically build predictive models. GenIQ Model, automatically data mines for new variables, performs variable selection, and...

    https://www.kdnuggets.com/software/classification-other.html

  • Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples

    ...s and valleys, so that gradient descent and hill-climbing will typically converge only to a local optimum, not to the global optimum. Techniques like genetic algorithms, particle swarm optimization (when the gradient cannot be calculated), and other evolutionary computing methods are used to...

    https://www.kdnuggets.com/2019/01/data-scientist-dilemma-cold-start-machine-learning.html

  • Microsoft F# for Big Data programming

    ...iginated at Microsoft Research around 2004. It was designed by Don Syme, principal researcher at the company. The language is geared to data-oriented programming as well as parallel programming and algorithmic development. F# 3.0, featuring support for large-scale schematized data and APIs, was...

    https://www.kdnuggets.com/microsoft-f-for-big-data-programming.html

  • What is the Best Python IDE for Data Science?">Platinum BlogWhat is the Best Python IDE for Data Science?

    ...aring problem. He is interested in product marketing, and analytics. His latest venture Hackr.io recommends the best Data Science tutorial and online programming courses for every programming language. All the tutorials are submitted and voted by the programming community. Related: Programming Best...

    https://www.kdnuggets.com/2018/11/best-python-ide-data-science.html

  • Microsoft F# for Big Data programming

    ...iginated at Microsoft Research around 2004. It was designed by Don Syme, principal researcher at the company. The language is geared to data-oriented programming as well as parallel programming and algorithmic development. F# 3.0, featuring support for large-scale schematized data and APIs, was...

    https://www.kdnuggets.com/2013/02/microsoft-fsharp-for-big-data-programming.html

  • Top KDnuggets tweets last week, Dec 1-7: Hilarious ! If programming languages were vehicles; Big Data Scientists have the highest salaries

    ...DijQWjD t.co/XWIw7hBXtB IBM #WatsonAnalytics goes public, cloud-based freemium data analysis with natural language t.co/e0MJnV9FZr t.co/XQ0H5hdQT0 If programming languages were vehicles, what would be R, SAS, and SQL? #rstats t.co/kOiLxwI6GQ t.co/a7U2Vee38C If programming languages were vehicles,...

    https://www.kdnuggets.com/2014/12/top-tweets-dec01-07.html

  • Learn Quantum Computing with Python and Q#, Get Programming with Python, Data Science with Python and Dask

    ...ity. That’s why it’s prized by everyone from beginner programmers to data scientists running the most powerful algorithms. If you’re looking to start programming with Python, one of the best resources available is Get Programming with Python. It’s written by Ana Bell, a scientist and lecturer who...

    https://www.kdnuggets.com/2019/09/manning-quantum-computing-python-data-science-dask.html

  • New Poll: Which methods/algorithms you used for a Data Science or Machine Learning application?

    ...Used by Data Scientists See also a similar 2011 KDnuggets Poll Algorithms for data analysis / data mining relevant KDnuggets posts The 10 Algorithms Machine Learning Engineers Need to Know 10 Algorithm Categories for A.I., Big Data, and Data Science Top 10 Data Mining Algorithms, Explained and a...

    https://www.kdnuggets.com/2016/08/new-poll-data-science-methods-algorithms-used.html

  • The Algorithms Aren’t Biased, We Are

    ...hat surfaces fake stories on your feed is taught to share what lots of other people share, irrespective of accuracy. All that data is about us. Those algorithms aren’t biased, we are! Algorithms are mirrors. Algorithmic mirrors don’t fully reflect the world around us, nor the world we want They...

    https://www.kdnuggets.com/2019/01/algorithms-arent-biased-we-are.html

  • Will Deep Learning take over Machine Learning, make other algorithms obsolete?

