- PyCharm for Data Scientists - May 17, 2019.
This article is a discussion of some of PyCharm's features, and a comparison with Spyder, an another popular IDE for Python. Read on to find the benefits and drawbacks of PyCharm, and an outline of when to prefer it to Spyder and vice versa.
Tags: Data Science, Data Scientist, Programming, PyCharm, Python
- Mathematical programming — Key Habit to Build Up for Advancing Data Science - May 15, 2019.
We show how, by simulating the random throw of a dart, you can compute the value of pi approximately. This is a small step towards building the habit of mathematical programming, which should be a key skill in the repertoire of a budding data scientist.
Tags: Data Science, Mathematics, Programming, Python
- What my first Silver Medal taught me about Text Classification and Kaggle in general? - May 13, 2019.
A first-hand account of ideas tried by a competitor at the recent kaggle competition 'Quora Insincere questions classification', with a brief summary of some of the other winning solutions.
Tags: Advice, Competition, Cross-validation, Kaggle, Python, Text Classification
- A Complete Exploratory Data Analysis and Visualization for Text Data: Combine Visualization and NLP to Generate Insights - May 9, 2019.
Visually representing the content of a text document is one of the most important tasks in the field of text mining as a Data Scientist or NLP specialist. However, there are some gaps between visualizing unstructured (text) data and structured data.
Pages: 1 2
Tags: Data Visualization, NLP, Plotly, Python, Text Analytics
- [White Paper] Unlocking the Power of Data Science & Machine Learning with Python - May 8, 2019.
This guide from ActiveState provides an executive overview of how you can implement Python for your team’s data science and machine learning initiatives.
Tags: ActiveState, Data Science, Machine Learning, Python, White Paper
- Linear Programming and Discrete Optimization with Python using PuLP - May 8, 2019.
Knowledge of such optimization techniques is extremely useful for data scientists and machine learning (ML) practitioners as discrete and continuous optimization lie at the heart of modern ML and AI systems as well as data-driven business analytics processes.
Pages: 1 2
Tags: Linear Programming, Optimization, Python
- Intro to XGBoost: Predicting Life Expectancy with Supervised Learning - May 8, 2019.
Today we’ll use XGBoost Boosted Trees for regression over the official Human Development Index dataset. XGBoost is a framework that allows us to train Boosted Trees exploiting multicore parallelism.
Tags: Decision Trees, Machine Learning, Python, Random Forests, XGBoost
- Naive Bayes: A Baseline Model for Machine Learning Classification Performance - May 7, 2019.
We can use Pandas to conduct Bayes Theorem and Scikitlearn to implement the Naive Bayes Algorithm. We take a step by step approach to understand Bayes and implementing the different options in Scikitlearn.
Pages: 1 2
Tags: Algorithms, Data Science, Machine Learning, Naive Bayes, Python, scikit-learn, Statistics
- How to Automate Tasks on GitHub With Machine Learning for Fun and Profit - May 3, 2019.
Check this tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.
Tags: Datasets, GitHub, Python, TensorFlow
- Modeling Price with Regularized Linear Model & XGBoost - May 2, 2019.
We are going to implement regularization techniques for linear regression of house pricing data. Our goal in price modeling is to model the pattern and ignore the noise.
Tags: Modeling, Python, Regularization, XGBoost
- Which Deep Learning Framework is Growing Fastest? - May 1, 2019.
In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity. TensorFlow was the champion of deep learning frameworks and PyTorch was the youngest framework. How has the landscape changed?
Tags: Data Science, Data Scientist, Deep Learning, fast.ai, Keras, Python, PyTorch, TensorFlow
- Build Your First Chatbot Using Python & NLTK - May 1, 2019.
Today we will learn to create a simple chat assistant or chatbot using Python’s NLTK library.
Tags: Chatbot, NLP, NLTK, Python
- KDnuggets™ News 19:n17, May 1: The most desired skill in data science; Seeking KDnuggets Editors, work remotely - May 1, 2019.
This week, find out about the most desired skill in data science, learn which projects to include in your portfolio, identify a single strategy for pulling data from a Pandas DataFrame (once and for all), read the results of our Top Data Science and Machine Learning Methods poll, and much more.
Tags: Algorithms, Data Science, Generative Adversarial Network, Machine Learning, Pandas, Poll, Portfolio, Python, Recurrent Neural Networks
- Powerful like your local notebook. Sharable like a Google Doc. - Apr 30, 2019.
Mode is the only analytics platform with native Python and R Notebooks. Get everyone up and running in minutes by delivering Notebook-powered results right in your browser. Now anyone on your team can re-run R- and Python-powered reports themselves—without ever touching code.
Tags: Mode Analytics, Python, R, SQL
Normalization vs Standardization — Quantitative analysis - Apr 30, 2019.
