Tutorials, Overviews
DataCamp - Easiest Way to Learn Data Science
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Learning R? Take this Intro to R for Data Science Tutorial. 
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Learning Python? Take this Intro to Python for Data Science Tutorial. |
Check also these fantastic posts:
R Learning Path: From beginner to expert in R in 7 steps
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Latest:
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Deploying Secure and Scalable Streamlit Apps on AWS with Docker Swarm, Traefik and Keycloak - Oct 23, 2020.
If you are a data scientist who just wants to get the work done but doesn’t necessarily want to go down the DevOps rabbit hole, this tutorial offers a relatively straightforward deployment solution leveraging Docker Swarm and Traefik, with an option of adding user authentication with Keycloak. -
DeepMind Relies on this Old Statistical Method to Build Fair Machine Learning Models - Oct 23, 2020.
Causal Bayesian Networks are used to model the influence of fairness attributes in a dataset. -
Behavior Analysis with Machine Learning and R: The free eBook - Oct 22, 2020.
Check out this new free ebook to learn how to leverage the power of machine learning to analyze behavioral patterns from sensor data and electronic records using R. -
Which flavor of BERT should you use for your QA task? - Oct 22, 2020.
Check out this guide to choosing and benchmarking BERT models for question answering. -
10 Underrated Python Skills - Oct 21, 2020.
Tips for feature analysis, hyperparameter tuning, data visualization and more. -
Deploying Streamlit Apps Using Streamlit Sharing - Oct 20, 2020.
Read this sneak peek into Streamlit’s new deployment platform. -
Data Science in the Cloud with Dask - Oct 20, 2020.
Scaling large data analyses for data science and machine learning is growing in importance. Dask and Coiled are making it easy and fast for folks to do just that. Read on to find out how. -
Feature Ranking with Recursive Feature Elimination in Scikit-Learn - Oct 19, 2020.
This article covers using scikit-learn to obtain the optimal number of features for your machine learning project. -
How to Explain Key Machine Learning Algorithms at an Interview - Oct 19, 2020.
While preparing for interviews in Data Science, it is essential to clearly understand a range of machine learning models -- with a concise explanation for each at the ready. Here, we summarize various machine learning models by highlighting the main points to help you communicate complex models. -
Roadmap to Natural Language Processing (NLP) - Oct 19, 2020.
Check out this introduction to some of the most common techniques and models used in Natural Language Processing (NLP). -
Optimizing the Levenshtein Distance for Measuring Text Similarity - Oct 16, 2020.
For speeding up the calculation of the Levenshtein distance, this tutorial works on calculating using a vector rather than a matrix, which saves a lot of time. We’ll be coding in Java for this implementation. -
Deep Learning for Virtual Try On Clothes – Challenges and Opportunities - Oct 16, 2020.
Learn about the experiments by MobiDev for transferring 2D clothing items onto the image of a person. As part of their efforts to bring AR and AI technologies into virtual fitting room development, they review the deep learning algorithms and architecture under development and the current state of results. -
Fast Gradient Boosting with CatBoost - Oct 16, 2020.
In this piece, we’ll take a closer look at a gradient boosting library called CatBoost. -
fastcore: An Underrated Python Library - Oct 15, 2020.
A unique python library that extends the python programming language and provides utilities that enhance productivity. -
How to ace the data science coding challenge - Oct 15, 2020.
Preparing to interview for a Data Scientist position takes preparation and practice, and then it could all boil down to a final review of your skills. Based on personal experience, these tips on how to approach such a review will help you excel in the coding challenge project for your next interview. -
Text Mining with R: The Free eBook - Oct 15, 2020.
This freely-available book will show you how to perform text analytics in R, using packages from the tidyverse. -
Free From MIT: Intro to Computational Thinking and Data Science - Oct 14, 2020.
This free course from MIT will help in your transition to thinking computationally, and ultimately solving complex data science problems. -
Goodhart’s Law for Data Science and what happens when a measure becomes a target? - Oct 14, 2020.
When developing analytics and algorithms to better understand a business target, unintended biases can sneak in that ensure desired outcomes are obtained. Guiding your work with multiple metrics in mind can help avoid such consequences of Goodhart's Law. -
Getting Started with PyTorch - Oct 14, 2020.
A practical walkthrough on how to use PyTorch for data analysis and inference. -
The Future of Fake News - Oct 13, 2020.