    …e will work just fine, and using a deep belief network will only complicate things. 2. While deep belief networks are one of the best domain-agnostic algorithms, if one has domain knowledge then many other algorithms (such as HMMs for speech recognition, wavelets for images, etc.) can outperform…

    https://www.kdnuggets.com/2014/10/deep-learning-make-machine-learning-algorithms-obsolete.html

  • Recommendation System Algorithms: An Overview

    ...business’s limitations and requirements. To simplify this task, the Statsbot team has prepared an overview of the main existing recommendation system algorithms.   Collaborative filtering   Collaborative filtering (CF) and its modifications is one of the most commonly used recommendation...

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

  • The 5 Graph Algorithms That Data Scientists Should Know">Silver BlogThe 5 Graph Algorithms That Data Scientists Should Know

    ...nalysis could help a lot in improving our models and generating value. And even understanding a little more about the world. There are a lot of graph algorithms out there, but these are the ones I like the most. Do look into the algorithms in more detail if you like. In this post, I just wanted to...

    https://www.kdnuggets.com/2019/09/5-graph-algorithms-data-scientists-know.html

  • Wolfram Breakthrough Knowledge-based Programming Language – what it means for Data Science?

    ...og post. Big promises apart – the current language of data scientists remains Python and R, and it would be interesting to see if the knowledge programming thing that the Wolfram Language enhances the availability of knowledge programming or simply fades as a marketing fad with some of...

    https://www.kdnuggets.com/2014/03/wolfram-breakthrough-knowledge-based-programming-language-data-science.html

  • If chatbots are to succeed, they need this

    ...the computer what was true. Given this database of true facts and rules, logical deduction could be used to answer the user’s queries. This style of programming is called “declarative programming”. The only real declarative programming language that has taken off and remains popular is SQL, which...

    https://www.kdnuggets.com/2018/05/chatbots-succeed-need-logic.html

  • How Different are Conventional Programming and Machine Learning?

    ...6;fizzbuzz’. If it is not divisible by any of the 3 or 5 then print ‘other’. It’s called a Fizzbuzz game.   Conventional Programming   It is extremely easy in conventional programming to feed the computer with a set of instructions because we have only 4 scenarios...

    https://www.kdnuggets.com/2018/12/different-conventional-programming-machine-learning.html

  • Getting Started with R Programming

    ...phical analysis. It is therefore commonly used in statistical inference, data analysis and Machine Learning. R is currently one of the most requested programming language in the Data Science job market (Figure 1). Figure 1: Most Requested programming languages for Data Science in 2019 [1]   R...

    https://www.kdnuggets.com/2020/02/getting-started-r-programming.html

  • Programming Best Practices For Data Science">Silver BlogProgramming Best Practices For Data Science

    ...: Data Engineering Posts on Dataquest   Bio: Srini Kadamati is Director of Content at Dataquest.io. Original. Reposted with permission. Related: Swiftapply – Automatically efficient pandas apply operations Data Structures Related to Machine Learning Algorithms Introduction to Functional...

    https://www.kdnuggets.com/2018/08/programming-best-practices-data-science.html

  • New Online Data Science Tracks for 2017">Silver Blog, Apr 2017New Online Data Science Tracks for 2017

    …: $39/month for certificate Language: R Organization: Johns Hopkins University Prerequisites: some programming, knowledge of algebra Curriculum The R Programming Environment Advanced R Programming Building R Packages Building Data Visualization Tools Mastering Software Development in R Capstone edX…

    https://www.kdnuggets.com/2017/04/new-online-data-science-tracks-2017.html

  • Why Python is One of the Most Preferred Languages for Data Science?

