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Course Outline
Supervised learning: classification and regression
- Machine Learning in Python: intro to the scikit-learn API
- linear and logistic regression
- support vector machine
- neural networks
- random forest
- Setting up an end-to-end supervised learning pipeline using scikit-learn
- working with data files
- imputation of missing values
- handling categorical variables
- visualizing data
Python frameworks for for AI applications:
- TensorFlow, Theano, Caffe and Keras
- AI at scale with Apache Spark: Mlib
Advanced neural network architectures
- convolutional neural networks for image analysis
- recurrent neural networks for time-structured data
- the long short-term memory cell
Unsupervised learning: clustering, anomaly detection
- implementing principal component analysis with scikit-learn
- implementing autoencoders in Keras
Practical examples of problems that AI can solve (hands-on exercises using Jupyter notebooks), e.g.
- image analysis
- forecasting complex financial series, such as stock prices,
- complex pattern recognition
- natural language processing
- recommender systems
Understand limitations of AI methods: modes of failure, costs and common difficulties
- overfitting
- bias/variance trade-off
- biases in observational data
- neural network poisoning
Applied Project work (optional)
Requirements
There are no specific requirements needed to attend this course.
Testimonials
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
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