Update README.md

Author: alo7lika

README.md

@@ -21,6 +21,7 @@ Repeated issue creation for more scores will be considered has flag.
 If later found out the points will be deducted. you cant be earning more than 60 points from this repo. any techincal feature addition is excluded
 
 ## Table of Contents
+- [📘 Theory of Machine Learning Workflow](#theory-of-machine-learning-workflow)
 - [Roadmap](roadmaps)
   - [Machine Learning Roadmap](#machine-learning)
   - [Roadmap.sh](#roadmapsh)
@@ -50,6 +51,39 @@ If later found out the points will be deducted. you cant be earning more than 60
 - [Interview](#interview)
 - [Others](#others)
 - [Conclusion](#conclusion)
+- 
+## 📘 Theory of Machine Learning Workflow
+
+Machine learning (ML) is a subset of artificial intelligence that focuses on building systems capable of learning from data, identifying patterns, and making decisions with minimal human intervention. The machine learning workflow is a structured approach that guides practitioners through the stages of developing effective models.
+
+### 1. Data Collection
+The first step involves gathering relevant data from various sources, such as databases, APIs, or web scraping. Quality data is crucial, as it directly impacts the performance of the machine learning model.
+
+### 2. Data Preprocessing
+Data preprocessing is essential for cleaning the data and preparing it for analysis. This step involves handling missing values, removing duplicates, and normalizing or standardizing features to ensure consistent scales across the dataset.
+
+### 3. Exploratory Data Analysis (EDA)
+EDA involves analyzing data distributions and relationships through visualization techniques, such as histograms, scatter plots, and box plots. This step helps identify patterns, trends, and anomalies within the data.
+
+### 4. Feature Engineering
+Feature engineering is the process of creating new features or transforming existing ones to improve the model's performance. This may involve techniques such as one-hot encoding for categorical variables, polynomial feature expansion, or domain-specific transformations.
+
+### 5. Model Selection
+Choosing the right algorithm is critical to the success of the machine learning project. This step involves selecting algorithms based on the problem type (e.g., classification, regression) and the nature of the data.
+
+### 6. Model Training
+In this stage, the selected model is trained using a portion of the dataset (training data). The model learns patterns and relationships in the data through various optimization techniques.
+
+### 7. Model Evaluation
+Once the model is trained, it is evaluated using a separate portion of the dataset (validation/test data). Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC, which help assess the model's performance.
+
+### 8. Deployment
+After validation, the model can be deployed into production, making it accessible for real-world applications. This step includes integrating the model into existing systems and ensuring it can handle live data.
+
+### 9. Monitoring & Maintenance
+Post-deployment, continuous monitoring of the model's performance is necessary to ensure its effectiveness. This involves tracking model accuracy, updating it with new data, and retraining when necessary to adapt to changing conditions.
+
+Through these stages, the machine learning workflow provides a systematic approach to building, validating, and deploying models that can yield valuable insights and drive decision-making across various domains.
 
 
 ### Roadmap