Practical Machine Learning

Developed with QA

Overview Steps Author

Start Learning Path

This content is developed in partnership with QA

DifficultyIntermediate

AVG Duration16h

Students1277

Ratings

4.1/5

Content

1162

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

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.

Go to hands-on lab

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

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.

16

Hands-on Lab - Beginner - 45m

UPDATED

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.

Go to hands-on lab

17

Exam - 30m

Knowledge Check: Practical Machine Learning - Module 5

Knowledge Check: Practical Machine Learning - Module 5

18

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.

19

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

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QA

Training Provider

Students27382

Labs122

Courses1329

Learning paths45

A world-leading tech and digital skills organization, we help many of the world’s leading companies to build their tech and digital capabilities via our range of world-class training courses, reskilling bootcamps, work-based learning programs, and apprenticeships. We also create bespoke solutions, blending elements to meet specific client needs.

Covered Topics

Amazon Web Services Microsoft Azure Artificial Intelligence for AWS Artificial Intelligence for Azure Azure Machine Learning Studio Amazon Machine Learning Machine Learning Development Artificial Intelligence Python