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Duration 28 hours
Course Outline
Supervised Learning: Classification and Regression
- Machine Learning in Python: Introduction to the scikit-learn API
- Linear and logistic regression models
- Support vector machines
- Neural network fundamentals
- Random forest algorithms
- Constructing end-to-end supervised learning pipelines using scikit-learn
- Processing structured data files
- Imputing missing values
- Managing categorical variables
- Data visualization techniques
Python frameworks for AI applications:
- TensorFlow, Theano, Caffe, and Keras
- Scalable AI processing 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
- Developing 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
Understand limitations of AI methods: modes of failure, costs, and common difficulties
- Model overfitting
- Bias-variance trade-off
- Biases in observational data
- Neural network poisoning
Applied Project work (optional)
Requirements
There are no specific requirements needed to attend 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
