Exercise 5: Solution
The course is part of these learning paths
Learn the ways in which data comes in many forms and formats with the second course in the Data and Machine Learning series.
Traditionally, machine learning has worked really well with structured data but is not as efficient in solving problems with unstructured data. Deep learning works very well with both structured and unstructured data, and it has had successes in fields like translation, and image classification, and many others. Learn and study how to explain the reasons deep learning is so popular. With many different data types, learn about its different formats, and we'll analyze the vital libraries that allow us to explore and organize data.
This course is made up of 8 lectures, accompanied by 5 engaging exercises along with their solutions. This course is part of the Data and Machine Learning learning paths from Cloud Academy.
- Learn and understand the functions of machine learning when confronted with structured and unstructured data
- Be able to explain the importance of deep learning
- It would be recommended to complete the Introduction to Data and Machine Learning course, before starting.
The Github repo for this course, including code and datasets, can be found here.
Hey guys, welcome back. Last exercise, exercise number five. Load the data set of the of the Titanic. By now you know that it's easy. And check it with a head. And then load this scatter matrix from pandas. Scatter matrix takes the whole data frame. Notice that I'm dropping the passenger ID. The column passenger ID. And then I'm just setting the figure size. So what do we have here? We have a grid of plots where each plot is the comparison of two columns. So we have survived against all the other columns. But we also have age against all the other columns. And we have siblings/spouses, parent/children, fare, and so on. This plot is useful to digitally check for any immediate signs of correlation. So if two columns display a correlation, you will definitely see it in this plot. I hope I've convinced you that the libraries we're gonna be using, pandas, macholib, and NumPy offer a wide variety of methods to explore data and extract meaningful insights. So thank you for watching and see you in the next section.
I am a Data Science consultant and trainer. With Catalit I help companies acquire skills and knowledge in data science and harness machine learning and deep learning to reach their goals. With Data Weekends I train people in machine learning, deep learning and big data analytics. I served as lead instructor in Data Science at General Assembly and The Data Incubator and I was Chief Data Officer and co-founder at Spire, a Y-Combinator-backed startup that invented the first consumer wearable device capable of continuously tracking respiration and activity. I earned a joint PhD in biophysics at University of Padua and Université de Paris VI and graduated from Singularity University summer program of 2011.