- A Gentle Introduction to PyTorch 1.2 - Sep 20, 2019.
This comprehensive tutorial aims to introduce the fundamentals of PyTorch building blocks for training neural networks.
Tags: Neural Networks, Python, PyTorch
- Applying Data Science to Cybersecurity Network Attacks & Events - Sep 19, 2019.
Check out this detailed tutorial on applying data science to the cybersecurity domain, written by an individual with backgrounds in both fields.
Tags: Cybersecurity, Data Science, Machine Learning, Python, Security
- 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python - Sep 19, 2019.
“I want to learn machine learning and artificial intelligence, where do I start?” Here.
Tags: Beginners, Data Science, Machine Learning, Python
- Python 2 End of Life Survey – Are You Prepared? - Sep 18, 2019.
Support for Python 2 will expire on Jan. 1, 2020, after which the Python core language and many third-party packages will no longer be supported or maintained. Take this survey to help determine and share your level of preparation.
Tags: ActiveState, Python, Survey
- Which Data Science Skills are core and which are hot/emerging ones? - Sep 17, 2019.
We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.
Tags: Career, Data Science Skills, Data Visualization, Deep Learning, Excel, Machine Learning, Python, PyTorch, Scala, Skills, Statistics, TensorFlow
- Explore the world of Bioinformatics with Machine Learning - Sep 17, 2019.
The article contains a brief introduction of Bioinformatics and how a machine learning classification algorithm can be used to classify the type of cancer in each patient by their gene expressions.
Tags: Bioinformatics, Machine Learning, Python
- 5 Step Guide to Scalable Deep Learning Pipelines with d6tflow - Sep 16, 2019.
How to turn a typical pytorch script into a scalable d6tflow DAG for faster research & development.
Tags: Deep Learning, Pipeline, Python, PyTorch, Workflow
- Ensemble Methods for Machine Learning: AdaBoost - Sep 12, 2019.
It turned out that, if we ask the weak algorithm to create a whole bunch of classifiers (all weak for definition), and then combine them all, what may figure out is a stronger classifier.
Tags: Adaboost, Ensemble Methods, Machine Learning, Python
- Train sklearn 100x Faster - Sep 11, 2019.
As compute gets cheaper and time to market for machine learning solutions becomes more critical, we’ve explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.
Tags: Distributed Systems, Machine Learning, Python, scikit-learn, Training
- KDnuggets™ News 19:n34, Sep 11: I wasn’t getting hired as a Data Scientist. So I sought data on who is - Sep 11, 2019.
How one person overcame rejections applying to Data Scientist positions by getting actual data on who is getting hired; Advice from Andrew Ng on building ML career and reading research papers; 10 Great Python resources for Data Scientists; Python Libraries for Interpretable ML.
Tags: Advice, Andrew Ng, Career, Data Scientist, Interpretability, Python
The 5 Graph Algorithms That Data Scientists Should Know - Sep 10, 2019.
In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python.
Tags: Algorithms, Data Science, Data Scientist, Graph, Python
- OpenStreetMap Data to ML Training Labels for Object Detection - Sep 9, 2019.
I am really interested in creating a tight, clean pipeline for disaster relief applications, where we can use something like crowd sourced building polygons from OSM to train a supervised object detector to discover buildings in an unmapped location.
Tags: Geospatial, Machine Learning, Object Detection, Python
10 Great Python Resources for Aspiring Data Scientists - Sep 9, 2019.
This is a collection of 10 interesting resources in the form of articles and tutorials for the aspiring data scientist new to Python, meant to provide both insight and practical instruction when starting on your journey.
Tags: Data Science, Data Scientist, Programming, Python
- Build Your First Voice Assistant - Sep 6, 2019.
Hone your practical speech recognition application skills with this overview of building a voice assistant using Python.
Tags: Machine Learning, NLP, Python, Speech Recognition
- Learn Quantum Computing with Python and Q#, Get Programming with Python, Data Science with Python and Dask - Sep 4, 2019.
Save 40% on Get Programming with Python, Data Science with Python and Dask, and Learn Quantum Computing with Python and Q# with code nlpython40.
