Get rid of some unnecessary subdirectories.

README.md

 # Bachelor of Applied Data Science: Data Challenges Unit Resources
 
+## Issues
+
+* Pandas-DataFrames/DosesWrangling.ipynb broken - seem to have the wrong data file?
+
 ## By Topic
 
 * [Project-Tools](Project-Tools)
@@ -12,15 +16,10 @@
 * [Visualisation](Visualisation)
 * [Machine-Learning](Machine-Learning)
     * [Unsupervised-Methods](Machine-Learning/Unsupervised-Methods)
-        * [Principal-Component-Analysis](Machine-Learning/Unsupervised-Methods/Principal-Component-Analysis)
-        * [K-Means-Clustering](Machine-Learning/Unsupervised-Methods/K-Means-Clustering)
     * [Supervised-Methods](Machine-Learning/Supervised-Methods)
         * [Regression](Machine-Learning/Supervised-Methods/Regression)
         * [Decision-Trees](Machine-Learning/Supervised-Methods/Decision-Trees)
-        * [Support-Vector-Machines](Machine-Learning/Supervised-Methods/Support-Vector-Machines)
-        * [K-Nearest-Neighbours](Machine-Learning/Supervised-Methods/K-Nearest-Neighbours)
     * [Ensemble-Methods](Machine-Learning/Ensemble-Methods)
-        * [Boosting](Machine-Learning/Ensemble-Methods/Boosting)
 * [Topic-Hints](Topic-Hints)
     * [Optimization](Topic-Hints/Optimization)
     * [Brain-Scans](Topic-Hints/Brain-Scans)
@@ -42,7 +41,7 @@
 * Week 8 [Merging and Joining Data](Pandas-DataFrames/Merging/MergeJoinIntro.ipynb)
 * Week 9 [Intro to Linear Regression](Machine-Learning/Supervised-Methods/Regression/03-IntroLinearRegression.ipynb)
 * Week 10 [Intro to Linear Regression (Dupe?)](Machine-Learning/Supervised-Methods/Regression/03-IntroLinearRegression.ipynb)
-* Week 11 [K Nearest Neighbours (kNN) Classification](Machine-Learning/Supervised-Methods/K-Nearest-Neighbours/12-kNN.ipynb)
+* Week 11 [K Nearest Neighbours (kNN) Classification](Machine-Learning/Supervised-Methods/K-Nearest-Neighbours.ipynb)
 
 ### ADS1002
 
@@ -50,13 +49,13 @@
 
 * Week 2 [Regression Workflow](Machine-Learning/Supervised-Methods/Regression/01-ML_Workflow_Diabetes.ipynb)
 * Week 3 [Logistic Regression](Machine-Learning/Supervised-Methods/Regression/02-LogisticRegression_Iris.ipynb)
-* Week 4 [Support Vector Machines (SVM)](Machine-Learning/Supervised-Methods/Support-Vector-Machines/03-SVM.ipynb)
+* Week 4 [Support Vector Machines (SVM)](Machine-Learning/Supervised-Methods/Support-Vector-Machines.ipynb)
 * Week 5 [Decision Trees](Machine-Learning/Supervised-Methods/Decision-Trees/04-DecisionTrees.ipynb)
 * Week 6 [Random Forests](Machine-Learning/Supervised-Methods/Decision-Trees/05-RandomForest.ipynb)
-* Week 7 [Principal Component Analysis (PCA)](Machine-Learning/Unsupervised-Methods/Principal-Component-Analysis/08-PCA.ipynb)
-* Week 8 [K-Means Clustering](Machine-Learning/Unsupervised-Methods/K-Means-Clustering/KMeans.ipynb)
+* Week 7 [Principal Component Analysis (PCA)](Machine-Learning/Unsupervised-Methods/Principal-Component-Analysis.ipynb)
+* Week 8 [K-Means Clustering](Machine-Learning/Unsupervised-Methods/K-Means-Clustering.ipynb)
 * Week 9 [Git](Project-Tools/Git/GIT-CLI-CORE.pdf)
 * Week 10 [Advanced Plotting](Visualisation/11-AdvancedPlotting.ipynb)
-* Week 11 [Boosting](Machine-Learning/Ensemble-Methods/Boosting/Boosting.ipynb)
+* Week 11 [Boosting](Machine-Learning/Ensemble-Methods/Boosting.ipynb)
 
 ### ADS2002