Stop using StandardScaler from Sklearn as a default feature scaling method can get you a boost of 7% in accuracy, even when you hyperparameters are tuned!
Pages: 1 2
Tags: Data Preprocessing, Data Science, Feature Engineering, Machine Learning, Normalization, Python, Standardization
- Pandas DataFrame Indexing - Apr 29, 2019.
The goal of this post is identify a single strategy for pulling data from a DataFrame using the Pandas Python library that is straightforward to interpret and produces reliable results.
Tags: Data Science, Pandas, Python
- Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own - Apr 25, 2019.
Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.
Pages: 1 2
Tags: Deep Learning, GANs, Generative Adversarial Network, Generative Models, MNIST, Neural Networks, Python
- Top 10 Python Use Cases - Apr 24, 2019.
This paper covers 10 of the most common use cases by industry for Python that ActiveState has witnessed implemented by its customers.
Tags: ActiveState, ebook, Python
- KDnuggets™ News 19:n16, Apr 24: Data Visualization in Python with Matplotlib & Seaborn; Getting Into Data Science: The Ultimate Q&A - Apr 24, 2019.
Best Data Visualization Techniques for small and large data; The Rise of Generative Adversarial Networks; Approach pre-trained deep learning models with caution; How Optimization Works; Building a Flask API to Automatically Extract Named Entities Using SpaCy
Tags: Data Science, Data Visualization, Generative Adversarial Network, Matplotlib, Optimization, Python, R, Seaborn
Data Visualization in Python: Matplotlib vs Seaborn - Apr 19, 2019.
Seaborn and Matplotlib are two of Python's most powerful visualization libraries. Seaborn uses fewer syntax and has stunning default themes and Matplotlib is more easily customizable through accessing the classes.
Tags: Advice, Data Visualization, Matplotlib, Python, Seaborn
- Unleash a faster Python on your data - Apr 18, 2019.
Intel’s optimized Python packages deliver quick repeatable results compared to standard Python packages. Intel offers optimized Scikit-learn, Numpy, and SciPy to help data scientists get rapid results on their Intel® hardware. Download now.
Tags: Data Science, Intel, numpy, Python, scikit-learn, SciPy
- Building a Flask API to Automatically Extract Named Entities Using SpaCy - Apr 17, 2019.
This article discusses how to use the Named Entity Recognition module in spaCy to identify people, organizations, or locations in text, then deploy a Python API with Flask.
Tags: API, Flask, NLP, Python
- K-Means Clustering: Unsupervised Learning for Recommender Systems - Apr 16, 2019.
In this article we apply unsupervised learning on a Magic: The Gathering dataset in order to build a system for making recommendations.
Tags: Clustering, Dask, K-means, Python, Recommender Systems, Unsupervised Learning
- Data Science with Optimus Part 2: Setting your DataOps Environment - Apr 16, 2019.
Breaking down data science with Python, Spark and Optimus. Today: Data Operations for Data Science. Here we’ll learn to set-up Git, Travis CI and DVC for our project.
Tags: Apache Spark, Data Operations, Data Science, Python, Workflow
- Data Science with Optimus Part 1: Intro - Apr 15, 2019.
With Optimus you can clean your data, prepare it, analyze it, create profilers and plots, and perform machine learning and deep learning, all in a distributed fashion, because on the back-end we have Spark, TensorFlow, Sparkling Water and Keras. It’s super easy to use.
Tags: Apache Spark, Data Science, Python, Workflow
- Because analysis is more than just dashboards - Apr 11, 2019.
Where traditional BI tools often make it easy to build dashboards, Mode makes it easy for you to answer any follow-up questions when you see changes in those dashboards. Choose the level of abstraction you want for a given dataset and quickly get to the story behind the change.
Tags: Analysis, Dashboard, Data Visualization, Mode Analytics, Python, R, SQL
- Build Python for Data Science in Just a Few Clicks - Apr 10, 2019.
There is only one Python distro that lets you add new versions of packages, remove unused packages, and rebuild in minutes. Yes, for free. Download ActiveState Python 3.6 build now.
Tags: ActiveState, free download, Python
- All you need to know about text preprocessing for NLP and Machine Learning - Apr 9, 2019.
We present a comprehensive introduction to text preprocessing, covering the different techniques including stemming, lemmatization, noise removal, normalization, with examples and explanations into when you should use each of them.
Tags: Data Preprocessing, Machine Learning, NLP, Python, Text Analysis, Text Mining
- Advanced Keras — Constructing Complex Custom Losses and Metrics - Apr 8, 2019.
In this tutorial I cover a simple trick that will allow you to construct custom loss functions in Keras which can receive arguments other than y_true and y_pred.
