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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
- Building an end-to-end supervised learning pipeline with scikit-learn
- manipulating 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: 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 and anomaly detection
- implementing principal component analysis using 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: failure modes, costs, and common challenges
- overfitting
- bias/variance trade-off
- biases within observational data
- neural network poisoning
Applied Project work (optional)
Requirements
No specific prerequisites are required 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 - 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
