An Introduction to Machine Learning for Data Engineers
An Introduction to Machine Learning for Data Engineers, available at $59.99, has an average rating of 4.35, with 46 lectures, 8 quizzes, based on 4016 reviews, and has 11966 subscribers.
You will learn about You'll be familiar with many of the basic algorithms used in machine learning. You'll have solid understanding of how real world models are built using Python. You'll know exactly what machine learning is and what it isn't. You'll be prepared for the machine learning questions on the Google Certified Data Engineering Exam. This course is ideal for individuals who are Data engineering students that need to learn the basics of machine learning for the Google Certified Data Engineering exam. or Anyone interested in learning what machine learning is and why Python is the gold standard for building models. It is particularly useful for Data engineering students that need to learn the basics of machine learning for the Google Certified Data Engineering exam. or Anyone interested in learning what machine learning is and why Python is the gold standard for building models.
Enroll now: An Introduction to Machine Learning for Data Engineers
Summary
Title: An Introduction to Machine Learning for Data Engineers
Price: $59.99
Average Rating: 4.35
Number of Lectures: 46
Number of Quizzes: 8
Number of Published Lectures: 46
Number of Published Quizzes: 8
Number of Curriculum Items: 54
Number of Published Curriculum Objects: 54
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- You'll be familiar with many of the basic algorithms used in machine learning.
- You'll have solid understanding of how real world models are built using Python.
- You'll know exactly what machine learning is and what it isn't.
- You'll be prepared for the machine learning questions on the Google Certified Data Engineering Exam.
Who Should Attend
- Data engineering students that need to learn the basics of machine learning for the Google Certified Data Engineering exam.
- Anyone interested in learning what machine learning is and why Python is the gold standard for building models.
Target Audiences
- Data engineering students that need to learn the basics of machine learning for the Google Certified Data Engineering exam.
- Anyone interested in learning what machine learning is and why Python is the gold standard for building models.
THE REVIEWS ARE IN:
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
Awesome! — Satendra
Great learning experience!! — Lakshminarayana
Wonderful learning…— Rajesh
Excellent — Dipthi
Clear and to the point. Fit’s a lot of knowledge into short, easy to understand concepts/thoughts/scenarios. — Sam
Course was fantastic. — Narsh
Great overview of ML — Eli
Very helpful for beginners, All concept explained well. Overall insightful training session. Thank you! –Vikas
Very good training. Concepts were well explained. — Jose
I like the real world touch given to course material . This is extremely important. — Soham
Learned some new terms and stuffs in Machine Learning. Ideal for learners who needs to get some overview of ML. — Akilan
This session is very good and giving more knowledge about machine learning— Neethu
Got to know many things on machine learning with data as a beginner. Thanks Mike. –Velumani
Really well explained and very informative. — Vinoth
COURSE INTRODUCTION:
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 questionsfor the Google Certified Data Engineering exam.
This is NOTa 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 algorithmsused. We will also cover the nomenclaturespecific 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.
Course Curriculum
Chapter 1: An Introduction
Lecture 1: Introduction
Lecture 2: Section Contents
Lecture 3: Is this Course for You?
Lecture 4: Machine Learning Defined
Lecture 5: Machine Learning Types
Lecture 6: The Modeling Process
Lecture 7: Terminology
Lecture 8: Summary
Chapter 2: Model Building in Python
Lecture 1: Why Applied Machine Learning is Mostly Python
Lecture 2: Creating Datalab Notebooks on Google's Cloud Platform
Lecture 3: Cloud Datalab Notebook Navigation
Lecture 4: Lab: Creating Our Datalab Virtual Machine
Lecture 5: Summary
Chapter 3: Data Wrangling
Lecture 1: Data Massaging Introduction
Lecture 2: Lesson Speed Warning
Lecture 3: Using Pandas to Massage Data – Data Structures
Lecture 4: Using Pandas to Massage Data – Data Frame
Lecture 5: Lab: Working with Dataframes
Lecture 6: Summary
Chapter 4: Machine Learning algorithms
Lecture 1: Linear Regression
Lecture 2: Naive Bayes
Lecture 3: Decision Trees
Lecture 4: Logistic Regression
Lecture 5: Neural Network
Lecture 6: Support Vector Machines
Lecture 7: K-Means Clustering
Lecture 8: Summary
Chapter 5: Building a Single Perceptron Model
Lecture 1: Section Approach
Lecture 2: The Perceptron
Lecture 3: Model Building with 1 Perceptron
Lecture 4: The Perceptron Code
Lecture 5: Linear Function Code
Lecture 6: The Entire Perceptron Model
Lecture 7: Summary
Chapter 6: Neural Networks in Under Ten Minutes
Lecture 1: Backpropagation
Lecture 2: Layers
Lecture 3: Batching
Lecture 4: Lab: A Simple Neural Network in TensorFlow
Lecture 5: Summary
Chapter 7: Testing
Lecture 1: Gradient Descent
Lecture 2: Overfitting and How to Correct it
Lecture 3: Feature Engineering
Lecture 4: Lab: Pick the Features that Matter
Lecture 5: Feature Engineering Lab Review
Lecture 6: Summary
Lecture 7: Bonus Lecture: Free Machine Learning Content
Instructors
-
Mike West
Creator of LogikBot
Rating Distribution
- 1 stars: 33 votes
- 2 stars: 86 votes
- 3 stars: 650 votes
- 4 stars: 1654 votes
- 5 stars: 1593 votes
Frequently Asked Questions
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