    ..., programming expertise is also one of the must-have skills an aspiring data scientist needs to acquire. Let’s dig deeper to unearth the most popular programming languages in the data science community!   Top 3 programming languages most used by data scientists As revealed by the findings of a...

    https://www.kdnuggets.com/2020/01/python-preferred-languages-data-science.html

  • COMAD India Graph Mining Programming contest

    ...in those networks. Formally, given an undirected un-weighted graph, the problem is to find the densest subgraph of the given graph. The combinatorial algorithms based on max flow computation for finding maximum density subgraphs have been studied extensively [Gol84,Law01]. However, the time...

    https://www.kdnuggets.com/2014/09/comad-india-graph-mining-programming-contest.html

  • More Free Data Mining, Data Science Books and Resources

    ...a Mining and Analysis: Fundamental Concepts and Algorithms by Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014.A great cover of the data mining exploratory algorithms and machine learning processes. These...

    https://www.kdnuggets.com/2015/03/free-data-mining-data-science-books-resources.html

  • Book: Data Mining and Analysis: Fundamental Concepts and Algorithms

    ...nd Algorithms by Mohammed J. Zaki and Wagner Meira, Jr. Cambridge University Press, May 2014 See also www.amazon.com/Data-Mining-Analysis-Fundamental-Algorithms/dp/0521766338/ Companion Website for Online Resources: dataminingbook.info Description & Features: The fundamental algorithms in data...

    https://www.kdnuggets.com/2014/05/book-data-mining-analysis-fundamental-concepts-algorithms.html

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

    ...ounts of data to teach the algorithm, the use of neural nets and reinforcement learning shows that data sets are not needed to create high-performing algorithms. DeepMind employed these techniques and created Alpha Go Zero, an algorithm that outperformed prior algorithms, by simply playing itself....

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

  • Machine Ethics and Artificial Moral Agents

    ...or someone who studied 12 years to become doctor might not be that easy to give that up). In fact, algorithm adversion is becoming a real problem for algorithms-assisted tasks and it looks that people want to have an (even if incredibly small) degree of control over algorithms (Dietvorst et al.,...

    https://www.kdnuggets.com/2017/11/machine-ethics-artificial-moral-agents.html

  • Common mistakes when carrying out machine learning and data science">Gold BlogCommon mistakes when carrying out machine learning and data science

    ...logarithm of the target variable. XGBoost and LigthGBM performed comparably, RF slightly worse, whereas NN was the worst. Performance (RMSLE) of the algorithms on the test set. Decision tree-based algorithms are very good at interpreting features. For example, they produce a feature importance...

    https://www.kdnuggets.com/2018/12/common-mistakes-data-science.html

  • A Non-Technical Reading List for Data Science">Silver BlogA Non-Technical Reading List for Data Science

    ...nt to understand. Both of these books do an incredible job of transforming dry subjects into entertaining and informative narratives about how to use algorithms, stats, and maths in our daily lives. For example, in Algorithms to Live By, the authors show how we can use the idea of the explore vs....

    https://www.kdnuggets.com/2019/12/non-technical-reading-list-data-science.html

  • What questions can data science answer?

    ...has several (or even many) possible answers: which flavor, which person, which part, which company, which candidate. Most multi-class classification algorithms are just extensions of two-class classification algorithms. Here are a few typical examples. Which animal is in this image? Which aircraft...

    https://www.kdnuggets.com/2016/01/questions-data-science-answer.html

  • Two Major Difficulties in AI and One Applied Solution

    ...d budgets[ii] to explain these algorithms and give decision makers more control over AI. With all this fuss around the amazing capabilities of the NN algorithms, we might think of NN and ML algorithms as mysterious and smart. We think that they must be smart as they’re doing some really amazing...

    https://www.kdnuggets.com/2019/02/ai-chess-difficulties-solution.html

  • Netflix: Senior Data Scientist, Streaming Science & Algorithms

    ...the Streaming Science and Algorithms team that is actively working on ways to improve the streaming Quality of Experience (QoE) for our members using algorithms applied to big data. Problems we tackle are diverse and range from optimizing the streaming algorithms to working with the digital supply...

    https://www.kdnuggets.com/jobs/14/06-18-netflix-senior-data-scientist.html

  • K-Means & Other Clustering Algorithms: A Quick Intro with Python

    ...sters that are in our result and then assign a different color to each of them (up to 10 for the given time is fine) before plotting them. Clustering Algorithms   Some clustering algorithms will cluster your data quite nicely and others will end up failing to do so. That is one of the main...

