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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
- Support vector machines
- Neural networks
- Random forests
- Constructing end-to-end supervised learning pipelines with scikit-learn
- Managing 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: Mlib
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 (PCA) using scikit-learn
- Building autoencoders with Keras
Practical examples of problems solvable by AI (hands-on exercises using Jupyter notebooks), including:
- Image analysis
- Forecasting 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
- The bias-variance trade-off
- Biases inherent in observational data
- Neural network poisoning
Applied project work (optional)
Requirements
No specific prerequisites are required to enroll in 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
