Exercise 1: Solution

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Machine learning is a branch of artificial intelligence that deals with learning patterns and rules from training data. In this lesson from Cloud Academy, you will learn all about its structure and history. Its origins date back to the middle of the last century, but in the last decade, companies have taken advantage of the resource for their products. This revolution of machine learning has been enabled by three factors.

First, memory storage has become economic and accessible. Second, computing power has also become readily available. Third, sensors, phones, and web application have produced a lot of data which has contributed to training these machine learning models. This lesson will guide you to the basic principles, foundations, and best practices of machine learning. It is advisable to be able to understand and explain these basics before diving into deep learning and neural nets. This lesson is made up of 10 lectures and two accompanying exercises with solutions. This Cloud Academy lesson is part of the wider Data and Machine Learning course.

Learning Objectives

  • Learn about the foundations and history of machine learning
  • Learn and understand the principles of memory storage, computing power, and phone/web applications

Intended Audience

It is recommended to complete the Introduction to Data and Machine Learning lesson before taking this lesson.

Resources

The datasets and code used throughout this lesson can be found in the GitHub repo here.

 

About the Author
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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.   

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