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Course Outline
Supervised Learning: Classification and Regression
- Introduction to the scikit-learn API in Python
- Linear and logistic regression techniques
- Support vector machines (SVM)
- Neural network implementations
- Random forest algorithms
- Establishing an end-to-end supervised learning pipeline using scikit-learn
- Managing data file inputs
- Imputation of missing values
- Processing of categorical variables
- Data visualization methods
Python Frameworks for AI Applications
- Overview of TensorFlow, Theano, Caffe, and Keras
- Scalable AI implementation with Apache Spark MLlib for government data systems
Advanced Neural Network Architectures
- Convolutional neural networks (CNNs) for image analysis
- Recurrent neural networks (RNNs) for time-series data
- Long short-term memory (LSTM) cells
Unsupervised Learning: Clustering and Anomaly Detection
- Implementation of principal component analysis (PCA) with scikit-learn
- Development of autoencoders using Keras
Practical Applications of AI Solutions (Hands-on Exercises via Jupyter Notebooks)
- Image analysis workflows
- Forecasting complex financial metrics, including stock price trends
- Complex pattern recognition
- Natural language processing (NLP)
- Development of recommender systems for government services
Limitations of AI Methods: Failure Modes, Costs, and Common Challenges
- Overfitting risks
- Bias-variance trade-offs
- Bias within observational datasets
- Security vulnerabilities such as neural network poisoning
Applied Project Work (Optional)
Requirements
Eligibility for this training program is open to all personnel, with no prerequisites established for government participation.
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