Tags: Keras, Metrics, Neural Networks, Python
- Building a Recommender System - Apr 4, 2019.
A beginners guide to building a recommendation system, with a step-by-step guide on how to create a content-based filtering system to recommend movies for a user to watch.
Tags: Movies, Python, Recommendation Engine, Recommender Systems
- Predict Age and Gender Using Convolutional Neural Network and OpenCV - Apr 4, 2019.
Age and gender estimation from a single face image are important tasks in intelligent applications. As such, let's build a simple age and gender detection model in this detailed article.
Tags: Computer Vision, Convolutional Neural Networks, OpenCV, Python
- Which Face is Real? - Apr 2, 2019.
Which Face Is Real? was developed based on Generative Adversarial Networks as a web application in which users can select which image they believe is a true person and which was synthetically generated. The person in the synthetically generated photo does not exist.
Tags: Deep Learning, GANs, Generative Adversarial Network, Neural Networks, NVIDIA, Python
Explaining Random Forest (with Python Implementation) - Mar 29, 2019.
We provide an in-depth introduction to Random Forest, with an explanation to how it works, its advantages and disadvantages, important hyperparameters and a full example Python implementation.
Tags: Explained, Machine Learning, Python, Random Forests
- A Beginner’s Guide to Linear Regression in Python with Scikit-Learn - Mar 29, 2019.
What linear regression is and how it can be implemented for both two variables and multiple variables using Scikit-Learn, which is one of the most popular machine learning libraries for Python.
Pages: 1 2
Tags: Beginners, Linear Regression, Python, scikit-learn
- [PDF] Python: The Programmer’s Lingua Franca - Mar 27, 2019.
This paper presents the case that Python is the language best suited to becoming a programmer’s lingua franca.
Tags: ActiveState, ebook, Python
- Data Pipelines, Luigi, Airflow: Everything you need to know - Mar 27, 2019.
This post focuses on the workflow management system (WMS) Airflow: what it is, what can you do with it, and how it differs from Luigi.
Tags: Data Workflow, Pipeline, Python, Workflow
R vs Python for Data Visualization - Mar 25, 2019.
This article demonstrates creating similar plots in R and Python using two of the most prominent data visualization packages on the market, namely ggplot2 and Seaborn.
Tags: Data Visualization, ggplot2, Matplotlib, Python, Python vs R, R, Seaborn
- Feature Reduction using Genetic Algorithm with Python - Mar 25, 2019.
This tutorial discusses how to use the genetic algorithm (GA) for reducing the feature vector extracted from the Fruits360 dataset in Python mainly using NumPy and Sklearn.
Pages: 1 2
Tags: Deep Learning, Feature Engineering, Genetic Algorithm, Neural Networks, numpy, Python, scikit-learn
- Deploy your PyTorch model to Production - Mar 20, 2019.
This tutorial aims to teach you how to deploy your recently trained model in PyTorch as an API using Python.
Tags: Data Science Education, Data Scientist, Deep Learning, Flask, Programming, Python, PyTorch
- Mastering Fast Gradient Boosting on Google Colaboratory with free GPU - Mar 19, 2019.
CatBoost is a fast implementation of GBDT with GPU support out-of-the-box. Google Colaboratory is a very useful tool with free GPU support.
Tags: CatBoost, CoLaboratory, GPU, Gradient Boosting, Machine Learning, Python, Yandex
- How to Train a Keras Model 20x Faster with a TPU for Free - Mar 19, 2019.
This post shows how to train an LSTM Model using Keras and Google CoLaboratory with TPUs to exponentially reduce training time compared to a GPU on your local machine.
Tags: CoLaboratory, Deep Learning, Keras, Python, TensorFlow, TPU
Artificial Neural Networks Optimization using Genetic Algorithm with Python - Mar 18, 2019.
This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance.
Pages: 1 2
Tags: AI, Algorithms, Deep Learning, Machine Learning, Neural Networks, numpy, Optimization, Python
- Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision - Mar 15, 2019.
In this blog, I’ll walk you through a personal project in which I cheaply built a classifier to detect anti-semitic tweets, with no public dataset available, by combining weak supervision and transfer learning.
Pages: 1 2
Tags: Bias, fast.ai, NLP, Python, Text Classification, Transfer Learning, Twitter, ULMFiT
- Advanced Keras — Accurately Resuming a Training Process - Mar 14, 2019.
This article on practical advanced Keras use covers handling nontrivial cases where custom callbacks are used.
Tags: Keras, Neural Networks, Python, Training
- Object Detection with Luminoth - Mar 13, 2019.
In this article you will learn about Luminoth, an open source computer vision library which sits atop Sonnet and TensorFlow and provides object detection for images and video.
Tags: Computer Vision, Image Recognition, Object Detection, Python
- Who is a typical Data Scientist in 2019? - Mar 11, 2019.
