Authoring Machine Learning Models from Scratch
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MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 611 MB | Duration: 1h 29m
What you'll learn
You'll learn how to author machine learning models in Python without the aide of frameworks or libraries.
You'll learn to code the functions of the most commonly used tools in machine learning.
You'll gain insight into who real-world machine learning models are written.
You will gain a deep appreciation for how the algorithm works
Requirements
You'll need to have a solid background in Python.
You'll need to have a solid background in machine learning.
Description
Welcome to Authoring Machine Learning Models from Scratch
This is your guide to learning the details of machine learning algorithms by implementing them from scratch in Python. You will discover how to load data, evaluate models and implement a suite of top machine learning algorithms using step-by-step tutorials.
Machine learning algorithms do have a lot of math and theory under the covers, but you do not need to know why algorithms work to be able to implement them and apply them to achieve real and valuable results. Most developers that I know (myself included) learn best by implementing. It is our preferred learning style and it is the reason that I created this course.
My name is Mike West and I'm a machine learning engineer in the applied space. I've worked or consulted with over 50 companies and just finished a project with Microsoft. If you're interested in learning what the real-world is really like then you're in good hands.
Who is this course for?
This course is for developers, machine learning engineers and data scientists that want to learn how to get the most out of Keras. You do not need to be a machine learning expert, but it would be helpful if you knew how to navigate a small machine learning problem using SciKit-Learn.
What are you going to Learn?
How to load from CSV files and prepare data for modeling.
How to select algorithm evaluation metrics and resampling techniques for a test harness.
How to develop a baseline expectation of performance for a given problem.
How to implement and apply a suite of linear machine learning algorithms.
How to implement and apply a suite of advanced nonlinear machine learning algorithms.
How to implement and apply ensemble machine learning algorithms to improve performance. From this outcome you will:
Know how top machine learning algorithms work internally.
Know how to better configure machine learning algorithms in order to get the most out of them.
Know the myriad of micro-decisions that a machine learning library has hidden from you in practice.
This course is a hands on-guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to the mechanics of machine learning models in Python. To get the most out of the course, I would recommend working through all the examples in each tutorial. If you watch this course like a movie you'll get little out of it.
In the applied space machine learning is programming and programming is a hands on-sport.
Thank you for your interest in Building Machine Learning Algorithms from Scratch in Python.
Let's get started!
Who this course is for:
If you're a machine learning engineer, this course will be an invaluable guide to understanding real-world machine learning models.
If you're a data scientist or preparing to be one, this course will help you understand the code behind the math.
Screenshots
Kod:
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