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Duration 28 hours
Course Outline
Supervised learning: classification and regression
- Introduction to Machine Learning in Python: the scikit-learn API
- Linear and logistic regression
- Support vector machines
- Neural networks
- Random forests
- Constructing an end-to-end supervised learning pipeline with scikit-learn
- Processing data files
- Imputing missing values
- Managing categorical variables
- Data visualisation
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-structured data
- Long short-term memory (LSTM) cells
Unsupervised learning: clustering and anomaly detection
- Implementing principal component analysis using scikit-learn
- Building autoencoders with Keras
Practical AI problem-solving examples (hands-on exercises via Jupyter notebooks), such as
- Image analysis
- Forecasting complex financial series, including stock prices
- Complex pattern recognition
- Natural language processing
- Recommender systems
Understanding the limitations of AI methods: failure modes, costs, and common challenges
- Overfitting
- The bias-variance trade-off
- Biases in observational data
- Neural network poisoning
Applied project work (optional)
Requirements
No specific prerequisites are required for 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
