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Duration 28 hours
Course Outline
Supervised Learning: Classification and Regression
- Introduction to Machine Learning in Python: exploring 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
- Processing data files
- Filling in missing values (imputation)
- Managing categorical variables
- Data visualization techniques
Python Frameworks for AI Applications:
- TensorFlow, Theano, Caffe, and Keras
- Scaling AI with Apache Spark: MLlib
Advanced Neural Network Architectures
- Convolutional Neural Networks (CNNs) for image analysis
- Recurrent Neural Networks (RNNs) for time-structured data
- The Long Short-Term Memory (LSTM) cell
Unsupervised Learning: Clustering and Anomaly Detection
- Applying Principal Component Analysis (PCA) with scikit-learn
- Building autoencoders in Keras
Practical AI Applications (hands-on exercises using Jupyter notebooks), including
- Image analysis
- Forecasting complex financial series, such as stock prices,
- Advanced pattern recognition
- Natural Language Processing (NLP)
- Recommender systems
Evaluating AI Method Limitations: Failure Modes, Costs, and Common Challenges
- Overfitting
- Bias-variance trade-off
- Biases present in observational data
- Neural network poisoning
Applied Project Work (Optional)
Requirements
This course has no specific prerequisite requirements.
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
