Course overview
Prerequisites
- Basic knowledge of Python coding is a pre-requisite
- Bring your own device
Who Should Attend?
- This course is intended for programmers who need to code machine learning algorithms in Python
- This course is also suitable for programmers who may have knowledge of general Python coding
Campus locations
Study method
- In-class
- Blended
- Online
Locations
- England
- London
Duration study load
- 2 Days
Course features
- Python Machine Learning certificate on completion (assessment based)
- Python Machine Learning notes
- Practical Python Machine Learning exercises, Python Machine Learning homework/python Machine Learning revision work
- To assist after the course, 1 free session for questions online Python Machine Learning via Skype or Teamviewer
- Max group size on this Python Machine Learning is 4
2019 Dates
Start Date: 23rd January 2019
End Date: 24th January 2019
Length: 2 Days
Start Date: 4th February 2019
End Date: 5th February 2019
Length: 2 Days
Start Date: 13th February 2019
End Date: 14th February 2019
Length: 2 Days
Start Date: 27th February 2019
End Date: 28th February 2019
Length: 2 Days
Start Date: 6th March 2019
End Date: 7th March 2019
Length: 2 Days
Start Date: 20th March 2019
End Date: 21st March 2019
Length: 2 Days
Start Date: 30th January 2019
End Date: 31st January 2019
Length: 2 Days
Start Date: 6th February 2019
End Date: 7th February 2019
Length: 2 Days
Start Date: 20th February 2019
End Date: 21st February 2019
Length: 2 Days
Start Date: 4th March 2019
End Date: 5th March 2019
Length: 2 Days
Start Date: 13th March 2019
End Date: 14th March 2019
Length: 2 Days
Start Date: 27th March 2019
End Date: 28th March 2019
Length: 2 Days
Subjects
Learn how to implement Python functions for machine learning and code and implement algorithms to predict future data.
Machine Learning gives computers the ability to learn without being explicitly programmed.
Machine Learning algorithms can learn from data and make predictions on data by extrapolating on existing trends.
Companies can take advantage of a wealth of available data and of Machine Learning techniques to gain actionable insights and ultimately improve their business.
Using scikit-learn, the core Machine Learning library for Python, attendees will learn how to implement Machine Learning systems to perform predictions on their data.
The first part of a Machine Learning project understands the data and the problem at hand.
Data cleaning, data transformation and data pre-processing are the steps to perform to get the data sets in the right shape, to enable Machine Learning algorithms to record trends and predict future data.
Python functions are pre-programmed algorithms that help programmers and makes data exploration and preprocessing relatively easy.
By injecting domain knowledge in the process, attributes are extracted from the data and how to encode and engineer them into features that make Machine Learning algorithms work.
In supervised learning, the “training data” consist of a set of “training” samples of data that is associated with the desired output label.
Supervised learning algorithms learn a desired output from the training data and predict new, unseen data.
Supervised learning has two different directions: classification (the task of predicting a category) and regression (the task of predicting a quantity).
Examples of applications include price prediction, spam detection and sentiment analysis.
About PCWorkshops
PCWorkshops mission is to be an integral partner to companies who up-skill and cross-skill their software and data staff.
We are based in London, Milton Keynes, Southend-On-Sea, Birmingham, Portsmouth and Manchester.
Our clients are in a variety of industries, e.g. e-commerce companies,
finance companies, insurance companies, software and technology companies and more.