Tags: Dask, Manning, Programming, Python, Quantum Computing
- An Easy Introduction to Machine Learning Recommender Systems - Sep 4, 2019.
Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.
Tags: Beginners, Machine Learning, Python, Recommendation Engine, Recommender Systems
- Python Libraries for Interpretable Machine Learning - Sep 4, 2019.
In the following post, I am going to give a brief guide to four of the most established packages for interpreting and explaining machine learning models.
Tags: Bias, Interpretability, LIME, Machine Learning, Python, SHAP
- An Overview of Topics Extraction in Python with Latent Dirichlet Allocation - Sep 4, 2019.
A recurring subject in NLP is to understand large corpus of texts through topics extraction. Whether you analyze users’ online reviews, products’ descriptions, or text entered in search bars, understanding key topics will always come in handy.
Tags: LDA, NLP, Python, Text Analytics, Topic Modeling
- Automate your Python Scripts with Task Scheduler: Windows Task Scheduler to Scrape Alternative Data - Sep 3, 2019.
In this tutorial, you will learn how to run task scheduler to web scrape data from Lazada (eCommerce) website and dump it into SQLite RDBMS Database.
Tags: Data Science, Python, Web Scraping
Object-oriented programming for data scientists: Build your ML estimator - Aug 30, 2019.
Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.
Tags: Data Scientist, Machine Learning, Programming, Python
- 4 Tips for Advanced Feature Engineering and Preprocessing - Aug 29, 2019.
Techniques for creating new features, detecting outliers, handling imbalanced data, and impute missing values.
Tags: Data Preprocessing, Feature Engineering, Python, Tips
- Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch - Aug 23, 2019.
Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch with computational graphs.
Tags: Backpropagation, Neural Networks, numpy, Python
- Comparing Decision Tree Algorithms: Random Forest vs. XGBoost - Aug 21, 2019.
Check out this tutorial walking you through a comparison of XGBoost and Random Forest. You'll learn how to create a decision tree, how to do tree bagging, and how to do tree boosting.
Tags: ActiveState, Decision Trees, Python, Random Forests, XGBoost
- Understanding Decision Trees for Classification in Python - Aug 21, 2019.
This tutorial covers decision trees for classification also known as classification trees, including the anatomy of classification trees, how classification trees make predictions, using scikit-learn to make classification trees, and hyperparameter tuning.
Tags: Classification, Decision Trees, Python, scikit-learn
- Automate Stacking In Python: How to Boost Your Performance While Saving Time - Aug 21, 2019.
Utilizing stacking (stacked generalizations) is a very hot topic when it comes to pushing your machine learning algorithm to new heights. For instance, most if not all winning Kaggle submissions nowadays make use of some form of stacking or a variation of it.
Tags: Algorithms, Big Data, Data Science, Python
- KDnuggets™ News 19:n31, Aug 21: Become a Marketable Data Scientist; Data Science Command Line Basics; Chatbots with Keras - Aug 21, 2019.
This week's news: Become More Marketable as a Data Scientist; Command Line Basics Every Data Scientist Should Know; Chatbots with Keras!; Understanding Cancer using Machine Learning; Statistical Modelling vs Machine Learning; Is Kaggle Learn a "Faster Data Science Education?"; and much more!
Tags: Cancer Detection, Chatbot, Data Science, Data Scientist, Keras, NLP, Python
- An Overview of Python’s Datatable package - Aug 20, 2019.
Modern machine learning applications need to process a humongous amount of data and generate multiple features. Python’s datatable module was created to address this issue. It is a toolkit for performing big data (up to 100GB) operations on a single-node machine, at the maximum possible speed.
Tags: Big Data, Data Science, Python
Deep Learning for NLP: Creating a Chatbot with Keras! - Aug 19, 2019.
Learn how to use Keras to build a Recurrent Neural Network and create a Chatbot! Who doesn’t like a friendly-robotic personal assistant?
Tags: Chatbot, Deep Learning, Keras, NLP, Python
- Pytorch Lightning vs PyTorch Ignite vs Fast.ai - Aug 16, 2019.
Here, I will attempt an objective comparison between all three frameworks. This comparison comes from laying out similarities and differences objectively found in tutorials and documentation of all three frameworks.
