Python is the de facto lingua franca of data science, machine learning, and artificial intelligence. Familiarity with Python is a must for modern data scientists.
Your course is designed to take you from the very foundations to state-of-the-art use of modern Python libraries.
You will learn the fundamentals of the Python programming language, play with Jupyter notebooks, proceed to advanced Python language features, learn about algorithmic complexity and how to address it with parallel and distributed computing, learn to work with data using NumPy, SciPy, Matplotlib, and Pandas, examine state-of-the-art machine learning libraries such as Scikit-Learn and Keras, and complete a realistic, real-life data science lab.
- Dr. Paul Bilokon
- Alexandra Mostovoy
- Ed Silantyev
This training takes place at London’s premier fintech hub:
Level39, One Canada Square
London E14 5AB
The nearest tube station is Canary Wharf (Jubilee line). The nearest DLR station is Canary Wharf DLR.
The delegates’ names will be registered with the ground floor Reception. If you need assistance, please contact us on +44 (0)20 796 57587.
Join our course in person and benefit from live, in-the-room interaction with our Python experts:
Alternatively, join us remotely via a Webinar from anywhere in the world:
WBS Training Ltd (World Business Strategies) organises workshops and conferences for the capital markets and treasury divisions of investment companies worldwide, with all our efforts centered solely on the education of our clients. WBS Training does not operate to present dozens of events every year. Instead we select only the most innovative, pertinent and dynamic subjects, thus bridging the gap between the latest theoretical developments through to proven practical trading floor requirements. Therefore, we aim to ensure that such requirements can be effectively implemented in the real financial world.
The Machine Learning Institute Certificate is a comprehensive six-month part-time course, with weekly live lectures in London or globally online. The MLI is comprised of 2 levels, 6 modules, 24 lecture weeks, lab assignments, a practical final project and a final sit down examination using our global network of examination centres. This course has been designed to empower individuals who work in or are seeking a career in machine learning in finance. Throughout our unique MLI programme, candidates work with hands-on assignments designed to illustrate the algorithms studied and to experience first-hand the practical challenges involved in the design and successful implementation of machine learning models. The MLI is a career-enhancing professional qualification, that can be taken worldwide.
|08:30 – 09:00||Registration and welcome, a tour of Level39|
|09:00 – 10:00||Lecture 1: The fundamentals of the Python programming language and Jupyter notebooks|
|10:00 – 10:30||Tutorial 1|
|10:30 – 11:00||Coffee break|
|11:00 – 12:00||Lecture 2: Advanced Python features; algorithmic complexity; distributed computing; sieve of Eratosthenes; applications to cryptocurrencies and Blockchain|
|12:00 – 12:30||Tutorial 2|
|12:30 – 13:30||Lunch|
|13:30 – 14:30||Lecture 3: Python libraries for working with data: NumPy, SciPy, Matplotlib, and Pandas|
|14:30 – 15:00||Tutorial 3|
|15:00 – 15:30||Coffee break|
|15:30 – 16:30||Lecture 4: Machine Learning with Scikit-Learn; Deep Learning with Keras|
|16:30 – 17:00||Tutorial 4|
|17:00 – 18:00||Lab|
- The fundamentals of the Python programming language and Jupyter notebooks
- Jupyter notebooks
- The Python syntax
- Data types, duck typing
- Data structures: lists, sets, and dictionaries
- Data types
- Advanced Python features; distributed tasks queues with Celery
- List comprehensions
- The Global Interpreter Lock (GIL)
- Multithreading and multiprocessing
- Python libraries for working with data: NumPy, SciPy, Matplotlib, and Pandas
- Multidimensional arrays in NumPy
- Linear algebra and optimisation with SciPy
- Data visualisation in Matplotlib
- Time series data
- Dealing with Pandas DataFrames
- Machine Learning with Scikit-Learn; Deep Learning with Keras, TensorFlow, and Theano
- Overview of machine learning
- Introduction to Scikit-Learn
- Introduction to Keras
What to bring
Although this is not absolutely necessary, you are advised to bring your laptop in order to work on data science exercises during the tutorials. If no laptop is available, we recommend that you work in a pair with a delegate who has access to a laptop. (We encourage collaboration during the tutorials.)
Solutions to problems, including programming assignments, will be provided during the training, so you can follow them. We advice that you have done the following in order to make sure that Jupyter notebook tutorials are properly working on your machine:
Step 1. Install the Anaconda Python distribution (64-bit, the Python 3.7 variant at the time of writing) by Continuum Analytics;
Step 2. Go to Anaconda Navigator (it should be available from the Start menu or its equivalent on your operating system) and make sure that Jupyter notebook is installed. “Launch” it from the Anaconda Navigator. When the browser window opens, try creating a “New” notebook to make sure that Jupyter notebooks work on your machine.
Step 3. This training requires the following packages:
Make sure they are all installed:
- Go to Anaconda Navigator;
- Go to the “Environments” on the left side of the screen;
- Search for the missing package in “Search Packages” among “Not Installed” ones;
- Tick the packages and press “Apply” button for the package to be installed.
Your course is designed to be self-contained. However, if you would like to read up on its contents before, during, or after the course, we recommend the following books:
- David Beazley, Brian K. Jones. Python Cookbook, third edition. O’Reilly, 2013.
- Yves Hilpisch. Python for Finance: Mastering Data-Driven Finance, second edition. O’Reilly, 2018.
- Wes McKinney. Python for Data Analysis, second edition. O’Reilly, 2017.
- Aurelien Geron. Hands-On Machine Learning with Scikit-Learn and TensorFlow. O’Reilly, 2017.
- Francois Chollet. Deep Learning with Python. Manning Publications, 2018.