0
1
00:00:00,210 --> 00:00:05,830
Hellooooo! I'm super super excited to start and hopefully you are too.
1

2
00:00:05,940 --> 00:00:11,700
But in order for us to uncover this world of data science and machine learning we need to understand
2

3
00:00:11,820 --> 00:00:14,870
what we are learning and where we're going to end up.
3

4
00:00:14,880 --> 00:00:17,660
So we have a clear path to success.
4

5
00:00:17,760 --> 00:00:22,260
Now this course has over 300+ videos that are broken down into sections.
5

6
00:00:22,320 --> 00:00:26,290
So let's go over the sections so you know what the plan is.
6

7
00:00:26,310 --> 00:00:32,540
First, we start off with a really fun section machine learning 101. what is machine learning? we're
7

8
00:00:32,540 --> 00:00:38,940
going to play around with some fun tools and understand what this whole craze around machine learning is so
8

9
00:00:38,940 --> 00:00:42,560
that you're able to explain this to your friends family and dog.
9

10
00:00:42,910 --> 00:00:44,310
Okay maybe not the dog.
10

11
00:00:44,430 --> 00:00:49,650
Once we get comfortable with the idea of machine learning we understand a history and how we got here
11

12
00:00:49,860 --> 00:00:52,460
and how it works on a high level.
12

13
00:00:52,470 --> 00:00:55,050
We then have two paths for you to follow.
13

14
00:00:55,410 --> 00:01:02,820
One is if you don't know Python or have never programmed in your life. Well we're going to teach you some
14

15
00:01:02,820 --> 00:01:06,190
Python so that you're able to follow the rest of the course.
15

16
00:01:06,420 --> 00:01:12,240
The other path is for those who are already familiar with programming and python and want to just dive
16

17
00:01:12,240 --> 00:01:16,700
straight in so you can pick Python or we keep going with the course.
17

18
00:01:16,770 --> 00:01:19,640
The next part is about work environment.
18

19
00:01:19,650 --> 00:01:24,090
We all want to have a professional setup that you're going to use in real life scenarios.
19

20
00:01:24,090 --> 00:01:30,450
So we're going to introduce you to topics like Jupyter notebooks, Conda and virtual environments so that
20

21
00:01:30,510 --> 00:01:33,430
by the end of the section you have a professional setup.
21

22
00:01:33,600 --> 00:01:39,120
And when you go into work on your first day on the job you understand exactly what you need to install
22

23
00:01:39,270 --> 00:01:40,650
on your computer.
23

24
00:01:40,650 --> 00:01:47,440
We then move on to data analysis how do we analyze this data that we have using libraries like pandas.
24

25
00:01:47,730 --> 00:01:54,000
Then we learn about a very important library when it comes to data science and that is NumPy, a fundamental
25

26
00:01:54,000 --> 00:01:56,720
tool for all data scientists.
26

27
00:01:56,760 --> 00:01:59,490
Then we move on to data visualizations.
27

28
00:01:59,520 --> 00:02:04,980
This is gonna be really fun because we get to work with libraries like matplotlib that allows us to
28

29
00:02:04,980 --> 00:02:09,390
make really neat graphs and visuals to describe our data.
29

30
00:02:09,390 --> 00:02:14,380
We then go into the very popular scikit-learn. If you want to get into machine learning,
30

31
00:02:14,490 --> 00:02:21,870
You need to know this library and scikit-learn allows us to use models and train models and check how
31

32
00:02:21,870 --> 00:02:24,770
accurate our machine learning models are.
32

33
00:02:24,930 --> 00:02:30,300
In that section we're going to learn a complete workflow for a machine learning project.
33

34
00:02:30,300 --> 00:02:32,350
Then things get interesting.
34

35
00:02:32,370 --> 00:02:38,450
This is when we start working on real life project and actually dive deep into machine learning.
35

36
00:02:38,550 --> 00:02:43,560
We're going to learn about supervised learning about neural networks, transfer learning, deep learning.
36

37
00:02:43,560 --> 00:02:46,890
We're going to do projects on classification, regression.
37

38
00:02:46,920 --> 00:02:49,650
We're going to build models around time series data.
38

39
00:02:49,650 --> 00:02:52,980
Now we're not going to shy away from difficult topics here.
39

40
00:02:52,980 --> 00:02:58,230
We're going to introduce you, especially later on in the course, to advanced topics like deep learning,
40

41
00:02:58,230 --> 00:03:05,160
neural networks, and transfer learning and we use the latest version of TensorFlow and Keras to do fun
41

