Mastering Machine Learning with Python

Participants embark on their machine learning journey with a comprehensive introduction to the essential concepts and tools. They explore and learn to apply key concepts such as linear regression, feature engineering and dimensionality reduction, setting a solid foundation in machine learning.

Topics covered include:

• Data preprocessing for machine learning models
• Linear Regression
• Gradient descent
• Feature Engineering
• Dimensionality reduction

Number of workshops: 4

Number of assignments: 2

Participants advance their machine learning skills by delving into one of the major types of machine learning used in industry today: supervised learning. In this module, your team will explore an array of supervised learning models that they can use, learn how to evaluate their models, and then refine them to boost performance.

Topics covered include:

• Various machine learning algorithms including: Logistic regression, K-Nearest Neighbours, Naive Bayes and Decision trees
• Binary and Multiclass classification
• Model evaluation for classification and regression algorithms

Number of workshops: 4

Number of assignments: 5

Propel your team's machine learning expertise to new heights with an exploration of neural networks and ensemble techniques. This module is crafted to give participants an introduction to these two major advanced concepts in machine learning, preparing them for the challenges of implementing more complex models in future.

Topics covered include:

• Bias and variance
• Ensemble methods: Bagging, Boosting, Stacking
• Fundamentals of Neural Networks
• Training Neural Networks, including concepts such as: Loss functions, Gradient Descent, and Hyper-Parameter Optimisation
• Machine learning algorithms include: Random Forests, AdaBoost, Gradient Boosting

Number of workshops: 2

Number of assignments: 2