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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.
28 Hours
Testimonials (2)
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
The trainer was a professional in the subject field and related theory with application excellently
