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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 forest algorithms
- Establishing an end-to-end supervised learning pipeline with scikit-learn
- managing data files
- handling missing value imputation
- processing categorical variables
- data visualization techniques
Python frameworks for AI applications:
- TensorFlow, Theano, Caffe, and Keras
- Scalable AI development 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
- building autoencoders in Keras
Practical examples of solvable AI problems (hands-on exercises using Jupyter notebooks), such as:
- image analysis
- forecasting complex financial time 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
- bias-variance trade-off
- biases present 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
