"I learnt so much and the course has definitely made me consider studying computer science and coding in the future."
Aaniya, Teen student
Machine Learning For Artificial Intelligence – In Person
Machine Learning and Data for Teens
NEW! From decision trees to neural networks, this course will equip you with the tools to create and manipulate Machine Learning models using Python and Google Colab.
For online courses: these require you to have a capable computer to work on and a broadband internet connection.
For in person courses: you will need a laptop with the same specification, please see below for more information. If this is a problem or you are unsure about anything, please contact us via info@fire-tech.com
Most PC/Mac computers from the last 5 years will be fine but you can view our recommended detailed system requirements here
For in person courses: you will need a laptop with the same specification, please see below for more information. If this is a problem or you are unsure about anything, please contact us via info@fire-tech.com
Most PC/Mac computers from the last 5 years will be fine but you can view our recommended detailed system requirements here
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Machine Learning For Artificial Intelligence - In Person
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Course highlights
MACHINE LEARNING TYPES
Learn all about the different types of Machine Learning models and how they they each work. We'll look at Decision Trees, K-nearest Neighbour and Naive-Bayes
PYTHON & MACHINE LEARNING
You'll learn how to use Python in Google Collab to integrate data using different models as well as importing different modules into Python
MACHINE LEARNING & DATA
Using external and internal data sources we'll show you how to make predictions using machine learning. This is one of the most powerful things you can do with Machine Learning
Course Overview
Join this course and learn all about Machine Learning and the amazing things it can do.
We’ll show you how to use Google Colab to make Machine Learning projects. You will also be shown TensorFlow, SciKit Learn, importing Python modules and tKinter to create graphical projects.
Topics covered will include:
- Computer Vision
- Text Recognition
- Classification
- Decision Trees
- K-nearest Neighbour
- Naive-Bayes
- Intro to Deep Learning
- Neural Networks
This course will address principles of Artificial Intelligence but won’t go into detail of how to implement it into projects. If you want to learn about A.I please look at our Senior Adventures in A.I course here.
What your child will learn
- The fundamentals of Machine Learning and what computer confidence means
- What Machine Learning can and cannot do well
- The difference between Artificial Intelligence, Machine Learning and Deep Learning. You'll also look at the benefits and drawbacks of each
- The three main types of Machine Learning Decision Trees, K-nearest Neighbour and Naive-Bayes
- Using data form external sources as well as how to link to your own data sets
- Learn about Python specific code to utilise new modules for use in their models
- Learn the basics of TensorFlow, SciKit Learn, pandas and tKinter
Typical daily schedule
In Person
1.5 hours
Kick off
20 minutes
Break
1.5 hours
Lesson time
45 minutes
Lunch
2 hours
Lesson time
20 minutes
Break
1 hour
Lesson time
15 minutes
Plenaries and finish
