This is a practical introduction to Machine Learning using Python programming language. Machine Learning allows you to create systems and models that understand large amounts of data. These models support our decision making in a range of fields, including market prediction, within scientific research and statistical analysis. This course will teach you how to use statistical techniques and machine learning algorithms that enable a computer system to learn from different types of data.
This is a ten week introductory course in Machine Learning using Python, which is a widely used programming language in the field of Machine Learning. Python is especially effective due to its readability, versatility, and its integration with several packages specifically designed for Machine Learning. The course is both theoretical and practical, and we will ensure you understand the theory behind the algorithm, before this is tested on real world data examples. We will guide you through areas such as analysing large amounts of data and classifying this into appropriate categories, and how to recognise recurring features and identify correlations, so that you can develop a complex system which has the ability to make accurate predictions.
We will begin the course by exploring the main tools provided in Python for data visualisation and data analysis. You will learn how to process the data and create graphs that can reveal specific insights of the database. From data analysis the course will then move on to explore the theory and practical application of the main machine learning algorithm in Scikit-learn for classification and regression models. After this we will take the first steps in exploring Keras, a more sophisticated machine learning library that includes and expands on our work with Scikit-learn. Towards the end of the course we will briefly discuss Neural Network and Convolutional Neural Network to give you a brief introduction to the concept of Deep Learning.
We will cover the most popular Python Data Science Libraries, such as:
- Jupyter Notebook
Please note that in order to participate in this course a basic level of computer literacy is required. Prior knowledge of data analysis and machine learning is not needed to take part. The course is both suitable for students with technical expertise, such as experienced programmers, as well as those taking their first steps in machine learning.
Why Study this Course?
- Data Scientist is one of the most trending jobs in many economic sectors, and machine learning applied to data science is increasingly adopted due to its ability to solve problems relating to the increased availability of large dataset.
- We will teach you how to ensure that there are no erroneous representations and rules of operation that lead to errors or misjudgements in your machine learning models.
- You will understand the theory behind the algorithm and you will be able to test them on real world data examples.
- The course is a great opportunity to learn new skills that can be used to further your career.
- The course is open to anyone interested in Machine Learning, students that are interested in data science, and to anyone with little experience at coding and that wants to understand the potentiality of machine learning applied to their datasets.
We are committed to providing reasonable teaching adjustments for students with disabilities that may impact on their learning experience. If you require adjustments, please complete the relevant section on the booking form and also contact us at firstname.lastname@example.org so we can respond to your requests as soon as possible.
Please note our short courses sell-out quickly, so early booking is advisable.
Starting date, Wednesday 15 Jan 2020
6.30-8.30pm | 10 weeks
Starting date, Wednesday 29 Apr 2020
6.30-8.30pm | 10 weeks
If you have any questions about this course please contact shortcourses (@gold.ac.uk) .
For information on our upcoming short courses please sign up to our mailing list.
Richard Hoggart Building