Topics: Tutorials, Overviews
DataCamp - Easiest Way to Learn Data Science
|
Learning R? Take this Intro to R for Data Science Tutorial. 
|
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
Comprehensive Guide to Learning Python for Data Science
Latest:
-
Microsoft Research Uses Transfer Learning to Train Real-World Autonomous Drones - Mar 31, 2020.
The new research uses policies learned in simulations in real world drone environments. -
How (not) to use Machine Learning for time series forecasting: The sequel - Mar 30, 2020.
Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn't real. Follow this example to learn how to spot trouble in time series data before it's too late. -
Brain Tumor Detection using Mask R-CNN - Mar 30, 2020.
Mask R-CNN has been the new state of the art in terms of instance segmentation. Here I want to share some simple understanding of it to give you a first look and then we can move ahead and build our model. -
COVID-19 Visualized: The power of effective visualizations for pandemic storytelling - Mar 27, 2020.
Clear, succinct data visualizations can be powerful tools for telling stories and explaining phenomena. This article demonstrates this concept as relates to the COVID-19 pandemic. -
Introduction to Kubeflow MPI Operator and Industry Adoption - Mar 27, 2020.
Kubeflow just announced its first major 1.0 release recently. This post introduces the MPI Operator, one of the core components of Kubeflow, currently in alpha, which makes it easy to run synchronized, allreduce-style distributed training on Kubernetes. -
How To Painlessly Analyze Your Time Series - Mar 26, 2020.
The Matrix Profile is a powerful tool to help solve this dual problem of anomaly detection and motif discovery. Matrix Profile is robust, scalable, and largely parameter-free: we’ve seen it work for a wide range of metrics including website user data, order volume and other business-critical applications. -
Diffusion Map for Manifold Learning, Theory and Implementation - Mar 25, 2020.
This article aims to introduce one of the manifold learning techniques called Diffusion Map. This technique enables us to understand the underlying geometric structure of high dimensional data as well as to reduce the dimensions, if required, by neatly capturing the non-linear relationships between the original dimensions. -
Top AI Resources – Directory for Remote Learning - Mar 24, 2020.
Whether you are just learning Data Science, a current professional, or just interested, it's crucial to keep the mind stimulated and stay current. With conferences, schools, and travel largely canceled because of #coronavirus, these remote resources will help you stay engaged. -
Graph Neural Network model calibration for trusted predictions - Mar 24, 2020.
In this article, we’ll talk about calibration in graph machine learning, and how it can help to build trust in these powerful new models. -
Made With ML: Discover, build, and showcase machine learning projects - Mar 23, 2020.
This is a short introduction to Made With ML, a useful resource for machine learning engineers looking to get ideas for projects to build, and for those looking to share innovative portfolio projects once built. -
Exploring TensorFlow Quantum, Google’s New Framework for Creating Quantum Machine Learning Models - Mar 23, 2020.
TensorFlow Quantum allow data scientists to build machine learning models that work on quantum architectures. -
Build an Artificial Neural Network From Scratch: Part 2 - Mar 20, 2020.
The second article in this series focuses on building an Artificial Neural Network using the Numpy Python library. -
24 Best (and Free) Books To Understand Machine Learning - Mar 20, 2020.
We have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field. -
A Comprehensive Data Repository for Fake Health News Detection - Mar 19, 2020.
We introduce the FakeHealth, a new data repository for fake health news detection. Following a preliminary analysis to demonstrate its features, we consider additional potential directions for better identifying fake news. -
The 4 Best Jupyter Notebook Environments for Deep Learning - Mar 19, 2020.
Many cloud providers, and other third-party services, see the value of a Jupyter notebook environment which is why many companies now offer cloud hosted notebooks that are hosted on the cloud. Let's have a look at 3 such environments. -
A Top Machine Learning Algorithm Explained: Support Vector Machines (SVM) - Mar 18, 2020.
Support Vector Machines (SVMs) are powerful for solving regression and classification problems. You should have this approach in your machine learning arsenal, and this article provides all the mathematics you need to know -- it's not as hard you might think. -
Time Series Classification Synthetic vs Real Financial Time Series - Mar 18, 2020.
