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Course Outline
Supervised learning: classification and regression
- Introduction to the scikit-learn API for Machine Learning in Python
- linear and logistic regression
- support vector machine
- neural networks
- random forest
- Constructing a complete supervised learning pipeline with scikit-learn
- processing data files
- filling in missing values
- managing 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 and anomaly detection
- principal component analysis using scikit-learn
- building autoencoders in Keras
Practical AI application examples (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: failure modes, costs, and common challenges
- 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.
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 - 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
