Merge pull request #233 from coding-for-reproducible-research/intro-to-machine-learning

[Ready for review] Intro to machine learning

Author: simonkirbysimonkirby

.gitignore

@@ -1,2 +1,3 @@
 .DS_Store
-.ipynb_checkpoints/
+.ipynb_checkpoints/
+individual_modules/introduction_to_machine_learning/datasets

_static/intro_to_ml_slides/intro_to_ml_part_1.pdf

@@ -23,6 +23,7 @@ parts:
                 sections:
                   - file: programme_information/intro_to_python
                   - file: programme_information/python_for_data_analysis
+                  - file: programme_information/introduction_to_machine_learning
               - file: programme_information/R
                 sections:
                   - file: programme_information/intro_to_r
@@ -139,6 +140,13 @@ parts:
                   - file: individual_modules/python_for_data_analysis/Python_ScikitLearn
                   - file: individual_modules/python_for_data_analysis/data-analysis-plotly
                   - file: individual_modules/python_for_data_analysis/data-analysis-task
+              - file: individual_modules/section_landing_pages/introduction_to_machine_learning
+                sections:
+                  - file: individual_modules/introduction_to_machine_learning/1_linear_regression
+                  - file: individual_modules/introduction_to_machine_learning/2_model_selection
+                  - file: individual_modules/introduction_to_machine_learning/3_pipeline_task
+                  - file: individual_modules/introduction_to_machine_learning/4_unsupervised_learning
+                  
           - file: course_homepages/R
             sections:
               - file: individual_modules/section_landing_pages/introduction_to_r

_static/intro_to_ml_slides/intro_to_ml_part_2.pdf

@@ -43,7 +43,13 @@
     "\n",
     "[Clickable Link to Self Study Notes](../individual_modules/section_landing_pages/python_for_data_analysis.md)\n",
     "\n",
-    "The course offers a comprehensive foundation in Python programming, covering essential topics such as data types, control structures, and functions, alongside advanced data manipulation using Pandas and NumPy for handling large datasets efficiently. Students will learn to create both static and interactive visualizations with Matplotlib and Plotly, apply machine learning techniques using Scikit-Learn for predictive modeling, and implement testing frameworks and virtual environments to manage dependencies effectively. Additionally, the course emphasizes reproducibility and validation of research by teaching methods to ensure that analyses can be replicated and validated by others.\n"
+    "The course offers a comprehensive foundation in Python programming, covering essential topics such as data types, control structures, and functions, alongside advanced data manipulation using Pandas and NumPy for handling large datasets efficiently. Students will learn to create both static and interactive visualizations with Matplotlib and Plotly, apply machine learning techniques using Scikit-Learn for predictive modeling, and implement testing frameworks and virtual environments to manage dependencies effectively. Additionally, the course emphasizes reproducibility and validation of research by teaching methods to ensure that analyses can be replicated and validated by others.\n",
+    "\n",
+    "## Introduction to Machine Learning Self Study Notes\n",
+    "\n",
+    "[Clickable Link to Self Study Notes](../individual_modules/section_landing_pages/introduction_to_machine_learning.md)\n",
+    "\n",
+    "This course is designed to introduce core machine learning concepts. Students will explore the machine learning landscape, and the main stages of the machine learning pipeline. They will learn about model selection, error, evaluation and validation, and put all of these into practice using Python. `Scikit-learn` will be introduced, and used to pre-process data, train models, and build pipelines.\n"
    ]
   }
  ],