We investigate what a typical data scientist looks like and see how this differs from this time last year, looking at skill set, programming languages, industry of employment, country of employment, and more.
Tags: Career, Data Science Skills, Data Scientist, Industry, MATLAB, Python, R, SQL
- Neural Networks with Numpy for Absolute Beginners — Part 2: Linear Regression - Mar 7, 2019.
In this tutorial, you will learn to implement Linear Regression for prediction using Numpy in detail and also visualize how the algorithm learns epoch by epoch. In addition to this, you will explore two layer Neural Networks.
Pages: 1 2
Tags: Gradient Descent, Linear Regression, Neural Networks, numpy, Python
- Top KDnuggets tweets, Feb 27 – Mar 05: How to Setup a Python Environment for Machine Learning; How to do Everything in Computer Vision - Mar 6, 2019.
Also Python Data Science for Beginners; Deep Learning for Natural Language Processing (NLP) - using RNNs and CNNs.
Tags: NLP, Python, Top tweets
- Neural Networks with Numpy for Absolute Beginners: Introduction - Mar 5, 2019.
In this tutorial, you will get a brief understanding of what Neural Networks are and how they have been developed. In the end, you will gain a brief intuition as to how the network learns.
Tags: Beginners, Neural Networks, numpy, Python
- Python 2 support ends this year. Are you ready to migrate? - Feb 27, 2019.
Python 2 ends on Jan 1, 2020. Migrating from Python 2 to 3 can be a scary process, so get this solution sheet with different options for moving your existing packages and applications from Python 2 to 3, along with best practice guidelines.
Tags: ActiveState, Python
4 Reasons Why Your Machine Learning Code is Probably Bad - Feb 26, 2019.
Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG.
Tags: Data Science, Machine Learning, Programming, Python, Workflow
- Simple Yet Practical Data Cleaning Codes - Feb 26, 2019.
Real world data is messy and needs to be cleaned before it can be used for analysis. Industry experts say the data preprocessing step can easily take 70% to 80% of a data scientist's time on a project.
Tags: Data Cleaning, Data Preprocessing, Python
- Don’t do analysis in a vacuum - Feb 22, 2019.
Traditional tools force analysts to play the import-and-export game, so it's difficult to keep data fresh and accessible. Every Mode report or dashboard lives at a unique URL for future sharing, iterating, and building upon. Mode brings your entire team together in one platform.
Tags: Analytics, Dashboard, Mode Analytics, Platform, Python, R
- Artificial Neural Network Implementation using NumPy and Image Classification - Feb 21, 2019.
This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset
Pages: 1 2
Tags: Deep Learning, Machine Learning, Neural Networks, numpy, Python
Python Data Science for Beginners - Feb 20, 2019.
Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.
Tags: Beginners, Data Science, Matplotlib, numpy, Pandas, Python, scikit-learn, SciPy
- KDnuggets™ News 19:n08, Feb 20: The Gold Standard of Python Machine Learning; The Analytics Engineer – new role in the data team - Feb 20, 2019.
Intro to scikit-learn; how to set up a Python ML environment; why there should be a new role in the Data Science team; how to learn one of the hardest parts of being a Data Scientist; and how explainable is BERT?
Tags: BERT, Python, scikit-learn
- Running R and Python in Jupyter - Feb 19, 2019.
The Jupyter Project began in 2014 for interactive and scientific computing. Fast forward 5 years and now Jupyter is one of the most widely adopted Data Science IDE's on the market and gives the user access to Python and R
Tags: IPython, Jupyter, Python, R
- How to Setup a Python Environment for Machine Learning - Feb 18, 2019.
In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.
Tags: Machine Learning, Programming, Python
- An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning - Feb 13, 2019.
If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library.
Tags: Machine Learning, Python, scikit-learn
- 10 Trending Data Science Topics at ODSC East 2019 - Feb 7, 2019.
ODSC East 2019, Boston, Apr 30 - May 3, will host over 300+ of the leading experts in data science and AI. Here are a few standout topics and presentations in this rapidly evolving field. Register for ODSC East at 50% off till Feb 8.
Tags: Apache Spark, Boston, Data Science, LSTM, Machine Learning, ODSC, Python
- Top KDnuggets tweets, Jan 30 – Feb 05: state-of-the-art in #AI, #MachineLearning - Feb 6, 2019.
Also Brilliant tour-de-force! Reinforcement Learning to solve Rubiks Cube; Dask, Pandas, and GPUs: first steps; Neural network AI is simple. So Stop pretending you are a genius.
Tags: Dask, GPU, Pandas, Python, Reinforcement Learning, Top tweets
- From Good to Great Data Science, Part 1: Correlations and Confidence - Feb 5, 2019.
