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.
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
Prerequisites
- It would be recommended to complete the Introduction to Data and Machine Learning course, before starting.
Resources
The Github repo for this course, including code and datasets, can be found here.
Hello and welcome to section two on data. Data comes in many forms and formats. 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. This is one of the reasons why deep learning is so popular. It can handle many different data types. So in this section, we'll learn about data, and the different formats it can have, and we'll meet two vital libraries that allow us to explore and organize data. These are pandas, for data manipulation and exploration, and matplotlib for data visualization. So let's get started with section two on data.
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.