In the last article of this series we talked about generation of a test set and also the different ways in which we can generate it (i.e. Random sampling, Stratified sampling). Once we have separated our dataset into training set and test set we will not look at our test set until we have finished […]
Tag Archives: ML Series
ML Series – #0:- AI, ML, Auto-ML, Cognitive Services and all that Jazz!!!
We live in a world of buzzwords!! Media, news, professional and self opinioned blogs, articles and magazines don’t really help when it comes to untangling the mesh of words that come with any new technological advancement! The latest buzzwords in the happening “IT” world today are AI, ML, Deep Learning, Cognitive Services, Auto-ML and the […]
ML Series–#6:- Is Random Sampling good enough?
Picking up on the last article where we talked about the first few steps we take when exploring our data, the last step i.e Data cleaning is probably the most time and resource intensive step. One of the first things you do when presented with a data sample (probably even before the Explore and Correlate […]
ML Series – #5:- The first few baby steps to an ML network!
Continuing with our ML series , it is finally time to load up some data and start having some fun! Now that we are familiar with Python, pandas and matplotlib we know that there are a set of basic commands that we can use to get through the first few steps. We created a mind […]
ML Series – #4:- Our Learning Path for Python!
Now that we are done with the basics of Machine Learning, what it is, how many types , why is data oh-so important ,its time to get our hands dirty!! When it comes to programming there really is no other way to learn other than …by…coding We were quite new to the world Python when […]
ML Series – #3:-Data in/ Data out or Garbage in/ Garbage out?
Now that we have looked at the broad categories in which we can slot Machine Learning programs, it is time to focus on the MOST important aspect of a ML system :- Data. Understanding your data, analyzing it , cleaning it and preparing it for Machine Learning is an extremely tedious ( maybe only 2nd […]