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Course Outline
Supervised Learning: Classification and Regression
- Bias-variance trade-off
- Logistic regression as a classification tool
- Evaluating classifier performance
- Support vector machines
- Neural networks
- Random forests
Unsupervised Learning: Clustering and Anomaly Detection
- Principal Component Analysis
- Autoencoders
Advanced Neural Network Architectures
- Convolutional neural networks for image analysis
- Recurrent neural networks for time-series data
- The long short-term memory (LSTM) cell
Practical Examples of AI Solutions
- Image analysis
- Forecasting complex financial series, such as stock prices
- Complex pattern recognition
- Natural language processing
- Recommender systems
Software Platforms for AI Applications
- TensorFlow, Theano, Caffe, and Keras
- Scalable AI with Apache Spark: Mlib
Understanding AI Limitations: Modes of Failure, Costs, and Common Challenges
- Overfitting
- Biases in observational data
- Missing data
- Neural network poisoning
Requirements
No prior specific requirements are necessary to attend this course.
28 Hours
Testimonials (2)
The adaptation of exos to our context and the consideration of our request
Amel Guetat - EURO-INFORMATION DEVELOPPEMENTS
Course - Fraud Detection with Python and TensorFlow
Machine Translated
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
