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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
- Handling data files
- Imputing missing values
- Processing categorical variables
- Data visualization
Python Frameworks for AI Applications
- TensorFlow, Theano, Caffe, and Keras
- Scaling AI with 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
- Implementing Principal Component Analysis (PCA) using scikit-learn
- Building autoencoders with 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
- Recommender systems
Understanding the Limitations of AI Methods: Failure Modes, Costs, and Common Challenges
- Overfitting
- The bias-variance trade-off
- Biases in observational data
- Neural network poisoning
Applied Project Work (Optional)
Requirements
This course does not require any specific prior prerequisites.
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
