All language subtitles for 1. Course Outline

Hellooooo! I'm super super excited to start and hopefully you are too.
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But in order for us to uncover this world of data science and machine learning we need to understand
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what we are learning and where we're going to end up.
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So we have a clear path to success.
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Now this course has over 300+ videos that are broken down into sections.
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So let's go over the sections so you know what the plan is.
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First, we start off with a really fun section machine learning 101. what is machine learning? we're
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going to play around with some fun tools and understand what this whole craze around machine learning is so
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that you're able to explain this to your friends family and dog.
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Okay maybe not the dog.
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Once we get comfortable with the idea of machine learning we understand a history and how we got here
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and how it works on a high level.
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We then have two paths for you to follow.
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One is if you don't know Python or have never programmed in your life. Well we're going to teach you some
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Python so that you're able to follow the rest of the course.
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The other path is for those who are already familiar with programming and python and want to just dive
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straight in so you can pick Python or we keep going with the course.
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The next part is about work environment.
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We all want to have a professional setup that you're going to use in real life scenarios.
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So we're going to introduce you to topics like Jupyter notebooks, Conda and virtual environments so that
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by the end of the section you have a professional setup.
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And when you go into work on your first day on the job you understand exactly what you need to install
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on your computer.
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We then move on to data analysis how do we analyze this data that we have using libraries like pandas.
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Then we learn about a very important library when it comes to data science and that is NumPy, a fundamental
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tool for all data scientists.
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Then we move on to data visualizations.
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This is gonna be really fun because we get to work with libraries like matplotlib that allows us to
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make really neat graphs and visuals to describe our data.
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We then go into the very popular scikit-learn. If you want to get into machine learning,
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You need to know this library and scikit-learn allows us to use models and train models and check how
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accurate our machine learning models are.
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In that section we're going to learn a complete workflow for a machine learning project.
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Then things get interesting.
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This is when we start working on real life project and actually dive deep into machine learning.
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We're going to learn about supervised learning about neural networks, transfer learning, deep learning.
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We're going to do projects on classification, regression.
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We're going to build models around time series data.
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Now we're not going to shy away from difficult topics here.
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We're going to introduce you, especially later on in the course, to advanced topics like deep learning,
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neural networks, and transfer learning and we use the latest version of TensorFlow and Keras to do fun
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projects like image classifications, transfer learning.
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We're even going to show you how to use GPUs on your models to accelerate the training.
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This is going to be a really fun part where we actually work on real life projects and we're going to have
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notebooks and workbooks by the end of it to show off on your portfolio. We then get into data engineering.
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Data engineering is actually a whole field in itself but as a data scientist you need to understand
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what they do and what the big high level concepts are.
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On topics like Hadoop and Spark so that you know how they're used in the industry and you can communicate
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with data engineers.
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This is a part that's often missing in a lot of courses.
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But one of my favorite parts is this last part: the storytelling and communication. something that we're
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very excited about because it's a topic so important but often forgotten that is in order for you to
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be a successful machine learning and data science engineer,
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You need to be able to communicate your work, present your work to management, to boss, to your co-workers.
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So using our experience working in the industry we're going to show you how to work on your storytelling and
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communication to present your project and to really stand out from all your colleagues.
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Data Science is a popular field and in order for you to succeed we want to go beyond just the basics and
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communication is a big part of that.
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As you can see we have a lot to cover here.
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A lot of videos and a lot of exercises.
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But I promise you it's going to be a lot of fun.
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As a matter of fact, we're going to follow a storyline where you get hired at a company and all these
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tasks are going to be thrown at you by a boss.
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And we've mimicked these tasks based on our experience working for companies so that when you land your
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first job... well, you wont have any surprises or at least you're used to the work environment.
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By the end of it all this is all going to fit in together and make sense from the very beginning of
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machine learning and data science basics to the very end with building our own projects.
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We're going to take you from zero to mastery but you know what the best part of this course is? Our online
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community.
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We have thousands of developers chatting every day helping each other out solving problems together
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just talking about the latest and greatest in programming data and the tech world.
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Now this is an optional resource for you to use so you can have back and forth conversation with other
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students and myself and Daniel.
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The ideas for you to feel like you're part of a classroom and you're not doing this all by yourself
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but you know what?
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Enough talk.
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I know you're getting excited.
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I am too.
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So let's get started in the next video.
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It's your first day at work and we're going to start this course.
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Let's start learning and see why,
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Being a data scientist has become one of the most in demand skills in the world.
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Let's get started.