    https://www.kdnuggets.com/2017/03/k-means-clustering-algorithms-intro-python.html

  • The 5 Sampling Algorithms every Data Scientist need to know

    comments Data Science is the study of algorithms. I grapple through with many algorithms on a day to day basis, so I thought of listing some of the most common and most used algorithms one will end up using in this new DS Algorithm series. Simple Random Sampling Say you want to select a subset of...

    https://www.kdnuggets.com/2019/09/5-sampling-algorithms.html

  • A Concise Explanation of Learning Algorithms with the Mitchell Paradigm

    ...point for the book’s explanation of learning algorithms. While inherently abstract, the variables E, T, and P can be mapped to machine learning algorithms and their learning processes in order to help solidify one’s understanding of learning algorithms abstractly and even more...

    https://www.kdnuggets.com/2018/10/mitchell-paradigm-concise-explanation-learning-algorithms.html

  • 10 More Must-See Free Courses for Machine Learning and Data Science">Gold Blog10 More Must-See Free Courses for Machine Learning and Data Science

    ...es for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.   10. Natural Language Processing University of...

    https://www.kdnuggets.com/2018/12/10-more-free-must-see-courses-machine-learning-data-science.html

  • Companion Website for “Data Mining and Analysis: Fundamental Concepts and Algorithms”

    ...as a textbook, as well as a reference book.” Professor Christos Faloutsos, Carnegie Mellon University Winner of the ACM SIGKDD Innovation Award Related: Top Research Leaders in Data Mining, Data Science, and KDD Book: Data Mining and Analysis: Fundamental Concepts and Algorithms Data Mining...

    https://www.kdnuggets.com/2014/11/companion-data-mining-analysis-fundamental-concepts-algorithms-zaki.html

  • What Types of Questions Can Data Science Answer

    …has several (or even many) possible answers: which flavor, which person, which part, which company, which candidate. Most multi-class classification algorithms are just extensions of two-class classification algorithms. Here are a few typical examples. Which animal is in this image? Which aircraft…

    https://www.kdnuggets.com/2015/09/questions-data-science-can-answer.html

  • Understanding Machine Learning Algorithms">Gold BlogUnderstanding Machine Learning Algorithms

    …current neural networks, and denoising autoencoders. The Magic is Gone There you have it – the ideas behind four of the most popular machine learning algorithms. While these algorithms build highly predictive models, they’re not magic. A grounding in the fundamental concepts will help you…

    https://www.kdnuggets.com/2017/10/understanding-machine-learning-algorithms.html

  • Top /r/MachineLearning Posts, Feb 15-21: The Elephant in the Room of ML Research

    ...to deep learning, this may be the way to do it. 5. The Genetic Algorithm – Explained +66 This article is a great first-exposure to the topic of genetic algorithms. It goes through the structure and motivation for using genetic algorithms. It then details some pseudocode and explains various...

    https://www.kdnuggets.com/2015/02/top-machine-learning-posts-feb15-21.html

  • Design by Evolution: How to evolve your neural network with AutoML

    ...me”. So now we have the basic buildings – how to create a random network, mutate its architecture, and train it. The next step is to create the genetic algorithm that will perform the selection and mutation of the best performing individuals. Every model is trained in parallel and doesn’t...

    https://www.kdnuggets.com/2017/07/design-evolution-evolve-neural-network-automl.html

  • The Current State of Automated Machine Learning

    ...t “your Data Science Replacement“). It is a Python tool which “automatically creates and optimizes machine learning pipelines using genetic programming.” TPOT, like Auto-sklearn, works in tandem with scikit-learn, describing itself as a scikit-learn wrapper. As mentioned...

