Original subtitles

0 Hellooooo! I'm super super excited to start and hopefully you are too. 1

But in order for us to uncover this world of data science and machine learning we need to understand 2

what we are learning and where we're going to end up. 3

So we have a clear path to success. 4

Now this course has over 300+ videos that are broken down into sections. 5

So let's go over the sections so you know what the plan is. 6

First, we start off with a really fun section machine learning 101. what is machine learning? we're 7

going to play around with some fun tools and understand what this whole craze around machine learning is so 8

that you're able to explain this to your friends family and dog. 9

Okay maybe not the dog. 10

Once we get comfortable with the idea of machine learning we understand a history and how we got here 11

and how it works on a high level. 12

We then have two paths for you to follow. 13

One is if you don't know Python or have never programmed in your life. Well we're going to teach you some 14

Python so that you're able to follow the rest of the course. 15

The other path is for those who are already familiar with programming and python and want to just dive 16

straight in so you can pick Python or we keep going with the course. 17

The next part is about work environment. 18

We all want to have a professional setup that you're going to use in real life scenarios. 19

So we're going to introduce you to topics like Jupyter notebooks, Conda and virtual environments so that 20

by the end of the section you have a professional setup. 21

And when you go into work on your first day on the job you understand exactly what you need to install 22

on your computer. 23

We then move on to data analysis how do we analyze this data that we have using libraries like pandas. 24

Then we learn about a very important library when it comes to data science and that is NumPy, a fundamental 25

tool for all data scientists. 26

Then we move on to data visualizations. 27

This is gonna be really fun because we get to work with libraries like matplotlib that allows us to 28

make really neat graphs and visuals to describe our data. 29

We then go into the very popular scikit-learn. If you want to get into machine learning, 30

You need to know this library and scikit-learn allows us to use models and train models and check how 31

accurate our machine learning models are. 32

In that section we're going to learn a complete workflow for a machine learning project. 33

Then things get interesting. 34

This is when we start working on real life project and actually dive deep into machine learning. 35

We're going to learn about supervised learning about neural networks, transfer learning, deep learning. 36

We're going to do projects on classification, regression. 37

We're going to build models around time series data. 38

Now we're not going to shy away from difficult topics here. 39

We're going to introduce you, especially later on in the course, to advanced topics like deep learning, 40

neural networks, and transfer learning and we use the latest version of TensorFlow and Keras to do fun 41

projects like image classifications, transfer learning. 42

We're even going to show you how to use GPUs on your models to accelerate the training. 43

This is going to be a really fun part where we actually work on real life projects and we're going to have 44

notebooks and workbooks by the end of it to show off on your portfolio. We then get into data engineering. 45

Data engineering is actually a whole field in itself but as a data scientist you need to understand 46

what they do and what the big high level concepts are. 47

On topics like Hadoop and Spark so that you know how they're used in the industry and you can communicate 48

with data engineers. 49

This is a part that's often missing in a lot of courses. 50

But one of my favorite parts is this last part: the storytelling and communication. something that we're 51

very excited about because it's a topic so important but often forgotten that is in order for you to 52

be a successful machine learning and data science engineer, 53

You need to be able to communicate your work, present your work to management, to boss, to your co-workers. 54

So using our experience working in the industry we're going to show you how to work on your storytelling and 55

communication to present your project and to really stand out from all your colleagues. 56

Data Science is a popular field and in order for you to succeed we want to go beyond just the basics and 57

communication is a big part of that. 58

As you can see we have a lot to cover here. 59

A lot of videos and a lot of exercises. 60

But I promise you it's going to be a lot of fun. 61

As a matter of fact, we're going to follow a storyline where you get hired at a company and all these 62

tasks are going to be thrown at you by a boss. 63

And we've mimicked these tasks based on our experience working for companies so that when you land your 64

first job... well, you wont have any surprises or at least you're used to the work environment. 65

By the end of it all this is all going to fit in together and make sense from the very beginning of 66

machine learning and data science basics to the very end with building our own projects. 67

We're going to take you from zero to mastery but you know what the best part of this course is? Our online 68

community. 69

We have thousands of developers chatting every day helping each other out solving problems together 70

just talking about the latest and greatest in programming data and the tech world. 71

Now this is an optional resource for you to use so you can have back and forth conversation with other 72

students and myself and Daniel. 73

The ideas for you to feel like you're part of a classroom and you're not doing this all by yourself 74

but you know what? 75

Enough talk. 76

I know you're getting excited. 77

I am too. 78

So let's get started in the next video. 79

It's your first day at work and we're going to start this course. 80

Let's start learning and see why, 81

Being a data scientist has become one of the most in demand skills in the world. 82

Let's get started.