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  5. Getting Started With Deep Learning: Working With Data

Exercise 4: Solution

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Duration1h 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



Hey guys, welcome back. Exercise four. In exercise four we were asked to plot the weights of the males and the females using a box plot. So to do that I decided to go the route of pivoting my data so that the females and males are split in two different columns by gender so I'll take the values of weight and pivot them in two different columns by gender. So if I do that, what I obtain is a data frame of the same length as the original data frame. Let's check it. Info, okay. Still has 10,000 entries but only and two columns, one is female, the other is male but the male values are non-null only where the males data is and the female values are non-null only where the female data is. Okay, so we can plot this. Now we have two columns, one for males and one for females and we can do just plot kind box and it will automatically draw our two populations. Thank you for watching and see you in the next video.

About the Author

Learning paths3

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.