    https://www.kdnuggets.com/2017/01/current-state-automated-machine-learning.html

  • Top KDnuggets tweets, Dec 1-2: Hilarious: If programming languages were vehicles; How Google Translates Pictures Into Words

    ...20;Translates” Pictures Into Words Using #DeepLearning, #BigData & Vector Math t.co/3WubEvOmGe t.co/txy7dmLLPg Most Clicked: Hilarious ! If programming languages were vehicles: on C, C++, Java, Python, Perl, Lisp, … t.co/JZMk0NRMed t.co/romPicF6Y1 Top 10 most engaging Tweets...

    https://www.kdnuggets.com/2014/12/top-tweets-dec01-02.html

  • Top KDnuggets tweets, Jan 7-8: Programming languages popularity by US state; Machine Learning best practices from Kaggle competitions

    ...ited: Great talk: #MachineLearning best practices learned from lots of competitions, by @Kaggle Chief Scientist #BigData t.co/JOvDjRaXaj Most Viewed: Programming languages popularity by US state: #Java is big in #NY, #NJ, #Python in Idaho t.co/Ek1msfJAQw t.co/5ZgzoQIKcy Most Clicked: Programming...

    https://www.kdnuggets.com/2015/01/top-tweets-jan07-08.html

  • Age of AI Conference 2018 – Day 1 Highlights

    ...branding of the modern collection Deep Learning techniques”.Hence, it’s the title of the talk. Christopher Olah’s seminal blog post on differentiable programming and other types of programming, in which he imagines how we would look back on Deep Learning thirty years from now and establishes a...

    https://www.kdnuggets.com/2018/02/age-ai-conference-2018-day-1.html

  • Python and R Courses for Data Science">Silver BlogPython and R Courses for Data Science

    ..., it is still useful to learn the productivity tool to organize your code and how to use your IDE to write code faster and more efficient. Here is the site containing all the online classes from Harvard for R programming. Here is the site containing all the online classes from Harvard for Python...

    https://www.kdnuggets.com/2020/02/python-r-courses-data-science.html

  • How a simple mix of object-oriented programming can sharpen your deep learning prototype

    ...hink very carefully about the API of the function i.e. what minimal set of arguments is required and how they will serve a purpose for a higher level programming task Don’t forget to write a docstring for a function, even if it is a one-liner description If you start accumulating many utility...

    https://www.kdnuggets.com/2019/08/simple-mix-object-oriented-programming-sharpen-deep-learning-prototype.html

  • The Difference Between Data Scientists and Data Engineers

    ...ithm used by the data scientist in R to a more robust production platform. Overlapping Skills Certainly, there are overlapping skills with respect to programming, although a data engineer’s programming skills often outweigh those of a data scientist. For example, having a data scientist program a...

    https://www.kdnuggets.com/2019/03/odsc-difference-data-scientists-data-engineers.html

  • KDnuggets™ News 18:n10, Mar 7: Functional Programming in Python; Surviving Your Data Science Interview; Easy Image Recognition with Google Tensorflow

    ...bruary Stories: Neural network AI is simple. So… Stop pretending you are a genius Top Stories, Feb 26 – Mar 4: Introduction to Functional Programming in Python; A Tour of The Top 10 Algorithms for Machine Learning Newbies Top KDnuggets tweets, Feb 21-27: Top 20 Python #AI and...

    https://www.kdnuggets.com/2018/n10.html

  • The secret sauce for growing from a data analyst to a data scientist

    ...ng how to use Jupyter will be key for a quicker workflow and data/model exploration. Tip 4: Practise practise and practise for stronger better faster programming skills. Because programming gives you magical powers Entering hackathons, participating in kaggle competitions, working on a personal...

    https://www.kdnuggets.com/2019/08/secret-sauce-growing-from-data-analyst-data-scientist.html

  • From Science to Data Science, a Comprehensive Guide for Transition

    ...s); and given you background you want to be a Type A Data scientist (i.e. more a statistician than a regular programmer), according to this taxonomy. Programming languages Most likely practical programming is the main skill you are missing. For general data science, the standard tools are Python...

