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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.
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
