Introduction A Broad Overview Video Lecture Transcript
This transcript was automatically generated, so there may be discrepancies between the video and the text.


16:22:29 Hi! Everybody! Welcome back in this video. We're just going to give a broad overview of the content.
16:22:34 That's going to be covered in our data science boot, camp educational materials.
16:22:38 Let's go ahead and share that Jupiter notebook, and we'll get started.
16:22:42 So this notebook's not gonna have any code. It's just gonna have some words that I'm gonna use as a background one.
16:22:49 As I describe what we're going to be covering in our data science boot camp.
16:22:53 So the goal of the data science boot Camp is to be able to give you the tools to be able to complete complete and end-to-end data science portfolio.
16:23:03 Worthy project. So what do we mean by portfolio, worthy it's something that you would be happy and thrilled to show somebody in a job interview, or any sort of presentation setting where people want to know what kind of projects you're working on that are related to data science so as with that
16:23:19 goal in mind, we're going to cover a wide array of things.
16:23:21 We're going to start off with data collection in order to work a data science project or machine learning project, you need to have data.
16:23:29 So we're going to start by showing you where are some places that you can get data.
16:23:33 Or how can you collect data yourself along the way? This, we're not gonna do this sequentially.
16:23:39 But along the way, as we keep going we're going to learn little bits and pieces about data.
16:23:42 Analysis and exploration which include things like exploratory plots, basic statistics, examination as well as data manipulation with pandas and numpy.
16:23:52 A lot of the time. The data that you get in the real world is not very clean.
16:23:56 So we'll also touch on some nice techniques for cleaning the data some very basic techniques, nothing in depth but for cleaning data, cleaning text data imputing missing values as well as creating new columns, by manipulating existing columns and then we're going to
16:24:11 get to the meat and potatoes of the various algorithms.
16:24:14 So we're going to learn about something called supervised learning, which you'll see in a later video.
16:24:18 If you continue on these include methods about regretression as well as classification, and include something called ensemble learning, which work for both regression and classification problems.
16:24:31 Again, you'll see what these mean and more depth if they're unfamiliar to you, we'll have a little bit about unsupervised learning primarily dimension, reduction techniques like Pca and Te Sny as well as Clustering techniques called
16:24:45 Kme and hierarchical clustering. I will note that not all of these techniques will be covered in the live version of the boot camp.
16:24:54 But all of these techniques will have videos about them corresponding to our Jupiter notebooks.
16:25:00 Finally, we'll talk about neural networks, which kind of are their own thing that allow you to do a lot of different things.
16:25:06 We're gonna do just the very basics of neural network.
16:25:09 So we'll touch on the history with perceptrons.
16:25:13 Talk about feedforward networks as well as give an introduction to convolutional neural networks as well as recurrent neural networks. By the end.
16:25:20 You'll also see some tips and tricks on how to present your findings and disseminate your projects to others, which is key particularly for the boot camp, because you will be submitting a project presentation at the end.
16:25:32 If you'd like to complete the boot camp entirely, so that's a nice broad overview of what we're gonna do.
16:25:40 So now that you have that overview, you're ready to start the materials and honest.
16:25:44 So I hope you enjoyed learning about what you're going to learn.
16:25:47 And I think now you're ready to actually start learning it so I hope to see it in the next video.