Learning Path Overview
QA’s Practical Machine Learning learning path is an intense deep dive into the world of machine learning. In it you'll learn how to implement different machine learning models, validate their quality, and how to implement them practically. This course is being iterated, and we are looking to flesh out the scope and learning activities within it. As such, if you have any feedback, please don’t hesitate to get in touch and let us know what you think we could do to improve this course.
This learning path is aimed at fledging data scientists and analysts who wish to gain more in-depth knowledge of Machine Learning.
- GCSE Mathematics or above.
- Must be comfortable with analytical and mathematical thinking.
- Familiar with basic python programming: variables, control flow, scope, data structures and functions. Must be comfortable with algorithmic thinking.
- Familiar with basics of data analysis including databases, descriptive statistics, and typical business use cases.
After completing Practical Machine Learning, you will know how to:
- Explore and prepare data
- Develop ML models
- Pick ML algorithms for a given task
- Understand techniques and metrics used to determine the quality of ML models
This learning path contains videos, quizzes and other resources for five modules, together with the associated course Introduction. It also incorporates quizzes for you to test your knowledge as you work through the Learning Path.
We welcome all feedback and suggestions - please contact us at firstname.lastname@example.org to let us know what you think.
Learning Path Steps
This course is the first in a two-part series covering the fundamentals of machine learning.
This course is part two of the module on machine learning and covers unsupervised learning, the theoretical basis for machine learning, model and linear regression, the semantic gap, and how we approximate the truth.
Knowledge Check: Practical Machine Learning - Module 0
This course covers the basics of python in machine learning, how to use loops, regressions, and classification, and how to set up machine learning in python.
Knowledge Check: Practical Machine Learning - Module 1
This course is the first part of the two-part series on the mathematics of machine learning.
This course is the second part of the two-part series on the mathematics of machine learning.
Knowledge Check: Practical Machine Learning - Module 2
The course introduces you to supervised learning and the nearest neighbors algorithm.
This course explores hyperparameters, distance functions, similarity measures, logistic regression, the method and workflow of machine learning and evaluation, and the train-test split.
Knowledge Check: Practical Machine Learning - Module 3
Using SageMaker Notebooks to Train and Deploy Machine Learning Models
In this lab, you'll use a SageMaker notebook to learn how to write Python code to prepare data, train and deploy models, and use them for real-time inference.
Selecting the right machine learning model will help you find success in your projects. In this module, we’ll discuss how to do so, as well the difference between explanatory and associative approaches, before we end on how to use out-sample performance.
Knowledge Check: Practical Machine Learning - Module 4
Predict income levels using census data and compare the performance of two trained models in this Azure Machine Learning Studio Lab.
Regression is a widely used machine learning and statistical tool and it’s important you know how to use it. In this module, we’ll discuss interpreting modes, as well as how to interpret linear classification models.
Take control of a p2.xlarge instance equipped with an NVIDIA Tesla K80 GPU to perform CPU vs GPU performance analysis for AWS Machine Learning in this Lab.
Knowledge Check: Practical Machine Learning - Module 5
This course covers the concept of unsupervised learning within the context of machine learning and how unsupervised learning differs from supervised learning.
This course explores the topic of probability and statistics, including various mathematical approaches and some different interpretations of probability.
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