Let's talk about misleading communications in the digital era. -
Software Engineering Tips and Best Practices for Data Science - Oct 13, 2020.
Bringing your work as a Data Scientist into the real-world means transforming your experiments, test, and detailed analysis into great code that can be deployed as efficient and effective software solutions. You must learn how to enable your machine learning algorithms to integrate with IT systems by taking them out of your notebooks and delivering them to the business by following software engineering standards. -
Uber Open Sources the Third Release of Ludwig, its Code-Free Machine Learning Platform - Oct 13, 2020.
The new release makes Ludwig one of the most complete open source AutoML stacks in the market. -
5 Best Practices for Putting Machine Learning Models Into Production - Oct 12, 2020.
Our focus for this piece is to establish the best practices that make an ML project successful. -
How to be a 10x data scientist - Oct 12, 2020.
If you are a Data Scientist looking to make it to the next level, then there are many opportunities to up your game and your efficiency to stand out from the others. Some of these recommendations that you can follow are straightforward, and others are rarely followed, but they will all pay back in dividends of time and effectiveness for your career. -
Exploring The Brute Force K-Nearest Neighbors Algorithm - Oct 12, 2020.
This article discusses a simple approach to increasing the accuracy of k-nearest neighbors models in a particular subset of cases. -
Annotated Machine Learning Research Papers - Oct 9, 2020.
Check out this collection of annotated machine learning research papers, and no longer fear their reading. -
How I Levelled Up My Data Science Skills In 8 Months - Oct 9, 2020.
Read how the author used their time to level up a variety of their data science skills over a short period of time, and learn how you could do the same. -
Strategies of Docker Images Optimization - Oct 8, 2020.
Large Docker images lengthen the time it takes to build and share images between clusters and cloud providers. When creating applications, it’s therefore worth optimizing Docker Images and Dockerfiles to help teams share smaller images, improve performance, and debug problems. -
How LinkedIn Uses Machine Learning in its Recruiter Recommendation Systems - Oct 8, 2020.
LinkedIn uses some very innovative machine learning techniques to optimize candidate recommendations. -
Free Introductory Machine Learning Course From Amazon - Oct 7, 2020.
Amazon's Machine Learning University offers an introductory course titled Accelerated Machine Learning, which is a good starting place for those looking for a foundation in generalized practical ML. -
A step-by-step guide for creating an authentic data science portfolio project - Oct 7, 2020.
Especially if you are starting out launching yourself as a Data Scientist, you will want to first demonstrate your skills through interesting data science project ideas that you can implement and share. This step-by-step guide shows you how to do go through this process, with an original example that explores Germany’s biggest frequent flyer forum, Vielfliegertreff. -
10 Best Machine Learning Courses in 2020 - Oct 6, 2020.
If you are ready to take your career in machine learning to the next level, then these top 10 Machine Learning Courses covering both practical and theoretical work will help you excel. -
A Guide to Preparing OpenCV for Android - Oct 6, 2020.
This tutorial guides Android developers in preparing the popular library OpenCV for use. Using a step-by-step guide, the library will be imported into Android Studio and then can be used for performing any of the operations it supports, such as object detection, segmentation, tracking, and more. -
Your Guide to Linear Regression Models - Oct 5, 2020.
This article explains linear regression and how to program linear regression models in Python. -
Key Machine Learning Technique: Nested Cross-Validation, Why and How, with Python code - Oct 5, 2020.
Selecting the best performing machine learning model with optimal hyperparameters can sometimes still end up with a poorer performance once in production. This phenomenon might be the result of tuning the model and evaluating its performance on the same sets of train and test data. So, validating your model more rigorously can be key to a successful outcome. -
Getting Started in AI Research - Oct 5, 2020.
A guide on how to contribute to confirming the reproducibility of some of the most recent papers and join open-search research. -
Data Protection Techniques Needed to Guarantee Privacy - Oct 2, 2020.
This article takes a look at the concepts of data privacy and personal data. It presents several privacy protection techniques and explains how they contribute to preserving the privacy of individuals. -
10 Days With “Deep Learning for Coders” - Oct 1, 2020.
Read about the author's experience with the course and the book from fast.ai. -
Understanding Transformers, the Data Science Way - Oct 1, 2020.
Read this accessible and conversational article about understanding transformers, the data science way — by asking a lot of questions that is.
