Update README.md

Module 4 - Machine Learning/01. Data Preparation and Modelling with sklearn/README.md

@@ -33,38 +33,42 @@
 	</ol>
 </details>
 
-## Steps for a Machine Learning Project
+## Model Building Pipeline (Step by Step Procedure)
 1. Identify input(X) and output(y) features.
-
-2. Identify the Data Preprocessing Techniques.
-	- Identify the data preprocessing techniques by analysing the input variables (i.e. X).
- 	- For Numerical Data
-  		- Data Cleaning Steps - Outliers and Missing Values
-		- Data Transformation Techniques - Standardization and Normalization
-	- For Categorical Data
-  		- Data Cleaning Steps - Outliers and Missing Values
-   		- Data Transformation Techniques - One Hot Encoding / Dummy Encoding and Label Encoding
-	- For Text Data
-   		- Data Cleaning Steps -  
-   			a. Removing Special Characters, Punctuations, etc..  
-    			b. Converting to lower cases  
-    			c. Removing Stop Words  
-      			d. Lemmatization / Stemming
-		- Data Transformation Techniques(Feature Extraction or Vectorization Techniques) - Bag of Words, Term Frequency Inverse Document Frequency (TF IDF), Word2Vec, GloVe, FastText, RNN, LSTMs, GRUs, Embeddings from Language Models (ELMo), Bidirectional Encoder Representation from Transformers (BERT)
-	- For Image Data
-  		- Data Cleaning Steps - 
- 		- Data Transformation Techniques(Feature Extraction or Vectorization Techniques) - Flattening, Convolutional Neural Network + Flattening (architechtures like: VGGNet, AlexNet, Inception Module (GoogleNet), ResNet, MobileNet, EfficientNet, etc...), VisionTransformers
-	- Audio Data
-  		- Data Cleaning Steps - 
- 		- Data Transformation Techniques(Feature Extraction or Vectorization Techniques) - Mel Scaled Filter Bank, Mel Frequency Cepstral Coefficients (MFCC)
-3. Identify the supervised ML Task.
-	- Identify the task by analysing the target variable (i.e. y).
-	- For Classification:
-		- Algorithm - Logistic Regression, SVC, KNeighborsClassifier, DecisionTreeClassifier, RandomForestClassifier, GBDTClassifier, etc
-		- Evaluation Metric - Accuracy, Confusion Metric, Precision, Recall, ROC AUC, Log Loss, etc
-	- For Regression:
-		- Algorithm - Linear Regression, SVC, KNeighborsRegressor, DecisionTreeRegressor, RandomForestRegressor, GBDTRegressor, etc
-		- Evaluation Metric - Mean Square Error, Root Mean Square Error, Mean Absolute Error, R Square, Adjusted R Square, etc
+<ol>
+	<li><details>
+	<summary>Identifying the Data Preprocessing Techniques</summary>
+		- Identify the data preprocessing techniques by analysing the input variables (i.e. X).
+		- For Numerical Data
+			- Data Cleaning Steps - Outliers and Missing Values
+			- Data Transformation Techniques - Standardization and Normalization
+		- For Categorical Data
+			- Data Cleaning Steps - Outliers and Missing Values
+			- Data Transformation Techniques - One Hot Encoding / Dummy Encoding and Label Encoding
+		- For Text Data
+			- Data Cleaning Steps -  
+				a. Removing Special Characters, Punctuations, etc..  
+					b. Converting to lower cases  
+					c. Removing Stop Words  
+					d. Lemmatization / Stemming
+			- Data Transformation Techniques(Feature Extraction or Vectorization Techniques) - Bag of Words, Term Frequency Inverse Document Frequency (TF IDF), Word2Vec, GloVe, FastText, RNN, LSTMs, GRUs, Embeddings from Language Models (ELMo), Bidirectional Encoder Representation from Transformers (BERT)
+		- For Image Data
+			- Data Cleaning Steps - 
+			- Data Transformation Techniques(Feature Extraction or Vectorization Techniques) - Flattening, Convolutional Neural Network + Flattening (architechtures like: VGGNet, AlexNet, Inception Module (GoogleNet), ResNet, MobileNet, EfficientNet, etc...), VisionTransformers
+		- Audio Data
+			- Data Cleaning Steps - 
+			- Data Transformation Techniques(Feature Extraction or Vectorization Techniques) - Mel Scaled Filter Bank, Mel Frequency Cepstral Coefficients (MFCC)
+	</details></li>
+	<li><details>
+	<summary>Identifying the supervised ML Task and Evaluation Metric</summary>
+		- Identify the task by analysing the target variable (i.e. y).
+		- For Classification:
+			- Algorithm - Logistic Regression, SVC, KNeighborsClassifier, DecisionTreeClassifier, RandomForestClassifier, GBDTClassifier, etc
+			- Evaluation Metric - Accuracy, Confusion Metric, Precision, Recall, ROC AUC, Log Loss, etc
+		- For Regression:
+			- Algorithm - Linear Regression, SVC, KNeighborsRegressor, DecisionTreeRegressor, RandomForestRegressor, GBDTRegressor, etc
+			- Evaluation Metric - Mean Square Error, Root Mean Square Error, Mean Absolute Error, R Square, Adjusted R Square, etc
+	</details></li>
 4. Split the data(X, y) into training(X_train, y_train) and testing data(X_test, y_test).
 5. Apply Data Preprocessing on X_train (which was identified in step-2). Get X_train_transformed.
 6. Choose an appropriate ML Algorithm (which was identified in step-3). Train a machine learning model using training data (X_train_transformed, y_train).