) KDnuggets™ News 19:n25, Jul 10: 5 Probability Distributions for Data Scientists; What the Machine Learning Engineer Job is Really Like
Tags: Data Science, Data Scientist, Distribution, Machine Learning, Machine Learning Engineer, NLP, NVIDIA, Probability, Risk Modeling
This edition of the KDnuggets newsletter is double-sized after taking the holiday week off. Learn about probability distributions every data scientist should know, what the machine learning engineering job is like, making the most money with the least amount of risk, the difference between NLP and NLU, get a take on Nvidia's new data science workstation, and much, much more.
Features | Tutorials | Opinions | News | Webcasts | Meetings | Jobs | Academic | Tops | Image of the week
Features
Tutorials, Overviews
Opinions
News
Webcasts and Webinars
Meetings
Jobs
Academic
Top Stories, Tweets
Image of the week
Features
5 Probability Distributions Every Data Scientist Should Know
What's the Machine Learning Engineering Job Like
- Optimization with Python: How to make the most amount of money with the least amount of risk?
- NLP vs. NLU: from Understanding a Language to Its Processing
- Nvidia's New Data Science Workstation - a Review and Benchmark
- XLNet Outperforms BERT on Several NLP Tasks
- State of AI Report 2019
- Top 8 Data Science Use Cases in Construction
Tutorials, Overviews
- Practical Speech Recognition with Python: The Basics
- Annotated Heatmaps of a Correlation Matrix in 5 Simple Steps
- Collaborative Evolutionary Reinforcement Learning
- XGBoost and Random Forest with Bayesian Optimisation
- Classifying Heart Disease Using K-Nearest Neighbors
- Building a Recommender System, Part 2
- Examining the Transformer Architecture - Part 2: A Brief Description of How Transformers Work
- 4 Most Popular Alternative Data Sources Explained
- Seven Key Dimensions to Help You Understand Artificial Intelligence Environments
- How do you check the quality of your regression model in Python?
- Make your Data Talk!
- An Overview of Outlier Detection Methods from PyOD - Part 1
- 10 Gradient Descent Optimisation Algorithms + Cheat Sheet
Opinions
- Why you're not a job-ready data scientist (yet)
- How Data Science Is Used Within the Film Industry
- Cartoon: AI + Self-Driving + BBQ = ?
- A Data Scientist's Path to Understanding Market Simulation
- An Overview of Human Pose Estimation with Deep Learning
- How To Get Funding For AI Startups
- PySyft and the Emergence of Private Deep Learning
- Why do we need AWS SageMaker?
News
Webcasts and Webinars
Meetings
Jobs
Academic
- Monash University: Research Fellow or Sr Research Fellow in Information Technology [Suzhou, China]
- Monash University: (Senior) Research Fellow in AI [Suzhou, China]
Top Stories, Tweets
- Top Stories, Jul 1-7: 5 Probability Distributions Every Data Scientist Should Know; NLP vs. NLU: from Understanding a Language to Its Processing
- Top Stories, Jun 24-30: Understanding Cloud Data Services; 7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition
- Top KDnuggets tweets, Jun 26 - Jul 02: An End-to-End Project on Time Series Analysis and Forecasting with #Python; The biggest mistake while learning #Python for #datascience
- 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
Image of the week
From Optimization with Python: How to make the most amount of money with the least amount of risk?
