Exercise 5
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1h 5m

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.

 Learning Objectives

  • Learn and understand the functions of machine learning when confronted with structured and unstructured data
  • Be able to explain the importance of deep learning



The Github repo for this course, including code and datasets, can be found here.


Hey, guys, welcome back. This video is about exercise five in section two. In exercise five, we will load a different data set. It's the Titanic train data set. This is a data set with information about passengers of the Titanic. It has a few columns containing the gender of the people, the fare they paid, the name, passenger ID, the cabin, the port, where they got embarked, and also if they survived or they died in the Titanic tragedy. What you're asked in this exercise is to learn about a particular type of plot called scattermatrix and use it to display the data. So it's pretty simple exercise. Here's the link that tells you how to use the scattermatrix. Just have to take some code from there and use it to display the Titanic data set. As usual, try to do the exercise first, and then feel free to watch the solution. Good luck.

About the Author
Learning Paths

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.