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Duration 28 hours
Course Outline
Supervised learning: classification and regression
- Introduction to the scikit-learn API for Machine Learning in Python
- Linear and logistic regression
- Support vector machines
- Neural networks
- Random forest
- Constructing a complete supervised learning workflow with scikit-learn
- Managing data files
- Addressing missing values through imputation
- Processing categorical variables
- Data visualization techniques
Python frameworks for AI applications:
- TensorFlow, Theano, Caffe, and Keras
- Scalable AI using 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
- Performing principal component analysis using scikit-learn
- Building autoencoders with Keras
Real-world problem-solving exercises (hands-on with Jupyter notebooks), including:
- Image analysis
- Forecasting complex financial series, such as 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 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
