Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 28 hours
Course Outline
Supervised learning: classification and regression
- Introduction to Machine Learning in Python: navigating 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
- Managing data files
- Handling missing values through imputation
- Processing categorical variables
- Data visualization techniques
Key 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) using scikit-learn
- Building autoencoders in Keras
Practical applications of AI (hands-on exercises via Jupyter notebooks)
- Image analysis
- Forecasting complex financial series, such as stock prices
- Advanced pattern recognition
- Natural language processing
- Developing recommender systems
Understanding AI limitations: failure modes, costs, and common challenges
- Overfitting
- The bias-variance trade-off
- Biases in observational data
- Neural network poisoning
Applied project work (optional)
Requirements
No prior specific requirements or prerequisites are necessary to join 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
