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Duration 28 hours
Course Outline
Supervised Learning: Classification and Regression
- Introduction to Machine Learning in Python via the scikit-learn API
- Linear and logistic regression
- Support Vector Machines
- Neural networks
- Random forests
- Constructing an end-to-end supervised learning pipeline with scikit-learn
- Data file management
- Imputation of missing values
- Processing categorical variables
- Data visualization
Python Frameworks for AI Applications:
- TensorFlow, Theano, Caffe, and Keras
- Scalable AI with Apache Spark: Mlib
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
- Building autoencoders with Keras
Practical AI Use Cases (Hands-on exercises via Jupyter notebooks), including:
- Image analysis
- Forecasting complex financial time series, such as stock prices
- Advanced pattern recognition
- Natural Language Processing
- Recommender systems
Understanding the Limitations of AI: Failure Modes, Costs, and 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
