## The course is part of these learning paths

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 scientist that is adding deep learning to the mix, this course will provide the necessary skills to serve as a solid foundation for you to continue learning after the course has been completed.

The course 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 the course, 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. Understanding and learning how to build a neural network model, including fully connected, convolutional, and recurrent neural network and train a model using cloud computing, all within this course.

This course is made up of 5 cohesive lectures that start off the journey into Data and Machine Learning with Cloud Academy.

**Learning Objectives**

- Learn the key principles of data and machine learning
- Come away with a strong foundation of the subject in order to develop new skills further
- Understanding and learning how to build neural network models

**Intended Audience**

- No prior knowledge of data and machine learning required

Hey guys. Welcome back. In this video, I'm going to walk you through how the material of the course is organized. In particular, show you where the lectures are, where the exercises are, and where the solutions to the exercises are. So I've just launched Jupyter Notebook within the Python environment as explained in the previous lecture, and as you can see there are a few folders and a few files in this folder, in the course folder, so we've already seen the environment.yml file that contains the specification for the environment. We have already seen the read me file because it's what you find on the GitHub notebook, it contains the instructions on how to get started. What I want to show you now are the three folders, the course folder, the data folder, and the solutions folder. So let's start with the course folder. If I click on the course folder, you see there is 10 notebooks. The first one, zero, check environment, is just a notebook that allows you to make sure that everything is in place in your environment and that you've correctly installed all the required packages. So the other notebooks, starting from one up to nine contain the material that is explained in the lecture, so for example, in section one, we will use notebook one. And as you can see, this notebook is pretty short and does not have any exercises.

So we will execute this, I'm not gonna talk about the code, we'll see it in the lecture but this is section one. From section two onwards, the notebook contains a few exercises at the end. So during this section, we'll review parts of the code and then at the end, there are a few exercises. So let me scroll down, here they are. So for example in section two, we have five exercises. And these are for you to complete and fill, so there are actually two ways in which you can proceed with this course. You can proceed top to bottom and use the lectures starting in material and then do the exercises, or you could also proceed vice versa. You can start from the notebook and the exercises and then go back to either the code along lectures or to the theory lectures if there are things you don't know how to do. So feel free to proceed in whatever way you find more convenient. Start from the beginning and go through the lectures and do the exercises, or start from the exercises and then go check the lectures when you feel you need some help. Okay, so this is for the notebooks of the lectures, let me walk out of the course folder now and go to the solutions folder. So in the solutions folder, you have notebooks that provide solutions to the problems presented in the exercises. So for example if I click on two, here is a notebook that has the solutions to the problems presented in section two.

Not gonna show you to them, so my advice is that you really try to solve the problems on your own. Check in for the solutions when you're really stuck or even better, when you've finished and you want to compare your solution with our solution. Keep in mind that our solutions are only possible solutions to problems, very often there is more than one solution and so if you solve the problems in a different way, that doesn't mean it's wrong. It could bee very well right and there are many ways to solve the problems we are presenting. Okay, so here are the solutions. In the course, we will do walk throughs of both the problems and the solutions. So you can review the solutions also as a screen cast. Finally, I'm gonna talk about the data folder. In the data folder, you have a few datasets that we will use throughout the course and feel free to have a look at them. Mostly are CSV files, not very big, few other small files. In general these are just the data that we'll use during the course. Thank you for watching and see you in the next video.

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.