With the aid of some hospital data, part one describes how just a little inexperience in statistics could result in two common mistakes.
Tags: Correlation, Data Science, Python, Statistics
- Intuitive Visualization of Outlier Detection Methods - Feb 5, 2019.
Check out this visualization for outlier detection methods, and the Python project from which it comes, a toolkit for easily implementing outlier detection methods on your own.
Tags: Cheat Sheet, Outliers, Python
- Exploring Python Basics - Jan 31, 2019.
This free eBook is a great resource for any beginner, providing a good introduction into Python, a look at the basics of learning a programming language and explores modelling and predictions.
Tags: Beginners, Book, Manning, Python
- ELMo: Contextual Language Embedding - Jan 31, 2019.
Create a semantic search engine using deep contextualised language representations from ELMo and why context is everything in NLP.
Tags: Data Visualization, NLP, Plotly, Python, Word Embeddings
- KDnuggets™ News 19:n05, Jan 30: Your AI skills are worth less than you think; 7 Steps to Mastering Basic Machine Learning - Jan 30, 2019.
Also: Logistic Regression: A Concise Technical Overview; AI is a Big Fat Lie; How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy; Airbnb Rental Listings Dataset Mining; Data Science Project Flow for Startups
Tags: AI, Hype, Logistic Regression, Machine Learning, Modeling, Python, Skills, Workflow
7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition - Jan 29, 2019.
With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python!
Tags: 7 Steps, Classification, Clustering, Jupyter, Machine Learning, Python, Regression
- KDnuggets™ News 19:n04, Jan 23: Top 7 Python Libraries for Data Science and AI; Ontology and Data Science - Jan 23, 2019.
Also Cartoon: Is this how you do the blockchain thing?; Data Scientist's Dilemma: The Cold Start Problem; Why Ice Cream Is Linked to Shark Attacks - Correlation/Causation Smackdown.
Tags: Data Science, Flask, Ontology, Python
- 2018’s Top 7 Python Libraries for Data Science and AI - Jan 21, 2019.
This is a list of the best libraries that changed our lives this year, compiled from my weekly digests.
Pages: 1 2
Tags: AI, Data Science, Python
- On Points Insights: Senior Python Developer with Big Data skills [Remote, US] - Jan 18, 2019.
Seeking a Senior Python Developer with Big Data skills (work remotely), to interpret internal or external business issues and recommend best practices, solve complex problems, and take a broad perspective to identify innovative solutions.
Tags: Big Data, Developer, On Points Insights, Python, Telecommute
- Automated Machine Learning in Python - Jan 18, 2019.
An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
Tags: Automated Machine Learning, AutoML, H2O, Keras, Machine Learning, Python, scikit-learn
How to build an API for a machine learning model in 5 minutes using Flask - Jan 17, 2019.
Flask is a micro web framework written in Python. It can create a REST API that allows you to send data, and receive a prediction as a response.
Tags: API, Flask, Machine Learning, Python
- The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
Tags: Automated Machine Learning, Facebook, fast.ai, Google, Keras, Machine Learning, Object Detection, Python, Reinforcement Learning, Word Embeddings
- Python Patterns: max Instead of if - Jan 10, 2019.
I often have to loop over a set of objects to find the one with the greatest score. You can use an if statement and a placeholder, but there are more elegant ways!
Tags: Programming, Python
- 3 More Google Colab Environment Management Tips - Jan 2, 2019.
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.
Tags: CoLaboratory, Google, Jupyter, Machine Learning, Python
- Manning Countdown to 2019 – Big Deals on AI, Data Science, Machine Learning books and videos - Dec 28, 2018.
Introducing the Manning countdown to 2019, where each day you’ll be able to get a different one day deal on some of their biggest books and video courses.
Tags: Book, Deep Learning, ebook, Manning, Python
- Synthetic Data Generation: A must-have skill for new data scientists - Dec 27, 2018.
A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.
Pages: 1 2
Tags: Classification, Clustering, Datasets, Machine Learning, Python
A Guide to Decision Trees for Machine Learning and Data Science - Dec 24, 2018.
What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.
Tags: Algorithms, Data Science, Decision Trees, Machine Learning, Python, scikit-learn
- Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning - Dec 19, 2018.
Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning.
Tags: Data Science, Deep Learning, Machine Learning, Pandas, Python, PyTorch, TensorFlow
- Exploring the Data Jungle Free eBook - Dec 18, 2018.
This free eBook by Brian Godsey will provide you with real-world examples in Python, R, and other languages suitable for data science.
Tags: Data Preparation, Data Science, Data Visualization, Free ebook, Manning, Python, R
- Solve any Image Classification Problem Quickly and Easily - Dec 13, 2018.
This article teaches you how to use transfer learning to solve image classification problems. A practical example using Keras and its pre-trained models is given for demonstration purposes.