Tags: fast.ai, Neural Networks, Python, PyTorch
- Learn how to use PySpark in under 5 minutes (Installation + Tutorial) - Aug 13, 2019.
Apache Spark is one of the hottest and largest open source project in data processing framework with rich high-level APIs for the programming languages like Scala, Python, Java and R. It realizes the potential of bringing together both Big Data and machine learning.
Tags: Apache Spark, Big Data, Data Science, Python
- A 2019 Guide to Semantic Segmentation - Aug 12, 2019.
Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few. We’ll now look at a number of research papers on covering state-of-the-art approaches to building semantic segmentation models.
Pages: 1 2
Tags: Image Classification, Image Recognition, Python, Segmentation
- Keras Callbacks Explained In Three Minutes - Aug 9, 2019.
A gentle introduction to callbacks in Keras. Learn about EarlyStopping, ModelCheckpoint, and other callback functions with code examples.
Tags: Explained, Keras, Neural Networks, Python
- Introduction to Image Segmentation with K-Means clustering - Aug 9, 2019.
Image segmentation is the classification of an image into different groups. Many kinds of research have been done in the area of image segmentation using clustering. In this article, we will explore using the K-Means clustering algorithm to read an image and cluster different regions of the image.
Tags: Clustering, Computer Vision, Image Recognition, K-means, Python, Segmentation
- Exploratory Data Analysis Using Python - Aug 7, 2019.
In this tutorial, you’ll use Python and Pandas to explore a dataset and create visual distributions, identify and eliminate outliers, and uncover correlations between two datasets.
Tags: ActiveState, Data Analysis, Data Exploration, Pandas, Python
- Feature selection by random search in Python - Aug 6, 2019.
Feature selection is one of the most important tasks in machine learning. Learn how to use a simple random search in Python to get good results in less time.
Tags: Collinearity, Cross-validation, Feature Selection, Python, Random
- 25 Tricks for Pandas - Aug 6, 2019.
Check out this video (and Jupyter notebook) which outlines a number of Pandas tricks for working with and manipulating data, covering topics such as string manipulations, splitting and filtering DataFrames, combining and aggregating data, and more.
Tags: Pandas, Python, Tips
- Lagrange multipliers with visualizations and code - Aug 6, 2019.
In this story, we’re going to take an aerial tour of optimization with Lagrange multipliers. When do we need them? Whenever we have an optimization problem with constraints.
Tags: Analytics, Mathematics, Optimization, Python
- Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree - Aug 2, 2019.
This cheatsheet should be easier to digest than the official documentation and should be a transitional tool to get students and beginners to get started reading documentations soon.
Tags: Beginners, Cheat Sheet, CoLaboratory, Deep Learning, Python, PyTorch, Udacity
- GPU Accelerated Data Analytics & Machine Learning - Aug 2, 2019.
The future is here! Speed up your Machine Learning workflow using Python RAPIDS libraries support.
Tags: Analytics, GPU, Machine Learning, Python
- How a simple mix of object-oriented programming can sharpen your deep learning prototype - Aug 1, 2019.
By mixing simple concepts of object-oriented programming, like functionalization and class inheritance, you can add immense value to a deep learning prototyping code.
Tags: Deep Learning, Keras, Programming, Python
- KDnuggets™ News 19:n28, Jul 31: Top 13 Skills To Become a Rockstar Data Scientist; Best Podcasts on AI, Analytics, Data Science - Jul 31, 2019.
Learn the essential skills needed to become a Data Science rockstar; Understand CNNs with Python + Tensorflow + Keras tutorial; Discover the best podcasts about AI, Analytics, Data Science; and find out where you can get the best Certificates in the field
Tags: Convolutional Neural Networks, Data Preparation, Data Science Certificate, Data Science Skills, Podcast, Python, TensorFlow
- Here’s how you can accelerate your Data Science on GPU - Jul 30, 2019.
Data Scientists need computing power. Whether you’re processing a big dataset with Pandas or running some computation on a massive matrix with Numpy, you’ll need a powerful machine to get the job done in a reasonable amount of time.
Tags: Big Data, Data Science, Deep Learning, GPU, NVIDIA, Python
- Exploring Python Basics. - Jul 29, 2019.