42
00:03:05,160 --> 00:03:08,460
projects like image classifications, transfer learning.
42

43
00:03:08,460 --> 00:03:14,780
We're even going to show you how to use GPUs on your models to accelerate the training.
43

44
00:03:14,790 --> 00:03:19,710
This is going to be a really fun part where we actually work on real life projects and we're going to have
44

45
00:03:19,950 --> 00:03:26,860
notebooks and workbooks by the end of it to show off on your portfolio. We then get into data engineering.
45

46
00:03:27,310 --> 00:03:32,830
Data engineering is actually a whole field in itself but as a data scientist you need to understand
46

47
00:03:32,890 --> 00:03:36,340
what they do and what the big high level concepts are.
47

48
00:03:36,430 --> 00:03:42,400
On topics like Hadoop and Spark so that you know how they're used in the industry and you can communicate
48

49
00:03:42,400 --> 00:03:44,230
with data engineers.
49

50
00:03:44,230 --> 00:03:47,670
This is a part that's often missing in a lot of courses.
50

51
00:03:48,010 --> 00:03:54,460
But one of my favorite parts is this last part: the storytelling and communication. something that we're
51

52
00:03:54,460 --> 00:04:00,910
very excited about because it's a topic so important but often forgotten that is in order for you to
52

53
00:04:00,910 --> 00:04:04,150
be a successful machine learning and data science engineer,
53

54
00:04:04,210 --> 00:04:10,890
You need to be able to communicate your work, present your work to management, to boss, to your co-workers.
54

55
00:04:10,990 --> 00:04:16,660
So using our experience working in the industry we're going to show you how to work on your storytelling and
55

56
00:04:16,660 --> 00:04:21,880
communication to present your project and to really stand out from all your colleagues.
56

57
00:04:21,880 --> 00:04:28,660
Data Science is a popular field and in order for you to succeed we want to go beyond just the basics and
57

58
00:04:28,660 --> 00:04:30,880
communication is a big part of that.
58

59
00:04:31,030 --> 00:04:33,170
As you can see we have a lot to cover here.
59

60
00:04:33,190 --> 00:04:36,100
A lot of videos and a lot of exercises.
60

61
00:04:36,100 --> 00:04:38,600
But I promise you it's going to be a lot of fun.
61

62
00:04:38,680 --> 00:04:43,840
As a matter of fact, we're going to follow a storyline where you get hired at a company and all these
62

63
00:04:43,840 --> 00:04:46,970
tasks are going to be thrown at you by a boss.
63

64
00:04:47,170 --> 00:04:53,350
And we've mimicked these tasks based on our experience working for companies so that when you land your
64

65
00:04:53,350 --> 00:04:58,390
first job... well, you wont have any surprises or at least you're used to the work environment.
65

66
00:04:58,390 --> 00:05:03,890
By the end of it all this is all going to fit in together and make sense from the very beginning of
66

67
00:05:03,920 --> 00:05:08,920
machine learning and data science basics to the very end with building our own projects.
67

68
00:05:08,980 --> 00:05:14,770
We're going to take you from zero to mastery but you know what the best part of this course is? Our online
68

69
00:05:14,770 --> 00:05:15,760
community.
69

70
00:05:15,790 --> 00:05:21,070
We have thousands of developers chatting every day helping each other out solving problems together
70

71
00:05:21,380 --> 00:05:26,560
just talking about the latest and greatest in programming data and the tech world.
71

72
00:05:26,560 --> 00:05:31,600
Now this is an optional resource for you to use so you can have back and forth conversation with other
72

73
00:05:31,600 --> 00:05:33,970
students and myself and Daniel.
73

74
00:05:34,060 --> 00:05:39,220
The ideas for you to feel like you're part of a classroom and you're not doing this all by yourself
74

75
00:05:39,880 --> 00:05:40,700
but you know what?
75

76
00:05:40,900 --> 00:05:41,870
Enough talk.
76

77
00:05:41,950 --> 00:05:43,330
I know you're getting excited.
77

78
00:05:43,360 --> 00:05:44,240
I am too.
78

79
00:05:44,260 --> 00:05:46,510
So let's get started in the next video.
79

80
00:05:46,510 --> 00:05:50,120
It's your first day at work and we're going to start this course.
80

81
00:05:50,170 --> 00:05:52,020
Let's start learning and see why,
81

82
00:05:52,020 --> 00:05:57,430
Being a data scientist has become one of the most in demand skills in the world.
82

83
00:05:57,430 --> 00:05:58,120
Let's get started.