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Duration 28 hours
Course Outline
Supervised learning: classification and regression
- Machine Learning in Python: Introduction to the scikit-learn API
- linear and logistic regression
- support vector machines
- neural networks
- random forests
- Establishing an end-to-end supervised learning pipeline using scikit-learn
- working with data files
- imputing missing values
- handling categorical variables
- visualizing data
Python frameworks for AI applications:
- TensorFlow, Theano, Caffe, and Keras
- Scaling AI with Apache Spark: MLlib
Advanced neural network architectures
- convolutional neural networks for image analysis
- recurrent neural networks for time-series data
- long short-term memory (LSTM) cells
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
Understanding the 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
No specific prerequisites are required to attend this course.
Testimonials (2)
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zaklad Uslugowy 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