This article discusses distinguishing between real financial time series and synthetic time series using XGBoost. -
A Beginner’s Guide to Data Integration Approaches in Business Intelligence - Mar 18, 2020.
An integrated BI system has a trickle-down effect on all business processes, especially reporting and analytics. Find out how integration can help you leverage the power of BI. -
Salesforce Open Sources a Framework for Open Domain Question Answering Using Wikipedia - Mar 16, 2020.
The framework uses a multi-hop QA method to answer complex questions by reasoning through Wikipedia’s datasets. -
Decision Boundary for a Series of Machine Learning Models - Mar 13, 2020.
I train a series of Machine Learning models using the iris dataset, construct synthetic data from the extreme points within the data and test a number of Machine Learning models in order to draw the decision boundaries from which the models make predictions in a 2D space, which is useful for illustrative purposes and understanding on how different Machine Learning models make predictions. -
How To Build Your Own Feedback Analysis Solution - Mar 12, 2020.
Automating the analysis of customer feedback will sound like a great idea after reading a couple hundred reviews. Building an NLP solution to provide in-depth analysis of what your customers are thinking is a serious undertaking, and this guide helps you scope out the entire project. -
Few-Shot Image Classification with Meta-Learning - Mar 12, 2020.
Here is how you can teach your model to learn quickly from a few examples. -
Google Open Sources TFCO to Help Build Fair Machine Learning Models - Mar 12, 2020.
A new optimization framework helps to incorporate fairness constraints in machine learning models. -
Python Pandas For Data Discovery in 7 Simple Steps - Mar 10, 2020.
Just getting started with Python's Pandas library for data analysis? Or, ready for a quick refresher? These 7 steps will help you become familiar with its core features so you can begin exploring your data in no time. -
The Berlin Rent Freeze: How many illegal overpriced offers can I find online? - Mar 10, 2020.
This post presents an analysis of Berlin online real estate listings, investigating a controversial law capping rents in the state, which went into effect on February 23. Are current landlords already respecting the new rent cap? -
Generate Realistic Human Face using GAN - Mar 10, 2020.
This article contain a brief intro to Generative Adversarial Network(GAN) and how to build a Human Face Generator. -
20+ Machine Learning Datasets & Project Ideas - Mar 9, 2020.
Upgrading your machine learning, AI, and Data Science skills requires practice. To practice, you need to develop models with a large amount of data. Finding good datasets to work with can be challenging, so this article discusses more than 20 great datasets along with machine learning project ideas for you to tackle today. -
50 Must-Read Free Books For Every Data Scientist in 2020 - Mar 9, 2020.
In this article, we are listing down some excellent data science books which cover the wide variety of topics under Data Science. -
A Crash Course in Game Theory for Machine Learning: Classic and New Ideas - Mar 9, 2020.
Game theory is experiencing a renaissance driven by the evolution of AI. What are some classic and new ideas that data scientists should be aware of. -
Analyzing GDPR Fines – who are largest violators? - Mar 6, 2020.
Fines from the GDPR have been rolling in since its inception in 2018. This article investigates who are the largest penalty recipients by country, the amounts, and private individuals. -
Tokenization and Text Data Preparation with TensorFlow & Keras - Mar 6, 2020.
This article will look at tokenizing and further preparing text data for feeding into a neural network using TensorFlow and Keras preprocessing tools. -
Phishytics – Machine Learning for Detecting Phishing Websites - Mar 6, 2020.
Since phishing is such a widespread problem in the cybersecurity domain, let us take a look at the application of machine learning for phishing website detection. -
Trends in Machine Learning in 2020 - Mar 5, 2020.
Many industries realize the potential of Machine Learning and are incorporating it as a core technology. Progress and new applications of these tools are moving quickly in the field, and we discuss expected upcoming trends in Machine Learning for 2020. -
TensorFlow 2.0 Tutorial: Optimizing Training Time Performance - Mar 5, 2020.
Tricks to improve TensorFlow training time with tf.data pipeline optimizations, mixed precision training and multi-GPU strategies. -
Recreating Fingerprints using Convolutional Autoencoders - Mar 4, 2020.
