Understanding time based patterns is critical for any business. Questions like how much inventory to maintain, how much footfall do you expect in your store to how many people will travel by an airline – all of these are important time series problems to solve. This is why time series forecasting is one of the must-know techniques for any data scientist. From predicting the weather to the sales of a product, it is integrated into the data science ecosystem and that makes it a mandatory addition to a data scientist’s skillset. If you are a beginner, time series also provides a good way to start working on real life projects. You can relate to time series very easily and they help you enter the larger world of machine learning.
Flow charts are an important part of a clinical trial report. Making them can be a pain though. One good way to do it seems to be with the grid and Gmisc packages in R. X and Y coordinates can be designated based on the center of the boxes in normalized device coordinates (proportions of the device space – 0.5 is this middle) which saves a lot of messing around with corners of boxes and arrows. A very basic flow chart, based very roughly on the CONSORT version, can be generated as follows…
1. Assuming your data is ready to use — and all you need
2. Not exploring your data set before starting work
3. Expecting too much
4. Not using a control group to test your new data model in action
5. Starting with targets rather than hypotheses
6. Letting your data model go stale
7. Automating without monitoring the final outcome
9. Picking too complex a tool
10. Reusing implementations that don’t fit your problem
11. Misunderstanding fundamentals like causation and cross validation
12. Underestimating what users can understand
In this article, we show that the issue with polynomial regression is not over-fitting, but numerical precision. Even if done right, numerical precision still remains an insurmountable challenge. We focus here on step-wise polynomial regression, which is supposed to be more stable than the traditional model. In step-wise regression, we estimate one coefficient at a time, using the classic least square technique.
AI is becoming more and more human like – basically the vision that was set out when this term was coined. It’s a little scary how good machines are getting but it’s exciting in equal measure. The potential of helping mankind is limitless and with the amount of research going on at the big tech giants like Google, AI will only keeping getting better.
1. Boston Dynamic’s Helping Robot
2. Amazon’s Warehouse Robots
3. An Autonomous Bike Driving Robot
4. Google’s DeepMind AI Taught itself How to Walk, Run, Jump..
5. An Interview with Sophia, the Robot
6. What’s new, Atlas
7. Flippy, the Burger Cook