    https://www.kdnuggets.com/2016/04/data-science-comprehensive-guide-transition.html

  • Beginners Learning Path for Machine Learning">Gold BlogBeginners Learning Path for Machine Learning

    ...s you the basics of OOP and algorithms in python. You can find this course for free at edx.org. The next step is familiarity with data structures and algorithms. A good programmer must know some basic algorithms like a linked list, binary trees, etc. This course Microsoft will teach you:...

    https://www.kdnuggets.com/2020/05/beginners-learning-path-machine-learning.html

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

    ...ollected Essays. New America: Open Technology Institute. Retrieved from https://na-production.s3.amazonaws.com/documents/data-and-discrimination.pdf. Programming and Prejudice: UTAH computer scientists discover how to find bias in algorithms (2015, August). UNews, University of UTAH. Retrieved from...

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

  • The Data Science Interview Study Guide

    ...Graph Search, DFS and BFS BFS (breadth-first search) and DFS (depth-first search) (video) Algorithms: Binary Search Binary Search Tree Review (video) Algorithms: Recursion Algorithms: Bubble Sort Algorithms: Merge Sort Algorithms: Quicksort String Manipulation Coding Interview Question and Answer:...

    https://www.kdnuggets.com/2020/01/data-science-interview-study-guide.html

  • Bayesian deep learning and near-term quantum computers: A cautionary tale in quantum machine learning">Gold BlogBayesian deep learning and near-term quantum computers: A cautionary tale in quantum machine learning

    ...puters to run certain parts of a learning algorithm. The near-term feasibility of the various quantum machine learning proposals varies. Some quantum algorithms can already be run on current quantum hardware (e.g., algorithms that need few qubits and gates, so-called variational algorithms), others...

    https://www.kdnuggets.com/2019/07/bayesian-deep-learning-near-term-quantum-computers.html

  • Designing Ethical Algorithms

    ...nce by blogging about learning to code and innovations in AI. You can read more about her experiences with ethics within artificial intelligence on her blog Artificially Intelligent Claire. Related: Ethics + Data Science: opinion by DJ Patil, former US Chief Data Scientist The Algorithms Aren’t...

    https://www.kdnuggets.com/2019/03/designing-ethical-algorithms.html

  • Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application?

    ...l is closed – here are the results: Top Data Science and Machine Learning Methods Used in 2018, 2019 Related: A Concise Explanation of Learning Algorithms with the Mitchell Paradigm Key Algorithms and Statistical Models for Aspiring Data Scientists Top Algorithms and Methods Used by Data...

    https://www.kdnuggets.com/2019/04/poll-data-science-machine-learning-methods-algorithms-use-2018-2019.html

  • Machine learning — Is the emperor wearing clothes?

    ...want to separate them with one diagonal line or many horizontal/vertical lines or flexible squigglers… or something else? There are lots and lots of algorithms to pick from.   Algorithms for hipsters These days, no data science hipster is into the humble straight line. Flexible, squiggly...

    https://www.kdnuggets.com/2018/10/machine-learning-emperor-wearing-clothes.html

  • Fundamental Breakthrough in 2 Decade Old Algorithm Redefines Big Data Benchmarks

    ...le difference in the statistical properties of hashes. It is rare to observe such significant gains without any noticeable tradeoffs. Previously, LSH algorithms had many overheads, such as the hashing cost, which is why hashing algorithms never achieved state-of-the-art performance in head-to-head...

    https://www.kdnuggets.com/2017/09/minwise-hashing-breakthrough-big-data-benchmarks.html

  • Reinforcement Learning and the Internet of Things

    ...iving room again or stay plugged in to my charging station? Should I brake or accelerate in response to that yellow light? Etc. Instead of rule based algorithms or algorithms based on learned behaviour(ex supervised learning algorithms), the application of Reinforcement Learning to this scenario is...