Pages: 1 2
Tags: Classification, Computer Vision, Image Recognition, Keras, Python
- Keras Hyperparameter Tuning in Google Colab Using Hyperas - Dec 12, 2018.
In this post, I will show you how you can tune the hyperparameters of your existing keras models using Hyperas and run everything in a Google Colab Notebook.
Tags: Automated Machine Learning, CoLaboratory, Google, Hyperparameter, Keras, Python
- KDnuggets™ News 18:n47, Dec 12: Common mistakes when doing machine learning; Here are the most popular Python IDEs / Editors - Dec 12, 2018.
Common mistakes when carrying out machine learning and data science; Most popular Python IDEs/Editors; Machine Learning / AI Main Developments in 2018 and Key Trends for 2019; Machine Learning Project checklist.
Tags: Data Science, Machine Learning, Mistakes, Python, Trends
- Introduction to Named Entity Recognition - Dec 11, 2018.
Named Entity Recognition is a tool which invariably comes handy when we do Natural Language Processing tasks. Read on to find out how.
Pages: 1 2
Tags: NLP, Python, Text Classification
Here are the most popular Python IDEs / Editors - Dec 7, 2018.
We report on the most popular IDE and Editors, based on our poll. Jupyter is the favorite across all regions and employment types, but there is competition for no. 2 and no. 3 spots.
Tags: Jupyter, Poll, Programming, PyCharm, Python
- Four Techniques for Outlier Detection - Dec 6, 2018.
There are many techniques to detect and optionally remove outliers from a dataset. In this blog post, we show an implementation in KNIME Analytics Platform of four of the most frequently used - traditional and novel - techniques for outlier detection.
Tags: Knime, Outliers, Python
- The Quick Python Book - Dec 5, 2018.
Written for programmers new to Python, this latest edition includes new exercises throughout. It covers features common to other languages concisely, while introducing Python's comprehensive standard functions library and unique features in detail.
Tags: Book, Manning, Python
- Handling Imbalanced Datasets in Deep Learning - Dec 4, 2018.
It’s important to understand why we should do it so that we can be sure it’s a valuable investment. Class balancing techniques are only really necessary when we actually care about the minority classes.
Tags: Balancing Classes, Datasets, Deep Learning, Keras, Python
- Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools - Dec 3, 2018.
We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.
Tags: Big Data, Data Visualization, Deep Learning, Jupyter, Machine Learning, Python, R, Tableau
- Free ebook: Exploring Data with Python - Nov 29, 2018.
This free eBook starts building your foundation in data science processes with practical Python tips and techniques for working and aspiring data scientists.
Tags: Data Science, Free ebook, Manning, Python
- Sales Forecasting Using Facebook’s Prophet - Nov 28, 2018.
In this tutorial we’ll use Prophet, a package developed by Facebook to show how one can achieve this.
Tags: Facebook, Python, Sales, Time Series
- KDnuggets™ News 18:n45, Nov 28: Your Favorite Python IDE/editor? Intro to Data Science for Managers - Nov 28, 2018.
Also: 6 Goals Every Wannabe Data Scientist Should Make for 2019; Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices.
Tags: Data Science Skills, Keras, LSTM, Python
- SQL, Python, and R in One Platform - Nov 27, 2018.
Stop jumping between applications. Get a complete analytical toolkit.
Tags: Data Science Platform, Data Visualization, Mode Analytics, Python, R, SQL
- What Python editors or IDEs you used the most in 2018? - Nov 27, 2018.
Vote in the new KDnuggets Poll - what are your favorite Python editors or IDEs?
Tags: Poll, Python
- KDnuggets™ News 18:n44, Nov 21: What is the Best Python IDE for Data Science?; Anticipating the next move in data science - Nov 21, 2018.
Also: Mastering The New Generation of Gradient Boosting; Top 10 Python Data Science Libraries; Predictive Analytics in 2018: Salaries & Industry Shifts; Sorry I didn't get that! How to understand what your users want; Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU
Tags: Cloud, Data Science, Gradient Boosting, Interpretability, Machine Learning, Predictive Analytics, Python
- Word Morphing – an original idea - Nov 20, 2018.
In this post, we describe how to utilise word2vec's embeddings and A* search algorithm to morph between words.
Tags: NLP, Python, Text Classification
- Top 10 Python Data Science Libraries - Nov 16, 2018.
The third part of our series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
Tags: Data Science, GitHub, numpy, Pandas, Python, StatsModels
- Mastering The New Generation of Gradient Boosting - Nov 15, 2018.
Catboost, the new kid on the block, has been around for a little more than a year now, and it is already threatening XGBoost, LightGBM and H2O.
