Python & Machine Learning

Course Overview

This course is designed to provide a robust foundation in Python programming alongside a deep dive into machine learning applications.

Beginning with an introduction to basic Python syntax, data input/output, functions, and classes, the course quickly advances to explore essential libraries for data science including Numpy, Keras, PyTorch, Pandas, and TensorFlow. Participants will also navigate through various platforms and IDEs, learning to write and host Python code, manage version control with Git, and understand the advantages of different development environments.

The programme introduces Jupyter Notebooks as a powerful tool for writing Python code in segments, encouraging collaboration and inline data analysis. Further modules focus on data visualisation with Plotly and Matplotlib, exploratory data analysis (EDA) techniques, data cleaning, and effectively framing data to answer specific research questions. Lastly, the course covers testing and troubleshooting in Python, including common pitfalls, test-driven development, pair programming, and automated testing procedures.

This course is ideal for individuals looking to leverage Python for machine learning projects, offering practical skills and insights to apply Python’s vast ecosystem in solving complex data science challenges.

Learning Objectives

• Gain a foundational understanding of Python programming, including basic syntax, functions, classes, and data input/output operations, along with access to key resources for further learning.

   

• Develop proficiency in using major Python libraries essential for data science and machine learning, such as Numpy for linear algebra, Keras and PyTorch for neural networks, Pandas for data manipulation, and TensorFlow for implementing machine learning models.

   

• Acquire the skills to effectively use platforms and Integrated Development Environments (IDEs) like PyCharm and text editors, manage projects with Git and version control, and understand the protocols for publishing and hosting Python applications.

   

• Learn to utilise Jupyter Notebooks for modular coding, perform inline data analysis, and foster collaboration in machine learning projects, comparing its benefits with platforms like Databricks.

   

• Master data visualisation techniques using Plotly and Matplotlib to create histograms, scatterplots, and incorporate key reference points in graphs to elucidate data insights visually.

   

• Understand the principles of Exploratory Data Analysis (EDA), including data cleaning, formulating pertinent questions, reframing data to derive answers, and adopting best practices for testing, troubleshooting, and maintaining Python code in machine learning applications.