Overview:
In this Course, you will be working on real-world datasets, applying a variety of machine learning techniques, using a functional programming approach. In the process, you will gradually learn simple but fundamental and widely applicable machine learning ideas, and develop a sense for what it means to “do machine learning”, and how it is similar and different from writing “normal” code. Rather than taking an abstract mathematical view of machine learning or jump straight into frameworks, we will implement together a selection of classic prediction algorithms, across datasets illustrating different practical problems you will encounter. By coding things from the ground up, you will develop an intuition for how things actually work inside the black box, what types of techniques are available and the problems they are suitable for, and some of the potential issues you need to watch out for
Objective:
Upon completion of this course, students should be able to:
• This course is aimed towards developers, in which we will delve into the mathematics behind the code as well as developing real life algorithms in Python.
• One-to-one help will be provided for developers new to Python and all algorithms, frameworks and libraries used will be demonstrated by the instructor.
Pre-Requisite:
This is a full beginner to advanced level course, which is suitable for most users with some development experience.
Some experience of Python is helpful, but not necessary. No data science experience is expected.
Course Curriculum
| Day 1 | |||
| How data science fits within a business context Details | 00:00:00 | ||
| Data science processes and language Details | 00:00:00 | ||
| Information and uncertainty Details | 00:00:00 | ||
| Types of learning Details | 00:00:00 | ||
| Segmentation Details | 00:00:00 | ||
| Modelling Details | 00:00:00 | ||
| Overfitting and generalisation Details | 00:00:00 | ||
| Holdout and validation techniques Details | 00:00:00 | ||
| Optimisation and simple data processing Details | 00:00:00 | ||
| Linear regression Details | 00:00:00 | ||
| Classification and clustering Details | 00:00:00 | ||
| Feature engineering Details | 00:00:00 | ||
| An in-depth practical example demonstrating the day’s concepts Details | 00:00:00 | ||
| Day 2 | |||
| Numerical and visual model evaluation Details | 00:00:00 | ||
| Introduction and application of statistics in data science Details | 00:00:00 | ||
| Understand the practical steps to design and deploy models Details | 00:00:00 | ||
| Further experience with real-life messy data Details | 00:00:00 | ||
| Unsupervised Machine Learning Details | 00:00:00 | ||
| A range of Machine Learning models: e.g. Logistic regression, linear and nonlinear SVMs, decision trees, etc Details | 00:00:00 | ||
| Introduction to tooling, testing and deployment Details | 00:00:00 | ||
| An in-depth practical example demonstrating the day’s concepts Details | 00:00:00 | ||
| Day 3 | |||
| Text feature engineering Details | 00:00:00 | ||
| Text mining, representation and learning Details | 00:00:00 | ||
| Neural networks Details | 00:00:00 | ||
| Deep belief networks Details | 00:00:00 | ||
| Stacked denoising autoencoders Details | 00:00:00 | ||
| Convolutional neural networks Details | 00:00:00 | ||
| Semi-supervised machine learning Details | 00:00:00 | ||
| Ensemble methods Details | 00:00:00 | ||
| An in-depth practical example demonstrating the day’s concepts Details | 00:00:00 | ||
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