Tags: Boosting, Gradient Boosting, Machine Learning, Python
What is the Best Python IDE for Data Science? - Nov 14, 2018.
Before you start learning Python, choose the IDE that suits you the best. We examine many available tools, their pros and cons, and suggest how to choose the best Python IDE for you.
Tags: Data Science, Jupyter, Programming, Python
- Healthcare Analytics Made Simple - Nov 12, 2018.
Finally, a book on Python healthcare machine learning techniques is here! Healthcare Analytics Made Simple does just what the title says: it makes healthcare data science simple and approachable for everyone.
Tags: Analytics, Book, Healthcare, Pandas, Python
- Multi-Class Text Classification with Doc2Vec & Logistic Regression - Nov 9, 2018.
Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.
Tags: Logistic Regression, NLP, Python, Text Classification
- Introduction to PyTorch for Deep Learning - Nov 7, 2018.
In this tutorial, you’ll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you’ll be comfortable applying it to your deep learning models.
Tags: Deep Learning, Neural Networks, Python, PyTorch
- KDnuggets™ News 18:n42, Nov 7: The Most in Demand Skills for Data Scientists; How Machines Understand Our Language: Intro to NLP - Nov 7, 2018.
Also: Machine Learning Classification: A Dataset-based Pictorial; Quantum Machine Learning: A look at myths, realities, and future projections; Multi-Class Text Classification Model Comparison and Selection; Top 13 Python Deep Learning Libraries
Tags: Classification, Data Science Skills, Deep Learning, Myths, NLP, Python, Text Classification
- Text Preprocessing in Python: Steps, Tools, and Examples - Nov 6, 2018.
We outline the basic steps of text preprocessing, which are needed for transferring text from human language to machine-readable format for further processing. We will also discuss text preprocessing tools.
Pages: 1 2
Tags: Data Preparation, NLP, Python, Text Analysis, Text Mining
- Building Surveillance System Using USB Camera and Wireless-Connected Raspberry Pi - Nov 6, 2018.
Read this post to learn how to build a surveillance system using a USB camera plugged into Raspberry Pi (RPi) which is connected a PC using its wireless interface.
Pages: 1 2
Tags: Computer Vision, Python, Raspberry Pi, Security, Video recognition
- Quantum Machine Learning: A look at myths, realities, and future projections - Nov 5, 2018.
An overview of quantum computing and quantum algorithm design, including current state of the hardware and algorithm design within the existing systems.
Tags: Machine Learning, Python, Quantum Computing, Statistics
Top 13 Python Deep Learning Libraries - Nov 2, 2018.
Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
Tags: Caffe, Deep Learning, GitHub, MXNet, Python, PyTorch, TensorFlow, Theano
- Multi-Class Text Classification Model Comparison and Selection - Nov 1, 2018.
This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.
Pages: 1 2
Tags: Modeling, NLP, Python, Text Classification
How Machines Understand Our Language: An Introduction to Natural Language Processing - Oct 31, 2018.
The applications of NLP are endless. This is how a machine classifies whether an email is spam or not, if a review is positive or negative, and how a search engine recognizes what type of person you are based on the content of your query to customize the response accordingly.
Tags: Machine Learning, NLP, NLTK, Python
- KDnuggets™ News 18:n41, Oct 31: Introduction to Deep Learning with Keras; Easy Named Entity Recognition with Scikit-Learn - Oct 31, 2018.
Also: Generative Adversarial Networks - Paper Reading Road Map; How I Learned to Stop Worrying and Love Uncertainty; Implementing Automated Machine Learning Systems with Open Source Tools; Notes on Feature Preprocessing: The What, the Why, and the How
Tags: Automated Machine Learning, Data Preprocessing, Deep Learning, Generative Adversarial Network, Keras, NLP, Python, scikit-learn
- Stop Installing Tensorflow Using pip for Performance Sake! - Oct 30, 2018.
If you aren’t already using conda, I recommend that you start as it makes managing your data science tools much more enjoyable.
Tags: Anaconda, Python, TensorFlow
- Introduction to Deep Learning with Keras - Oct 29, 2018.
In this article, we’ll build a simple neural network using Keras. Now let’s proceed to solve a real business problem: an insurance company wants you to develop a model to help them predict which claims look fraudulent.
Pages: 1 2
Tags: Deep Learning, Keras, Neural Networks, Python
- SQL, Python, & R in One Platform - Oct 26, 2018.
No more jumping between applications. Mode Studio combines a SQL editor, Python and R notebooks, and a visualization builder in one platform.
Tags: Data Visualization, Mode Analytics, Python, R, SQL
- Notes on Feature Preprocessing: The What, the Why, and the How - Oct 26, 2018.
This article covers a few important points related to the preprocessing of numeric data, focusing on the scaling of feature values, and the broad question of dealing with outliers.
