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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
- Constructing an end-to-end supervised learning pipeline with scikit-learn
- managing data files
- imputing missing values
- processing categorical variables
- data visualization
Python frameworks for AI applications:
- TensorFlow, Theano, Caffe, and Keras
- Scalable AI with Apache Spark: Mlib
Advanced neural network architectures
- convolutional neural networks for image analysis
- recurrent neural networks for sequential data
- long short-term memory (LSTM) cells
Unsupervised learning: clustering and anomaly detection
- performing principal component analysis using scikit-learn
- creating autoencoders in Keras
Practical examples of problems solvable by AI (hands-on exercises using Jupyter notebooks), including:
- image analysis
- predicting complex financial series, such as stock prices
- advanced 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
No specific prerequisites are 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
