It is not always the big stuff or the latest packages that help improve the accuracy or performance of our #machine learning models. At times we overlook the basics of Machine Learning and rush to higher order solutions. When the solution is just right there in front of us. Below are 10 simple things you should remember to try first before throwing in the towel and jumping straight to RNNs and CNNs (of course there are datasets which merit you to start straight from LSTMs and BERT).Let us remind ourselves of our checklist before bringing out our Calculus skills. Try to understand as much about the domain as you can.

It is true that parts of data analytics are being automated every day like visualization and reporting, and I believe the trend will continue well into the future. I can understand this statement because many professions such as call center agents are being replaced by Chat bots and aspiring data analysts are afraid that if they start learning data analytics in a few years' time they too will be out of work. What is good about AI? When used on routine and repetitive tasks AI is very efficient. Finally, when we look at latest developments in "Strong" AI such as GANs, we see that machines can really take us to places we never imagined.

Get your team access to 3,500 top Udemy courses anytime, anywhere. Get your team access to 3,500 top Udemy courses anytime, anywhere. From beginner to advanced Learn how to select most important features and build simpler and more robust machine learning models. The course covers various ways of Feature Selection in complete Detail, Below are the Major categories of Methods covered:- 1. Filter Methods 2. Wrapper Methods 3. Embedded Methods 4. Genetic Algorithm 5. Other Advance Methods The videos include full code written in Python 3 (Jupyter notebook) that you can directly apply to your own data sets. So what are you waiting for?