Applied AI from Scratch in Python Training Course
The training program delivers essential skills for developers and data analysts seeking to engineer machine learning systems in Python from the ground level. It addresses fundamental concepts within supervised learning, including classification and regression, alongside unsupervised techniques such as clustering and anomaly detection, while also exploring complex neural network structures. The curriculum reviews established methodologies for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical development workflows. This resource supports government professionals in deploying functional machine learning models, assessing algorithmic constraints, and executing applied projects tailored for public sector challenges.
This course is available as onsite live training in US Government or online live training.Course Outline
Supervised learning: classification and regression
- Machine Learning in Python: introduction to the scikit-learn API
- linear and logistic regression
- support vector machines
- neural networks
- random forest algorithms
- Establishing an end-to-end supervised learning pipeline using scikit-learn for government applications
- managing data files
- imputing missing values
- processing categorical variables
- data visualization techniques
Python frameworks for AI applications:
- TensorFlow, Theano, Caffe, and Keras
- Scalable AI implementation 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 with scikit-learn
- implementing autoencoders in Keras
Practical examples of problems that AI can solve (hands-on exercises using Jupyter notebooks), e.g.
- image analysis
- forecasting complex financial series, such as stock prices,
- complex pattern recognition
- natural language processing
- recommender systems
Understanding limitations of AI methods: modes of failure, costs, and common difficulties
- overfitting
- bias/variance trade-off
- biases in observational data
- neural network poisoning
Applied Project work (optional)
Requirements
This course has no prerequisites for government participation.
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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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
Fahad Malalla - Tatweer Petroleum
Course - Applied AI from Scratch in Python
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