Intro to Machine Learning
Enquire about our Machine Learning course
CLASSROOM – 4 Evenings @ Monument, London, Mon to Thu, 6:30pm – 8:30pm
£275 + VAT
30th March 2020
REMOTE – 4 Evenings, via Live Video Feed, Mon to Thu, 6:30pm – 8:30pm
£225 + VAT
30th March 2020
By the end of this course:
You will have built several models under the watch of our teachers. You’ll explore decision trees, classification and regression algorithms, as well as k-means clustering.
All of this will be conducted in Python, giving you an introduction to the key libraries pandas and NumPy.
Our Teaching:
We firmly believe in classroom-based study versus online, pre-recorded content, because it ensures your teacher is always on-hand to help you with the challenges you encounter.
You’ll also have ongoing access to the course content to take home and work on at your own pace.
Our Teachers:
Our Machine Learning teachers are Data Scientists.
They come with years of commercial experience.
Meet Priyesh Kapadia
Priyesh is a Step Function Python teacher and works full-time as a Data Scientist at Collinson Group. Priyesh has a Nuclear Engineering Doctorate form Imperial College, where he also worked as a Post-Doc developing Python based modelling tools.
Priyesh was a student on the Step Function Data Science Professional course in the summer of 2017.
Priyesh has taught students up to PhD level and enjoys sharing his knowledge.
Student Review:
“It was pretty awesome. A relaxed, short course that gave a great insight into how machine learning works. It’s a complex subject that can’t be mastered in a week! But to get great insights and experience from within this field was fantastic.”
TOM FLANAGAN – February 2018 Course
“I just wanted to say I really enjoyed the classes… especially the machine learning component which is my real interest! The evening classes really suited my schedule.”
HAMILTON HINDS – April 2018 Python and Machine Learning Course
Introduction to Machine Learning Course Syllabus
Day 1 – Intro to Machine Learning
- A walkthrough of a machine learning model
- Build your own real model that classifies handwritten digits
Day 2 – Supervised Learning Part 1
- Introduction to a Classification problem
- Working with Decision Trees
Day 3 – Supervised Learning Part 2
- Introduction to a regression problem
- Linear regression
Day 4 – Unsupervised Learning
- Introduction to unsupervised learning
- k-means clustering
Bonus Material – Python Analytics Libraries
- pandas
- NumPy
Please note that the details of this Introduction to Python Course syllabus are accurate at the time of printing. There may be some variation in the final syllabus that is taught.
Step Function, August 2019