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part 1 | Lecture | [Materials](notebooks/week5_lecture_2.ipynb) | MO\n5 | Introduction to Machine Learning - part 1 | Recap | [Solutions](notebooks/week5_solutions_2.ipynb) | MO\n6 | Introduction to Machine Learning - part 2 | Lecture | Materials | MO\n6 | Introduction to Machine Learning - part 2 | Recap | Solutions | MO\n7 | Networks and Knowledge Graphs | Lecture | [Materials](notebooks/week7_lecture.ipynb) | KN\n\n\n\n### References\n\n- Data Science in Python by Data Science Academy at AstraZeneca:\n  - [June-July 2020 course](https://github.com/semacu/data-science-python)\n  - [January-February 2021 course](https://github.com/semacu/202101-data-science-python)\n  - [May-June 2021 course](https://github.com/semacu/202105-data-science-python)\n  - [October-November 2021 course](https://github.com/semacu/202110-data-science-python)\n- The University of Cambridge [Introduction to Python course](https://github.com/pycam/python-basic)\n- The University of Cambridge [Data Science in Python course](https://github.com/pycam/python-data-science)\n- Data Carpentry [Python lessons](https://datacarpentry.org)\n- The CRUK-CI [Introduction to R during COVID-19 course](https://bioinformatics-core-shared-training.github.io/r-intro/)\n- Python pandas [documentation](https://pandas.pydata.org/docs/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fastrazeneca%2Fdata-science-python-course","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fastrazeneca%2Fdata-science-python-course","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fastrazeneca%2Fdata-science-python-course/lists"}