The article gets you started working with fingerprints using Deep Learning. -
Linear to Logistic Regression, Explained Step by Step - Mar 3, 2020.
Logistic Regression is a core supervised learning technique for solving classification problems. This article goes beyond its simple code to first understand the concepts behind the approach, and how it all emerges from the more basic technique of Linear Regression. -
The Augmented Scientist Part 1: Practical Application Machine Learning in Classification of SEM Images - Mar 3, 2020.
Our goal here is to see if we can build a classifier that can identify patterns in Scanning Electron Microscope (SEM) images, and compare the performance of our classifier to the current state-of-the-art. -
5 Google Colaboratory Tips - Mar 2, 2020.
Are you looking for some tips for using Google Colab for your projects? This article presents five you may find useful. -
Uber Unveils a New Service for Backtesting Machine Learning Models at Scale - Mar 2, 2020.
The transportation giant built a new service and architecture for backtesting forecasting models.
March:
-
The Big Bad NLP Database: Access Nearly 300 Datasets
Check out this database of nearly 300 freely-accessible NLP datasets, curated from around the internet. - Hands on Hyperparameter Tuning with Keras Tuner
- Decision Tree Intuition: From Concept to Application
- Introducing fastpages: An easy to use blogging platform with extra features for Jupyter Notebooks
- Data Science Curriculum for self-study
- Image Recognition and Object Detection in Retail
-
Python and R Courses for Data Science
Since Python and R are a must for today's data scientists, continuous learning is paramount. Online courses are arguably the best and most flexible way to upskill throughout ones career.
-
Probability Distributions in Data Science
Some machine learning models are designed to work best under some distribution assumptions. Therefore, knowing with which distributions we are working with can help us to identify which models are best to use.
-
Free Mathematics Courses for Data Science & Machine Learning
It's no secret that mathematics is the foundation of data science. Here are a selection of courses to help increase your maths skills to excel in data science, machine learning, and beyond. - Audio Data Analysis Using Deep Learning with Python (Part 2)
- Microsoft Open Sources ZeRO and DeepSpeed: The Technologies Behind the Biggest Language Model in History
- Passive Data Collection and Actionable Results: What to Know
- How Kubeflow Can Add AI to Your Kubernetes Deployments
- The Forgotten Algorithm
- Getting Started with R Programming
- Audio Data Analysis Using Deep Learning with Python (Part 1)
-
20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1)
2020 is well underway, and we bring you 20 AI, data science, and machine learning terms we should all be familiar with as the year marches onward.
- Using the Fitbit Web API with Python
- Using AI to Identify Wildlife in Camera Trap Images from the Serengeti
- Inside The Machine Learning that Google Used to Build Meena: A Chatbot that Can Chat About Anything
-
Fourier Transformation for a Data Scientist
The article contains a brief intro into Fourier transformation mathematically and its applications in AI.
- Introduction to Geographical Time Series Prediction with Crime Data in R, SQL, and Tableau
- Adversarial Validation Overview
- Practical Hyperparameter Optimization
- Easy Image Dataset Augmentation with TensorFlow
- Sharing your machine learning models through a common API
- Illustrating the Reformer
- How to learn data science on your own: a practical guide
- Basics of Audio File Processing in R
- Recommender System Metrics: Comparing Apples, Oranges and Bananas
- Observability for Data Engineering
- Intent Recognition with BERT using Keras and TensorFlow 2
- Amazon Uses Self-Learning to Teach Alexa to Correct its Own Mistakes
- Large Scale Adversarial Representation Learning
- Understanding Density-based Clustering
- Getting up and Running with Python: Installing Anaconda on Windows
- Intro to Machine Learning and AI based on high school knowledge
- Create Your Own Computer Vision Sandbox
- Optimal Estimation Algorithms: Kalman and Particle Filters
- Audio File Processing: ECG Audio Using Python
- Serverless Machine Learning with R on Cloud Run
- Microsoft Open Sources Jericho to Train Reinforcement Learning Using Linguistic Games
-
12-Hour Machine Learning Challenge: Build & deploy an app with Streamlit and DevOps tools
This article will present the knowledge, process, tools, and frameworks required for completing a 12-hour ML challenge. I hope you can find it useful for your personal or professional projects.
