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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 machine
- neural networks
- random forest
- Constructing an end-to-end supervised learning pipeline with scikit-learn
- managing data files
- handling missing values through imputation
- processing categorical variables
- data visualization
Python frameworks for AI applications:
- TensorFlow, Theano, Caffe, and Keras
- Scaling AI 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 using scikit-learn
- building autoencoders with 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 prerequisites required to participate in 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
