Practical Machine Learning

Developed with QA
OverviewStepsAuthor
QA
This content is developed in partnership with QA
DifficultyIntermediate
AVG Duration17h
Students856
Ratings
4.1/5
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Content
1163

Description

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.  

Intended Audience
This learning path is aimed at fledging data scientists and analysts who wish to gain more in-depth knowledge of Machine Learning.


Prerequisites

  • 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. 


Learning Objectives

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

Agenda

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.


Feedback

We welcome all feedback and suggestions - please contact us at qa.elearningadmin@qa.com to let us know what you think.  

Certificate

Your certificate for this learning path
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Training Content

1
Course - Beginner - 1h 23m
Module 0 - What is Machine Learning? - Part One
This course is the first in a two-part series covering the fundamentals of machine learning.
2
Course - Beginner - 1h 30m
Module 0 - What is Machine Learning? - Part Two
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.
3
Exam - 35m
Knowledge Check: Practical Machine Learning - Module 0
Knowledge Check: Practical Machine Learning - Module 0
4
Course - Beginner - 52m
Module 1 – Python for Machine Learning
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.
5
Exam - 45m
Knowledge Check: Practical Machine Learning - Module 1
Knowledge Check: Practical Machine Learning - Module 1
6
Course - Beginner - 1h 45m
Module 2 - Maths for Machine Learning - Part One
This course is the first part of the two-part series on the mathematics of machine learning.
7
Course - Beginner - 1h 32m
Module 2 - Maths for Machine Learning - Part Two
This course is the second part of the two-part series on the mathematics of machine learning.
8
Exam - 55m
Knowledge Check: Practical Machine Learning - Module 2
Knowledge Check: Practical Machine Learning - Module 2
9
Course - Beginner - 1h
Module 3 - Supervised Learning - Part One
The course introduces you to supervised learning and the nearest neighbors algorithm.
10
Course - Beginner - 1h 52m
Module 3 - Supervised Learning - Part Two
This course explores hyperparameters, distance functions, similarity measures, logistic regression, the method and workflow of machine learning and evaluation, and the train-test split.
11
Exam - 45m
Knowledge Check: Practical Machine Learning - Module 3
Knowledge Check: Practical Machine Learning - Module 3
12
Hands-on Lab - Intermediate - 1h
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.
13
Course - Beginner - 48m
Module 4 - Model Selection
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.
14
Exam - 45m
Knowledge Check: Practical Machine Learning - Module 4
Knowledge Check: Practical Machine Learning - Module 4
15
Hands-on Lab - Beginner - 50m
Predict Income Levels Using Azure Machine Learning Studio
Predict income levels using census data and compare the performance of two trained models in this Azure Machine Learning Studio Lab.
16
Course - Beginner - 36m
Module 5 - Regression
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.
17
Hands-on Lab - Beginner - 45m
Analyzing CPU vs GPU Performance for AWS Machine Learning
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.
18
Exam - 30m
Knowledge Check: Practical Machine Learning - Module 5
Knowledge Check: Practical Machine Learning - Module 5
19
Course - Beginner - 55m
Module 6 - Unsupervised learning
This course covers the concept of unsupervised learning within the context of machine learning and how unsupervised learning differs from supervised learning.
20
Course - Beginner - 1h 3m
Module 7 - Probability and statistics
This course explores the topic of probability and statistics, including various mathematical approaches and some different interpretations of probability.
About the Author
Students10385
Labs31
Courses155
Learning paths33

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