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What we will cover in the course
A week-by-week overview of the DataCampp course 'Machine Learning for Image Data'
The main topics we will cover during this five-week course are as follows:
Week 1 – introduction to machine learning:
- Introduction to machine learning for image data
- Software tools
- Key machine learning concepts – supervised versus unsupervised learning
- Common machine learning tasks – e.g. classification and regression
Week 2 – data and features:
- Types of data and features
- Extracting features
- Labelling image data
- Pre-processing data
Week 3 – common techniques:
- Clustering – K-means
- Classification – Decision trees, Naive Bayes
- Regression – Linear regression
- Model evaluation, visualisation and selection
Week 4 – tips and tricks:
- Good training practice – splitting datasets and cross-validation
- Data augmentation
- Common challenges – overfitting, dimensionality reduction
Week 5 – Deep learning:
- What is deep learning?
- Introduction to neural networks and convolutional neural networks
- Advantages and disadvantages
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