Training Dates: April 20, 27; May 4, 11, 18 and June 8, 15, 22
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Objective: Learn and improve your Python programming skills with real practical Machine Learning/Artificial Intelligence projects. The following Machine Learning project workflow will be covered: project documentation, team meeting review (Data Engineers, Data Scientists, Subject Matter Experts, Managers, IT Developers and Systems Administrators, etc.), data load, data profiling (data statistical analysis and data visualization), data preprocessing, data profiling, team meeting review, labels and features selection, features engineering (a data science art!), select and apply classic and modern algorithms, select best features and best algorithms, team meeting review, model deployment and unit test, model updates methodology and unit test, final project documentation, final team meeting review, etc. A very well-organized and real production Python code will be provided as well.
Prerequisites: Previous Python programming experiences, Probability and Statistics (undergraduate level).
Requirements: A laptop with the latest Python Anaconda distribution package and any popular Python IDE programs (PyCharm, Spyder, Visual Studio Code, Jupyter Notebook, etc.) installed. A Python virtual environment will setup properly with the latest Python Data Ecosystem libraries.
Materials: All course materials will be provided for the Instructor, including text PDF eBook, slides, samples code, weekly assigns, certification document, etc.
Duration: One night a week, 6:00 pm – 9:00 pm, 8 classes (two months).
Location: To be determined.
- Setup a Python virtual environment with the latest Python Data Ecosystem libraries. Advanced Python project setup and programming for Machine Learning/Artificial Intelligence projects.
- Python Object-Oriented and Multithreading/Asynchronous Programming
- Data Load, Exploration and Pre-processing
- Database Access/Manipulation with Extract-Transform-Load (ETL) System
- Ensemble Machine Learning Algorithms (Bagging, Boosting and Stacking)
- Deep Learning with Scikit-Learn and Keras/TensorFlow Frameworks
- Image Processing and Classification using OpenCV/ Keras/TensorFlow and Boosting algorithms
- How to speed-up Python Machine Learning Programs