Machine learning is a branch of artificial intelligence that deals with learning patterns and rules from training data. In this course 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 course 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 course is made up of 10 lectures and two accompanying exercises with solutions. This Cloud Academy course is part of the wider Data and Machine Learning learning path.
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 course before taking this course.
Resources
The datasets and code used throughout this course can be found in the GitHub repo here.
Hello and welcome to section 3 on Machine Learning. Machine learning is a branch of artificial intelligence that deals with learning patterns and rules from training data. Its origins date back to the middle of the last century, but it's really in the last decade that companies have used it massively for their products. This revolution of machine learning has been enabled by really three factors. First of all, memory storage has become really cheap and accessible. Second, computing power has also become really cheap and accessible. And third, sensors, phones, and web application have produced a lot of data which has contributed to training these machine learning models. So the same is true for deep learning.
In the last few years, we have seen a revolution in the revolution, with deep learning enabled by the same three factors kind of taking the stage and becoming very popular. Before we dive into deep learning and neural nets, we need to make sure we speak the same language and we are familiar with some concepts that are general in machine learning. So if you're familiar with things like, "What is a regression? What is a classification?" How to do a cross validation and how to draw a computer matrix, feel free to skip this section and just check your knowledge with the exercise. Make sure you understand fully what cost function and parameter optimizations are, because these two are really the pillars upon which we'll build the rest of the course. So let's get started with section 3, Machine Learning.
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.