September:
- AI in Healthcare: A review of innovative startups
- Machine Learning Model Deployment
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The Best Free Data Science eBooks: 2020 Update
The author has updated their list of best free data science books for 2020. Read on to see what books you should grab. - Missing Value Imputation – A Review
- International alternatives to Kaggle for Data Science / Machine Learning competitions
- How AI is Driving Innovation in Astronomy
- Looking Inside The Blackbox: How To Trick A Neural Network
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Geographical Plots with Python
When your data includes geographical information, rich map visualizations can offer significant value for you to understand your data and for the end user when interpreting analytical results.
- The Online Courses You Must Take to be a Better Data Scientist
- Making Python Programs Blazingly Fast
- Create and Deploy your First Flask App using Python and Heroku
- Causal Inference: The Free eBook
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Introduction to Time Series Analysis in Python
Data that is updated in real-time requires additional handling and special care to prepare it for machine learning models. The important Python library, Pandas, can be used for most of this work, and this tutorial guides you through this process for analyzing time-series data. - The Most Complete Guide to PyTorch for Data Scientists
- LinkedIn’s Pro-ML Architecture Summarizes Best Practices for Building Machine Learning at Scale
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How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy
Data science work typically requires a big lift near the end to increase the accuracy of any model developed. These five recommendations will help improve your machine learning models and help your projects reach their target goals.
- Artificial Intelligence for Precision Medicine and Better Healthcare
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Machine Learning from Scratch: Free Online Textbook
If you are looking for a machine learning starter that gets right to the core of the concepts and the implementation, then this new free textbook will help you dive in to ML engineering with ease. By focusing on the basics of the underlying algorithms, you will be quickly up and running with code you construct yourself.
- Statistical and Visual Exploratory Data Analysis with One Line of Code
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Automating Every Aspect of Your Python Project
Every Python project can benefit from automation using Makefile, optimized Docker images, well configured CI/CD, Code Quality Tools and more…
- What is Simpson’s Paradox and How to Automatically Detect it
- The Insiders’ Guide to Generative and Discriminative Machine Learning Models
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Implementing a Deep Learning Library from Scratch in Python
A beginner’s guide to understanding the fundamental building blocks of deep learning platforms.
- Can Neural Networks Show Imagination? DeepMind Thinks They Can
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Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities
We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.
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Autograd: The Best Machine Learning Library You’re Not Using?
If there is a Python library that is emblematic of the simplicity, flexibility, and utility of differentiable programming it has to be Autograd.
- DIY Election Fraud Analysis Using Benford’s Law
- Visualization Of COVID-19 New Cases Over Time In Python
- Lessons From My First Kaggle Competition
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Deep Learning’s Most Important Ideas
In the field of deep learning, there continues to be a deluge of research and new papers published daily. Many well-adopted ideas that have stood the test of time provide the foundation for much of this new work. To better understand modern deep learning, these techniques cover the basic necessary knowledge, especially as a starting point if you are new to the field.
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Statistics with Julia: The Free eBook
This free eBook is a draft copy of the upcoming Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. Interested in learning Julia for data science? This might be the best intro out there.
- Understanding Bias-Variance Trade-Off in 3 Minutes
- Feature Engineering for Numerical Data
- An Introduction to NLP and 5 Tips for Raising Your Game
- AI Papers to Read in 2020
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Free From MIT: Intro to Computer Science and Programming in Python
This free introductory computer science and programming course is available via MIT's Open Courseware platform. It's a great resource for mastering the fundamentals of one of data science's major requirements.
- 4 Tools to Speed Up Your Data Science Writing
- 4 Tricks to Effectively Use JSON in Python
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Creating Powerful Animated Visualizations in Tableau
In this post we explore animated data visualization in Tableau,one of the tool's powerful features for making visualizations appealing and interactive.
- A Deep Learning Dream: Accuracy and Interpretability in a Single Model
- Data Scientists think data is their #1 problem. Here’s why they’re wrong.
- Design of Experiments in Data Science
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How to Evaluate the Performance of Your Machine Learning Model
You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.
- 10 Things You Didn’t Know About Scikit-Learn
- Computer Vision Recipes: Best Practices and Examples
- Which methods should be used for solving linear regression?
- PyCaret 2.1 is here: What’s new?
- Showcasing the Benefits of Software Optimizations for AI Workloads on Intel® Xeon® Scalable Platforms