    https://www.kdnuggets.com/2016/08/reinforcement-learning-internet-things.html

  • Data Science of Variable Selection: A Review

    ...rience. He has led teams in management consulting, digital media, financial and health care industries. Source: Originally posted anonymously by the author to a thread on Stack Exchange’s statistical Q&A site, Cross Validated. Reposted with permission. Related: Datasets Over Algorithms...

    https://www.kdnuggets.com/2016/06/data-science-variable-selection-review.html

  • The 5 Clustering Algorithms Data Scientists Need to Know">Gold BlogThe 5 Clustering Algorithms Data Scientists Need to Know

    ...t to class 1 and Y-percent to class 2. I.e GMMs support mixed membership.   Agglomerative Hierarchical Clustering   Hierarchical clustering algorithms actually fall into 2 categories: top-down or bottom-up. Bottom-up algorithms treat each data point as a single cluster at the outset and...

    https://www.kdnuggets.com/2018/06/5-clustering-algorithms-data-scientists-need-know.html

  • Adobe: Manager – Algorithms / Machine Learning, 30834

    ...ese methods. Familiar with one or more machine learning or statistics tools such as R, Weka, Matlab etc. Experience with at least one general purpose programming language such as Java, Python, C etc. Knowledge and experience of relational databases and SQL Strong analytical and quantitative problem...

    https://www.kdnuggets.com/jobs/14/07-29-adobe-manager-algorithms-machine-learning.html

  • Top Data Science and Machine Learning Methods Used in 2018, 2019">Gold BlogTop Data Science and Machine Learning Methods Used in 2018, 2019

    ...rcement Learning, 56.1% up, from 4.2% to 6.6% Neural Nets – Convolution, 38.8% up, from 15.8% to 22% Other Methods, 27.1% up, from 6.1% to 7.8% Genetic / Evolutionary Algorithms & Methods, 25.7% up, from 4.8% to 6.0% Neural Networks – Deep Learning, 23.5% up, from 20.6% to 25% The...

    https://www.kdnuggets.com/2019/04/top-data-science-machine-learning-methods-2018-2019.html

  • 3 ways to learn Data Science at Statistics.com

    ...eek to complete. Prerequisite: an undergraduate degree, plus some familiarity with basic programming (though we can help with that, if you are new to programming). Programming in Data Science – Use R for data mining, Python for text mining, extract data from databases with SQL, learn Hadoop...

    https://www.kdnuggets.com/2016/12/statisticscom-3-ways-learn-data-science.html

  • KDnuggets™ News 19:n33, Sep 4: Data Science Skills Poll; Object-oriented Programming for Data Scientists

    ...s   Meetings Get KDnuggets Pass to Strata Data or TensorFlow World   Top Stories, Tweets Top Stories, Aug 26 – Sep 1: Object-oriented programming for data scientists; Why Data Visualization Is The Most Important Skill in a Data Analyst Arsenal Top KDnuggets tweets, Aug 21-27:...

    https://www.kdnuggets.com/2019/n33.html

  • Can Java Be Used for Machine Learning and Data Science?">Gold BlogCan Java Be Used for Machine Learning and Data Science?

    ...has many libraries and tools available for Data Science and Machine Learning. For example, Weka 3 is a fully Java-based workbench popularly used for algorithms in machine learning, data mining, data analysis, and predictive modeling. Massive Online Analysis is an open-source software used...

    https://www.kdnuggets.com/2020/04/java-used-machine-learning-data-science.html

  • What Is Data Science, and What Does a Data Scientist Do?">Gold Blog, Mar 2017What Is Data Science, and What Does a Data Scientist Do?

    ...Data Scientist’s Toolbox   We’ll finish with an overview of some of the typical tools in the data scientist’s proverbial toolbox. Since computer programming is a large component, data scientists must be proficient with programming languages such as R, Python, SQL, Scala, Julia, Java, and so...

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

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