- This repositroy contains the codebase for the Cambrdige Spark SL7 AI Apprenticheship
- The notebooks contained within are a combination of teaching material and practicals
- Please fork this Repository if you want to safely run code within this project
├── 01_Data_Science_Toolbox/ # Covers some of the most widely used, industry-standard tools for analysis of data in Python
│ └── 01_Programming_with_Python/
│ └── 02_Working_with_Git
│ └── 03_Introduction_to_Numpy
│ └── 04_Introduction_to_Pandas
├── 02_Introduction_to_Machine_Learning/ # Introduction to Machine Learning fundamentals
│ └── 01_Introduction/
│ └── 02_Introduction_to_ML/
│ └── 03_Data_PreProcessing_and_Supervised_Learning/
│ └── 04_Assumptions_of_Linear_Regression/
│ └── 05_Feature_Engineering/
│ └── 06_Supporting_Maths/
├── 03_Product_Management_for_AI/ # Understanding how AI tools and products best interact with customers and end users
├── 04_Supervised_Classification/ # Intoduction to an array of discriminative and generative supervised learning models, and techniques to evaluate/improve model suitability & performance
│ └── 01_Intoducing_Classification/
│ └── 02_Deterministic_Classifiers/
│ └── 03_Logisitc_Regression/
│ └── 04_Model_Evaluation_and_Probabilistic_Approaches/
├── 05_Ensemble_Methods/ # Introudction to Ensembles, and the intuition behind them, as well as covering key concepts (e.g. bagging, random forsets, types of boosting, and stacking)
│ └── 01_Introduction_to_Ensembles_and_Bagging/
│ └── 02_Introduction_to_Boosting/
│ └── 03_Boosting/
│ └── 04_XGBoost/
│ └── 05_Stacking/
├── 06_Pragmatic_Model_Evaluation/ # Covers advances sklearn tools, and how to evaluate techniques to tackle different types of data science problems
│ └── 01_Enterprise_Architecture/
│ └── 02_Advanced_Model_Evaluation/
│ └── 03_Advanced_Scikit_Learn/
│ └── 04_Model_Selection/
│ └── 05_Model_Optimisation/
├── 07_Unsupervised_Learning/ # Covers a wide range of unsupervised learning models and techniques used to reveal latent structures within data
│ └── 01_Intoduction_to_Unsupervised_Learning/
│ └── 02_KMeans_Clustering/
│ └── 03_Hierarchical_Clustering/
│ └── 04_DBScan/
│ └── 05_TSNE/
├── 08_The_AI_Landscape/ # Understanding key topics impacting Data Science and AI today
│ └── 01_Introduction/
│ └── 02_Workshop_Preparation/
│ └── 03_Live_Workshop/
│ └── 04_Personal_Data_Protection/
│ └── 05_AI_and_ML_Ethics/
│ └── 06_Appendix/
├── 09_Time_Series_Analysis/ # How to handle time objects in python, and understand the tools and techniques for building time-series models
│ └── 01_Time_Objects_in_Python/
│ └── 02_Introduction_to_Time_Series/
│ └── 03_Classical_Time_Series_Models/
│ └── 04_Time_Series_In_Practice/
├── 10_Neural_Networks_and_Deep_Learning/ # Covers the theoretical foundations of neural networks, and how to apply them to real problems
│ └── 01_Introduction_to_Calculus/
│ └── 02_Introduction_to_Neural_Networks/
│ └── 03_Introduction_to_Optimisation/
│ └── 04_Training_a_Neural_Network/
│ └── 05_Convolutional_Neural_Networks/
│ └── 06_Training_RNNs/
│ └── 07_Graph_Neural_Networks/
├── 11_Model_Explainability_and_Interpretability/ # Understanding different approaches & techniques for interpeting and explaining a range of ML Models and neural networks
│ └── 01_Introduction_to_Explainability_and_Interpretability/
│ └── 02_Global_Explainations/
If you would like Lecture Material for any of the topics mentioned above, please contact Nathan Pinnock at nathan.pinnock@hotmail.com