It is impossible to say "This model will work best for this kind of data". If we can say that, we would not need people to try and build models, we could automate the process. So how can we choose optimal model for our problem and how can we make that process faster ? How can we make our training so that when we ship our model and it is live, it will be generalizable ?
After looking at those subjects we will continue with using our trained model to understand our data more and then build a stronger model with those insights and then use a new model to understand our data more. After iterating over this process couple of times we will have strong insight into our data and we will have a better model. Also, we will see which prediction of our model we should trust more and will investigate powerful tool that allows us to go beyond prediction and make simulation using our model and answer how changing our input will affect our outputs (this is very valuable in cases like you want to have more customer coming to your business and want to reduce the number of customers leaving etc...)
This course will give you the intuitive understanding of the concepts without memorization. This course combines both theory and practice to give students deep understanding of the subject and hands-on experience via coding.
Students must have experience in coding in Python language and are expected to have knowledge about Pandas and Numpy Libraries. If they don't, taking python coding and python for data science lectures before this course is recommended.
Individuals comfortable with basic programming and machine learning looking to master machine-learning techniques.
Practical Machine Learning
Model Driven EDA
Join our public courses in our Switzerland facilities. Private class trainings will be organized at the location of your preference, according to your schedule.