This free ebook is a great resource for data science beginners, providing a good introduction into Python, coding with Raspberry Pi, and using Python to building predictive models.
Tags: Beginners, Book, Manning, Python
Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras - Jul 26, 2019.
Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras.
Tags: Convolutional Neural Networks, Keras, Neural Networks, Python, TensorFlow
- Easy, One-Click Jupyter Notebooks - Jul 24, 2019.
All of the setup for software, networking, security, and libraries is automatically taken care of by the Saturn Cloud system. Data Scientists can then focus on the actual Data Science and not the tedious infrastructure work that falls around it
Tags: Big Data, Cloud, Data Science, Data Scientist, DevOps, Jupyter, Python
- Kaggle Kernels Guide for Beginners: A Step by Step Tutorial - Jul 23, 2019.
This is an attempt to hold the hands of a complete beginner and walk them through the world of Kaggle Kernels — for them to get started.
Tags: Kaggle, Python, R
- Things I Learned From the SciPy 2019 Lightning Talks - Jul 22, 2019.
This post summarizes the interesting aspects of the Day One of the SciPy 2019 lightning talks, a flash round of a dozen ~3 minute talks covering a wide variety of topics.
Tags: Presentation, Python, SciPy
- Computer Vision for Beginners: Part 1 - Jul 17, 2019.
Image processing is performing some operations on images to get an intended manipulation. Think about what we do when we start a new data analysis. We do some data preprocessing and feature engineering. It’s the same with image processing.
Tags: Computer Vision, Deep Learning, Image Processing, Python
- Dealing with categorical features in machine learning - Jul 16, 2019.
Many machine learning algorithms require that their input is numerical and therefore categorical features must be transformed into numerical features before we can use any of these algorithms.
Tags: Data Cleaning, Data Preprocessing, Feature Engineering, Machine Learning, Python
Training a Neural Network to Write Like Lovecraft - Jul 11, 2019.
In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out.
Tags: Keras, LSTM, Natural Language Generation, Neural Networks, Python, TensorFlow
- 10 Simple Hacks to Speed up Your Data Analysis in Python - Jul 11, 2019.
This article lists some curated tips for working with Python and Jupyter Notebooks, covering topics such as easily profiling data, formatting code and output, debugging, and more. Hopefully you can find something useful within.
Tags: Data Analysis, Jupyter, Pandas, Python, Tips
- How to Learn Python without First Needing to Learn Python - Jul 10, 2019.
Learn how data scientists and anyone coding with Python can set up a made-to-order runtime in minutes - not days. Read the 3-minute blog post.
Tags: ActiveState, Python
- A Gentle Guide to Starting Your NLP Project with AllenNLP - Jul 10, 2019.
For those who aren’t familiar with AllenNLP, I will give a brief overview of the library and let you know the advantages of integrating it to your project.
Tags: Allen Institute, NLP, Python, Sentiment Analysis
- Practical Speech Recognition with Python: The Basics - Jul 9, 2019.
Do you fear implementing speech recognition in your Python apps? Read this tutorial for a simple approach to getting practical with speech recognition using open source Python libraries.
Tags: Google, NLP, Python, Speech Recognition
- Annotated Heatmaps of a Correlation Matrix in 5 Simple Steps - Jul 9, 2019.
A heatmap is a graphical representation of data in which data values are represented as colors. That is, it uses color in order to communicate a value to the reader. This is a great tool to assist the audience towards the areas that matter the most when you have a large volume of data.
Tags: Data Visualization, Python, Statistics
- XGBoost and Random Forest with Bayesian Optimisation - Jul 8, 2019.
This article will explain how to use XGBoost and Random Forest with Bayesian Optimisation, and will discuss the main pros and cons of these methods.
Tags: Bayesian, Optimization, Python, Random Forests, XGBoost
- Classifying Heart Disease Using K-Nearest Neighbors - Jul 8, 2019.
I have written this post for the developers and assumes no background in statistics or mathematics. The focus is mainly on how the k-NN algorithm works and how to use it for predictive modeling problems.
Pages: 1 2
Tags: Healthcare, K-nearest neighbors, Machine Learning, Medical, Python
- Building a Recommender System, Part 2 - Jul 3, 2019.
