AI: Basic Practical Machine Learning.

The course aims to give a comprehensive understanding of and skills in machine learning, data handling and statistical analysis. After completing the programme, students shall have a basic understanding of machine learning, including its different types such as supervised and unsupervised learning, and its applications in different industries. The course gives knowledge of: - what machine learning is, its different types (supervised, unsupervised, reinforcement learning), and understanding its role and applications in different industries - handling, "cleaning" and preparing data for analysis. This includes knowledge of using tools such as Python and its libraries such as Pandas to process and visualise data - basic statistical methods and how they are applied in data analysis. This covers knowledge of relationships, trends and patterns in data. - building, training and evaluating machine learning models. This includes linear and logistic regression, K-nearest neighbors, decision trees and ensemble methods such as Random Forest. - neural networks and deep learning, including a basic understanding of convolutional neural networks (CNN) and recurrent neural networks (RNN). The course gives skills in: - Using programming languages such as Python, particularly for data analysis and machine learning applications. This includes using libraries such as Pandas, NumPy and Scikit-Learn. - Preparing, "cleaning" and handling large amounts of data. Skill in carrying out exploratory data analysis (EDA) to understand and draw conclusions from data. - Choosing, training and fine-tuning different types of machine learning models, from simple linear regressions to more complex classification and clustering models. - Using deep learning frameworks such as TensorFlow or Keras, particularly for building simple CNN and RNN models. The course gives the competence to: - Identify problems that can be solved with machine learning and apply suitable machine learning techniques to find solutions. - Critically analyse results and carry out data-driven decision-making, which is important for drawing conclusions and making recommendations based on data. - Understand the ethical considerations in machine learning, such as bias, data privacy and the potential impact of AI on society. - Effectively communicate technical results and insights to both technical and non-technical stakeholders.