An Introduction to Machine Learning for Data Engineers
Review from similar course:
Another Excellent course from a brilliant Instructor. Really well explained, and precisely the right amount of information. Mike provides clear and concise explanations and has a deep subject knowledge of Google’s Cloud.
— Julie Johnson
Welcome to An Introduction to Machine Learning for Data Engineers. This course is part of my series for data engineering. The course is a prerequisite for my course titled Tensorflow on the Google Cloud Platform for Data Engineers.
This course will show you the basics of machine learning for data engineers. The course is geared towards answering questions for the Google Certified Data Engineering exam.
This is NOT a general course or introduction to machine learning. This is a very focused course for learning the concepts you’ll need to know to pass the Google Certified Data Engineering Exam.
At this juncture, the Google Certified Data Engineer is the only real world certification for data and machine learning engineers.
Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The key part of that definition is “without being explicitly programmed.”
The vast majority of applied machine learning is supervised machine learning. The word applied means you build models in the real world. Supervised machine learning is a type of machine learning that involves building models from data that exists.
A good way to think about supervised machine learning is: If you can get your data into a tabular format, like that of an excel spreadsheet, then most machine learning models can model it.
In the course, we’ll learn the different types of algorithms used. We will also cover the nomenclature specific to machine learning. Every discipline has their own vernacular and data science is not different.
You’ll also learn why the Python programming language has emerged as the gold standard for building real world machine learning models.
Additionally, we will write a simple neural network and walk through the process and the code step by step. Understanding the code won’t be as important as understanding the importance and effectiveness of one simple artificial neuron.
*Five Reasons to take this Course.*
1) You Want to be a Data Engineer
It’s the number one job in the world. (not just within the computer space) The growth potential career wise is second to none. You want the freedom to move anywhere you’d like. You want to be compensated for your efforts. You want to be able to work remotely. The list of benefits goes on.
2) The Google Certified Data Engineer
Google is always ahead of the game. If you were to look back at a timeline of their accomplishments in the data space you might believe they have a crystal ball. They’ve been a decade ahead of everyone. Now, they are the first and the only cloud vendor to have a data engineering certification. With their track record I’ll go with Google.
3) The Growth of Data is Insane
Ninety percent of all the world’s data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 Exabytes a day. That number doubles every month.
4) Machine Learning in Plain English
Machine learning is one of the hottest careers on the planet and understanding the basics is required to attaining a job as a data engineer. Google expects data engineers to be able to build machine learning models. In this course, we will cover all the basics of machine learning at a very high level.
5) You want to be ahead of the Curve
The data engineer role is fairly new. While you’re learning, building your skills and becoming certified you are also the first to be part of this burgeoning field. You know that the first to be certified means the first to be hired and first to receive the top compensation package.
Thanks for your interest in An Introduction to Machine Learning for Data Engineers.
What are we going to cover in this course.
Machine learning but specific to Google's Cloud.
Yes... there are some differences.
Let's learn what a section is according to Udemy and find out what's in this lesson.
Are you the target audience?
I want this course to be what you are looking for.
What is machine learning.
Let's define it.
There are two types of machine learning and 99% of all applied machine learning is one type.
In this lecture let's learn about the process of building machine learning models.
You'll do the same thing time after time when you begin building your machine learning models.
Every career has it's own vernacular and machine learning is no different.
Let's learn some key terms to get started.
Model Building in Python
Let's learn why Python has become the gold standard for building machine learning models.
In this lesson let's learn how to create a virtual machine to house our datalab notebooks.
Our cloud datalab notebooks are pretty intuitive but in this lesson let's learn some navigation basics.
It's your turn.
In this lab you create a datalab for your notebooks.
Do keep in mind you are paying for this.
Much of machine learning is data massage.
Let's learn about data wrangling in this lesson.
Just a friendly reminder these next few lessons are quick.
In this lesson let's learn how to massage our data in Pandas.
The core data structure you'll use often is the Pandas dataframe.
In this lesson let's learn what that is and how to use it.
A dataframe is like an excel spreadsheet.
Let's get hands on with the in this lesson.
Machine Learning algorithms
Linear regression is one of the most basic machine learning models and most used.
Let's define what they are in this lesson.
It sounds scary but the basic of it aren't.
Let's learn about Naive Bayes in this lesson.
Decision trees form the basis of a lot of other algorithms.
Let's learn the basics in this lesson.
In this lesson let's learn the basics of Logistic Regression.
They've been around for a long time but now they are all the rage.
Let's find out what a neural network is.
SVMs are one of the most widely used models.
Let's learn what they are in this lesson.
In this lesson let's learn what K-Means Clustering is.
Building a Single Perceptron Model
These next few lesson will be fast.
Let's discuss what's important for you to take away from this section.
The fundamental building block of all neural networks is the perceptron.
In this lesson let's learn what that is and how data flow through it.
Can you build a model with one perceptron?
You can and in this lesson and the next few I'll show you how to do just that.
Let's start walking through the code of neuron.
In this lesson let's continue looking at the code for our perceptron.
This is the last part of our algorithm. Let's take a look at the code.
Neural Networks in Under Ten Minutes
Backpropagation is a core part of building neural networks.
Let's learn what it is in this lesson.
Most of the time one layer isn't enough.
In this lesson let's learn about layers.
What is a batch?
Let's find out in this lesson.
This is a lab lesson.
In this lesson you'll build a simple neural network in Python.
In this lesson let's learn what gradient optimization is.
This is one of the most prominent issues in machine learning models.
Let's find out what overfitting is and how to correct it.
Features are columns or attributes that will make or break our models.
Let's learn about feature engineering in this lesson.
In this lesson you pick the features you believe will result in the best performance for our model.
Let's review the worksheet you completed in the previous lesson on feature selection.