This post explores an technique for collaborative filtering which uses latent factor models, a which naturally generalizes to deep learning approaches. Our approach will be implemented using Tensorflow and Keras.
Tags: Movies, Python, Recommendation Engine, Recommender Systems
- How do you check the quality of your regression model in Python? - Jul 2, 2019.
Linear regression is rooted strongly in the field of statistical learning and therefore the model must be checked for the ‘goodness of fit’. This article shows you the essential steps of this task in a Python ecosystem.
Tags: Collinearity, Data Science, Python, Regression, Statistics
- Make your Data Talk! - Jun 28, 2019.
Matplotlib and Seaborn are two of the most powerful and popular data visualization libraries in Python. Read on to learn how to create some of the most frequently used graphs and charts using Matplotlib and Seaborn.
Pages: 1 2 3
Tags: Data Visualization, Matplotlib, Python, Seaborn
- PySyft and the Emergence of Private Deep Learning - Jun 27, 2019.
PySyft is an open-source framework that enables secured, private computations in deep learning, by combining federated learning and differential privacy in a single programming model integrated into different deep learning frameworks such as PyTorch, Keras or TensorFlow.
Tags: Deep Learning, Differential Privacy, Privacy, Python, Security
- An Overview of Outlier Detection Methods from PyOD – Part 1 - Jun 27, 2019.
PyOD is an outlier detection package developed with a comprehensive API to support multiple techniques. This post will showcase Part 1 of an overview of techniques that can be used to analyze anomalies in data.
Tags: Algorithms, Big Data, Outliers, Python
- Top KDnuggets Tweets, Jun 19 – 25: Learn how to efficiently handle large amounts of data using #Pandas; The biggest mistake while learning #Python for #datascience - Jun 26, 2019.
Also: Data Science Jobs Report 2019; Harvard CS109 #DataScience Course, Resources #Free and Online; Google launches TensorFlow; Mastering SQL for Data Science
Tags: Advice, Pandas, Python, Top tweets
- Optimization with Python: How to make the most amount of money with the least amount of risk? - Jun 26, 2019.
Learn how to apply Python data science libraries to develop a simple optimization problem based on a Nobel-prize winning economic theory for maximizing investment profits while minimizing risk.
Tags: Finance, Investment, Optimization, Python, Risk Modeling, Stocks
- KDnuggets™ News 19:n24, Jun 26: Understand Cloud Services; Pandas Tips & Tricks; Master Data Preparation w/ Python - Jun 26, 2019.
Happy summer! This week on KDnuggets: Understanding Cloud Data Services; How to select rows and columns in Pandas using [ ], .loc, iloc, .at and .iat; 7 Steps to Mastering Data Preparation for Machine Learning with Python; Examining the Transformer Architecture: The OpenAI GPT-2 Controversy; Data Literacy: Using the Socratic Method; and much more!
Tags: Cloud, Data Preparation, Machine Learning, NLP, OpenAI, Pandas, Python
7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition - Jun 24, 2019.
Interested in mastering data preparation with Python? Follow these 7 steps which cover the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.
Tags: 7 Steps, Data Preparation, Data Preprocessing, Data Science, Data Wrangling, Machine Learning, Pandas, Python
- Top KDnuggets Tweets, Jun 12 – 18: The biggest mistake while learning #Python for #datascience; 5 practical statistical concepts for data scientists - Jun 19, 2019.
Also: Resources for developers transitioning into data science; Best Data Visualization Techniques for small and large data; Top Data Science and Machine Learning Methods Used in 2018, 2019
Tags: Advice, Python, Statistics, Top tweets
- How to select rows and columns in Pandas using [ ], .loc, iloc, .at and .iat - Jun 19, 2019.
Subset selection is one of the most frequently performed tasks while manipulating data. Pandas provides different ways to efficiently select subsets of data from your DataFrame.
Tags: Data Cleaning, Data Preparation, Jupyter, Pandas, Python
- One Simple Trick for Speeding up your Python Code with Numpy - Jun 19, 2019.
Looping over Python arrays, lists, or dictionaries, can be slow. Thus, vectorized operations in Numpy are mapped to highly optimized C code, making them much faster than their standard Python counterparts.
