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Course Outline
Supervised learning: classification and regression
- Introduction to Machine Learning in Python via 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
- processing data files
- imputation of missing values
- managing 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 time-structured data
- long short-term memory (LSTM) cells
Unsupervised learning: clustering and anomaly detection
- implementing principal component analysis with scikit-learn
- building autoencoders in Keras
Practical AI problem-solving examples (hands-on exercises using Jupyter notebooks), such as
- image analysis
- forecasting complex financial series, such as stock prices
- complex pattern recognition
- natural language processing
- recommender systems
Understanding AI limitations: failure modes, costs, and common difficulties
- overfitting
- bias/variance trade-off
- biases in observational data
- neural network poisoning
Applied Project work (optional)
Requirements
No prior 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
