Zero to Deep Learning Bootcamp One - Introduction to Data Science and Machine Learning

Developed with Catalit
This content is developed in partnership with Catalit
Duration4h 16m
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This Learning Path is the first of three Learning Paths in the Zero to Deep Learning Bootcamp Cloud Academy has developed in collaboration with Deep Learning expert Francesco Mosconi from Catalit. The Zero to Deep Learning Bootcamp has been developed to help you master Deep Learning in an interactive, self paced format.

Intended Audience
Anyone interested in getting started with practical applications of Data Science and Deep Learning. Whether you're just starting out with machine learning or a more experienced data scientist looking to add deep learning to the mix, this learning path will provide the necessary skills to serve as a solid foundation for you to continue learning after the course has been completed.

We recommend you complete What is Cloud Computing? course to gain an understanding of the fundamentals of cloud computing before beginning this Learning Path. No prior knowledge of data and machine learning is required.

This first learning Path introduces you to the principles of machine learning. We introduce you to the fundamentals of data science, machine learning ,and data modelling before we progress to more practical applications of data modeling and learning in Bootcamp Two and Bootcamp Three.
This Learning path comprises 2 hours of high definition video delivered in short lectures, practical exercises, and explanations. There is an assessment exam at the end of the Learning Path to check point your knowledge and add the relevant skills to your skills profile.

Learning Objectives

  • Recognize and explain the key principles of data science 
  • Recognize and explain the key principles of machine learning

Course Agenda
The learning path will start out covering simple data and structured data, then moving onto images, with sound, with text, and more complex data where deep learning comes to life. By the end of this learning path, you'll be able to recognize which problems can be solved with deep learning and organize data in a way that can be used by a neural network.

We welcome all feedback so please direct any comments or questions on this course to us at


Your certificate for this learning path

Learning Path Steps

1 courses

In this Zero to Deep Learning course has been expertly created to provide you with a strong foundation in machine learning and deep learning. So whether you're just starting out with your practice of machine learning or you're a more experienced data scient...

2 courses

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 unstru...

3 courses

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 ce...

4 exam-filled

Exam: Zero to Deep Learning - Bootcamp One - Introduction to Data Science and Machine Learning


Dec 17 2018

Updated the Introduction to Machine Learning Course 

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.