Tags: Big Data, numpy, Python
- KDnuggets™ News 19:n23, Jun 19: Useful Stats for Data Scientists; Python, TensorFlow & R Winners in Latest Job Report - Jun 19, 2019.
This week on KDnuggets: 5 Useful Statistics Data Scientists Need to Know; Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS; How to Learn Python for Data Science the Right Way; The Machine Learning Puzzle, Explained; Scalable Python Code with Pandas UDFs; and much more!
Tags: Data Science, Data Scientist, Machine Learning, Pandas, Python, R, Report, SAS, Scalability, Statistics, TensorFlow
- Python Users Come From All Sorts of Backgrounds - Jun 18, 2019.
Python users come from all sorts of backgrounds, but computer science skills make the difference between a Python apprentice and a Python master. Save 50% off Classic Computer Science Problems in Python today, using the code kdcsprob50 when you buy from manning.com.
Tags: Book, Computer Science, Manning, Python
- K-means Clustering with Dask: Image Filters for Cat Pictures - Jun 18, 2019.
How to recreate an original cat image with least possible colors. An interesting use case of Unsupervised Machine Learning with K Means Clustering in Python.
Tags: Clustering, Dask, Image Classification, Image Recognition, K-means, Python, Unsupervised Learning
- Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS - Jun 17, 2019.
Data science jobs continue to grow in 2019, and this report shares the change and spread of jobs by software over recent years.
Tags: Data Science, indeed, Jobs, Python, R, SAS, TensorFlow
- How to Use Python’s datetime - Jun 17, 2019.
Python's datetime package is a convenient set of tools for working with dates and times. With just the five tricks that I’m about to show you, you can handle most of your datetime processing needs.
Tags: Programming, Python, Time Series
- How to Learn Python for Data Science the Right Way - Jun 14, 2019.
The biggest mistake you can make while learning Python for data science is to learn Python programming from courses meant for programmers. Avoid this mistake, and learn Python the right way by following this approach.
Tags: Advice, Data Science, Jupyter, Matplotlib, Pandas, Python, scikit-learn, StatsModels
- Become a Pro at Pandas, Python’s Data Manipulation Library - Jun 13, 2019.
Pandas is one of the most popular Python libraries for cleaning, transforming, manipulating and analyzing data. Learn how to efficiently handle large amounts of data using Pandas.
Tags: Matplotlib, numpy, Pandas, Python, SQL
- Scalable Python Code with Pandas UDFs: A Data Science Application - Jun 13, 2019.
There is still a gap between the corpus of libraries that developers want to apply in a scalable runtime and the set of libraries that support distributed execution. This post discusses how to bridge this gap using the the functionality provided by Pandas UDFs in Spark 2.3+
Tags: Apache Spark, Big Data, Pandas, Python
- Top KDnuggets Tweets, Jun 5 – 11: A New Extension to Organize your Code on Jupyter Notebooks; Data Science Cheat Sheet - Jun 12, 2019.
Also: Cognitive Biases are Making Sure You Aren’t So Smart; 3 Machine Learning Books that Helped me Level Up as a Data Scientist; Mastering Intermediate Machine Learning with Python
Tags: Cheat Sheet, Jupyter, Python, Top tweets
- How to Automate Hyperparameter Optimization - Jun 12, 2019.
A step-by-step guide into performing a hyperparameter optimization task on a deep learning model by employing Bayesian Optimization that uses the Gaussian Process. We used the gp_minimize package provided by the Scikit-Optimize (skopt) library to perform this task.
Tags: Bayesian, Deep Learning, Hyperparameter, Machine Learning, Neural Networks, Optimization, Python, TensorFlow
What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem - Jun 10, 2019.
We identify the 6 tools in the modern open-source Data Science ecosystem, examine the Python vs R question, and determine which tools are used the most with Deep Learning and Big Data.
Tags: Anaconda, Apache Spark, Big Data Software, Deep Learning, Excel, Keras, Poll, Python, R, RapidMiner, scikit-learn, Software, SQL, Tableau, TensorFlow
- PyViz: Simplifying the Data Visualisation Process in Python - Jun 6, 2019.
