Merge pull request #117 from data-derp/fix/42-formatting

42 | ks | formating lists

Author: tklae

versioned_docs/version-2.0/data-science-and-interpretability/overview.mdx

@@ -66,7 +66,7 @@ Feeling Lost? [Machine Learning Estimators Map](https://scikit-learn.org/stable/
 * Dimensionality Reduction
 
 ### Optimization & Reinforcement Learning
-* Convex Optimization, Genetic Algorithms 
+* Convex Optimization, Genetic Algorithms
 * Deep Reinforcement Learning
 
 ## Intuition
@@ -79,13 +79,14 @@ Feeling Lost? [Machine Learning Estimators Map](https://scikit-learn.org/stable/
 
 </div>
 What are some limitations of linear models (y = mx + b)? or even multiple linear regression (y = m1x1 + m2x2 + … + b)?
-* Assumes **monotonic gradient (slope)** between the target variable (y) and any feature (e.g. x1)
-* Assumes **constant slope steepness** between the target variable (y) and any feature (e.g. x1)
+
+- Assumes **monotonic gradient (slope)** between the target variable (y) and any feature (e.g. x1)
+- Assumes **constant slope steepness** between the target variable (y) and any feature (e.g. x1)
   for the entire domain of that feature
 
 Machine Learning models can often provide the **flexibility** to overcome this. Think of it as **automatic curve fitting** and **if-else**!
-  * You don’t need to hard-code the predicates
-  * The algorithm will determine and optimize your if-conditions
+- You don’t need to hard-code the predicates
+- The algorithm will determine and optimize your if-conditions
 
 However, note the issue happening with the green line in the picture 😵
 
@@ -101,7 +102,7 @@ However, note the issue happening with the green line in the picture 😵
 </div>
 
 * Flexibility vs Generalization
-* Overfitting vs Underfitting 
+* Overfitting vs Underfitting
   * How can we avoid overfitting in particular?
 * Complexity vs Interpretability
   * Is that dichotomy still strictly true today?
@@ -164,16 +165,16 @@ However, note the issue happening with the green line in the picture 😵
 * Deep Learning
   * PyTorch
     * Super flexible, create any model architecture
-  * TensorFlow 
-    * the Keras API is super easy to use 
+  * TensorFlow
+    * the Keras API is super easy to use
     * if not using Keras, probably better to go with PyTorch
-* MLOps, CD4ML 
+* MLOps, CD4ML
   * [MLflow](https://mlflow.org/) for experiment tracking, logging, model versioning
   * [DVC](https://dvc.org/) for data versioning
-  * [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning/) and/or [AWS SageMaker](https://aws.amazon.com/sagemaker/) 
+  * [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning/) and/or [AWS SageMaker](https://aws.amazon.com/sagemaker/)
     * covers full lifecycle management
 
-  
+
 ## Interpretability
 <div style={{textAlign: 'center'}}>