Tags: Data Preparation, Data Preprocessing, numpy, Python, scikit-learn, SciPy
- Naive Bayes from Scratch using Python only – No Fancy Frameworks - Oct 25, 2018.
We provide a complete step by step pythonic implementation of naive bayes, and by keeping in mind the mathematical & probabilistic difficulties we usually face when trying to dive deep in to the algorithmic insights of ML algorithms, this post should be ideal for beginners.
Pages: 1 2
Tags: Machine Learning, Naive Bayes, Python
- Get a 2–6x Speed-up on Your Data Pre-processing with Python - Oct 23, 2018.
Get a 2–6x speed-up on your pre-processing with these 3 lines of code!
Tags: Data Preprocessing, Efficiency, Programming, Python
- Beginner Data Visualization & Exploration Using Pandas - Oct 22, 2018.
This tutorial will offer a beginner guide into how to get around with Pandas for data wrangling and visualization.
Pages: 1 2
Tags: Data Exploration, Data Visualization, Pandas, Python
- Accelerating Your Algorithms in Production [Webinar Replay] - Oct 16, 2018.
Numerical algorithms are computationally demanding, which makes performance an important consideration when using Python for machine learning, especially as you move from desktop to production.
Tags: ActiveState, Intel, Production, Python
- GitHub Python Data Science Spotlight: High Level Machine Learning & NLP, Ensembles, Command Line Viz & Docker Made Easy - Oct 16, 2018.
This post spotlights 5 data science projects, all of which are open source and are present on GitHub repositories, focusing on high level machine learning libraries and low level support tools.
Tags: Data Science, Docker, Ensemble Methods, fast.ai, GitHub, Machine Learning, NLP, Python
- SQL, Python, & R: All in One Platform - Oct 11, 2018.
Mode Studio connects a SQL editor, Python and R notebooks, and a visualization builder in one platform. Sign up now for access.
Tags: Data Visualization, Python, R, SQL
- Evaluating the Business Value of Predictive Models in Python and R - Oct 11, 2018.
In these blogs for R and python we explain four valuable evaluation plots to assess the business value of a predictive model. We show how you can easily create these plots and help you to explain your predictive model to non-techies.
Pages: 1 2
Tags: Business Value, Data Visualization, Lift charts, Predictive Models, Python, R
10 Best Mobile Apps for Data Scientist / Data Analysts - Oct 10, 2018.
A collection of useful mobile applications that will help enhance your vital data science and analytic skills. These free apps can improve your listening abilities, logical skills, basic leadership qualities and more.
Tags: Apps, Data Scientist, Mobile, Python
- KDnuggets™ News 18:n38, Oct 10: Concise Explanation of Learning Algorithms; Why I Call Myself a Data Scientist; Linear Regression in the Wild - Oct 10, 2018.
This week, KDnuggets brings you a discussion of learning algorithms with a hat tip to Tom Mitchell, discusses why you might call yourself a data scientist, explores machine learning in the wild, checks out some top trends in deep learning, shows you how to learn data science if you are low on finances, and puts forth one person's opinion on the top 8 Python machine learning libraries to help get the job done.
Tags: Algorithms, Data Science, Deep Learning, Linear Regression, Machine Learning, Python, Tom Mitchell
- Top 8 Python Machine Learning Libraries - Oct 9, 2018.
Part 1 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
Tags: GitHub, Keras, Machine Learning, Python
- Basic Image Data Analysis Using Python – Part 4 - Oct 5, 2018.
Accessing the internal component of digital images using Python packages helps the user understand its properties, as well as its nature.
Tags: Computer Vision, Image Processing, Python
- Linear Regression in the Wild - Oct 3, 2018.
We take a look at how to use linear regression when the dependent variables have measurement errors.
Tags: Algorithms, Linear Regression, Python
- Unleash a Faster Python on Your Data - Oct 2, 2018.
Intel provides optimized Scikit-learn, the most used Python package for classical machine learning. Get faster scikit-learn through Intel® Distribution for Python*
Tags: Analytics, Intel, Python, scikit-learn
How to Create a Simple Neural Network in Python - Oct 2, 2018.
The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that.
Tags: Machine Learning, Neural Networks, Python
- Basic Image Data Analysis Using Python – Part 3 - Sep 28, 2018.
Accessing the internal component of digital images using Python packages becomes more convenient to help understand its properties, as well as nature.
Tags: Computer Vision, Image Processing, numpy, Python
- Visualising Geospatial data with Python using Folium - Sep 27, 2018.
Folium is a powerful data visualization library in Python that was built primarily to help people visualize geospatial data. With Folium, one can create a map of any location in the world if its latitude and longitude values are known. This guide will help you get started.
Tags: Data Visualization, Geospatial, GitHub, Python