There are python libraries suitable for basic data visualizations but not for complicated ones, and there are libraries suitable only for complex visualizations. Is there a single library that handles both these tasks efficiently? The answer is yes. It's PyViz
Tags: Data Visualization, GitHub, Matplotlib, Python
- The Whole Data Science World in Your Hands - Jun 5, 2019.
Testing MatrixDS capabilities on different languages and tools: Python, R and Julia. If you work with data you have to check this out.
Tags: Data Science, Data Scientist, Julia, Jupyter, MatrixDS, Python, R
- KDnuggets™ News 19:n21, Jun 5: Transitioning your Career to Data Science; 11 top Data Science, Machine Learning platforms; 7 Steps to Mastering Intermediate ML w. Python - Jun 5, 2019.
The results of KDnuggets 20th Annual Software Poll; How to transition to a Data Science career; Mastering Intermediate Machine Learning with Python ; Understanding Natural Language Processing (NLP); Backprop as applied to LSTM, and much more.
Tags: Backpropagation, Data Science Platform, LSTM, Machine Learning, NLP, Poll, Python
- The Hitchhiker’s Guide to Feature Extraction - Jun 3, 2019.
Check out this collection of tricks and code for Kaggle and everyday work.
Tags: Feature Engineering, Feature Extraction, Feature Selection, Kaggle, Python
- 7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition - Jun 3, 2019.
This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!
Tags: 7 Steps, Classification, Cross-validation, Dimensionality Reduction, Feature Engineering, Feature Selection, Image Classification, K-nearest neighbors, Machine Learning, Modeling, Naive Bayes, numpy, Pandas, PCA, Python, scikit-learn, Transfer Learning
Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis - May 30, 2019.
Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.
Pages: 1 2
Tags: Anaconda, Apache Spark, Deep Learning, Excel, Keras, Poll, Python, R, RapidMiner, scikit-learn, Software, SQL, TensorFlow
- Who is your Golden Goose?: Cohort Analysis - May 30, 2019.
Step-by-step tutorial on how to perform customer segmentation using RFM analysis and K-Means clustering in Python.
Pages: 1 2
Tags: Clustering, Data Analysis, K-means, Python, Retail
- Animations with Matplotlib - May 30, 2019.
Animations make even more sense when depicting time series data like stock prices over the years, climate change over the past decade, seasonalities and trends since we can then see how a particular parameter behaves with time.
Tags: Data Science, Data Visualization, Matplotlib, Python
- Boost Your Image Classification Model - May 27, 2019.
Check out this collection of tricks to improve the accuracy of your classifier.
Tags: fast.ai, Generative Adversarial Network, Image Classification, Image Recognition, Python
- Stylight: Sr Python Engineer – Evolving Systems (d/f/m) [Munich, Germany] - May 24, 2019.
Seeking an experienced python engineer to join our team in Munich. You’re interested in further optimizing and automating our pipeline as well as delivering high quality software? Then Stylight is your place to be.
Tags: Engineer, Germany, Munich, Python, Stylight
- Analyzing Tweets with NLP in Minutes with Spark, Optimus and Twint - May 24, 2019.
Social media has been gold for studying the way people communicate and behave, in this article I’ll show you the easiest way of analyzing tweets without the Twitter API and scalable for Big Data.
Pages: 1 2
Tags: Apache Spark, Big Data, Deep Learning, Machine Learning, NLP, Optimus, Python, Twint
- Extracting Knowledge from Knowledge Graphs Using Facebook’s Pytorch-BigGraph - May 22, 2019.
We are using the state-of-the-art Deep Learning tools to build a model for predict a word using the surrounding words as labels.
Pages: 1 2
Tags: Deep Learning, Facebook, Machine Learning, NLP, Python, PyTorch, word2vec
- Autoencoders: Deep Learning with TensorFlow’s Eager Execution - May 20, 2019.
Deep Learning has revolutionized the Machine Learning scene in the last years. Can we apply it to image compression? How well can a Deep Learning algorithm reconstruct pictures of kittens? What’s an autoencoder?
Tags: Autoencoder, Deep Learning, Neural Networks, Python, TensorFlow
- 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!
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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.
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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
