This learning plan is designed specifically for preparing for the DP-100 certification exam, which focuses on Designing and Implementing a Data Science Solution on Azure. The plan uses Pomodoro Technique for focused study sessions and integrates the Forgetting Curve theory for optimal retention.

Study Overview

Goal: To successfully prepare and pass the DP-100 certification exam.

Duration: 8 weeks (with flexibility to extend or shorten based on individual progress)

Week 1: Data Exploration and Experimentation (EDA)

Day 1: Introduction to Exploratory Data Analysis (EDA)

• Task 1: Study the concept of Exploratory Data Analysis (EDA).

  • Goal: Understand the importance of EDA in analyzing datasets and identifying patterns.
  • Action: Watch video tutorials on EDA basics (30 minutes).
  • Pomodoro: Break down the concepts in 3 cycles of 25 minutes each.
  • Outcome: Write a brief summary (10 minutes) on how EDA helps in understanding a dataset.

• Task 2: Introduction to basic data visualization techniques.

  • Learn about different types of visualizations: histograms, scatter plots, box plots.
  • Goal: Understand the role of visualizations in uncovering insights from data.
  • Pomodoro: Watch tutorials (20 minutes) and practice creating visualizations with a sample dataset.

Day 2: Hands-On with Basic Visualizations

• Task 1: Work with Matplotlib and Seaborn libraries for basic visualizations.

  • Goal: Be able to create histograms, scatter plots, and pair plots for any dataset.
  • Pomodoro: Create visualizations for the Iris dataset.
  • Outcome: Produce a set of visualizations that describe the dataset's characteristics (e.g., distribution of flower species, correlations).

• Task 2: Hands-on practice with Seaborn's pairplot and boxplot.

  • Goal: Learn how to use pairplots for understanding feature relationships.
  • Pomodoro: Focus on working with the Iris dataset (45 minutes).

Day 3: Handling Missing Data

• Task 1: Study different techniques to handle missing data:

  • Imputation, deletion, forward-fill.
  • Goal: Understand when to use each method depending on the context of the dataset.
  • Pomodoro: Spend 2 Pomodoros (50 minutes total) reviewing the different methods and apply them to a sample dataset.

• Task 2: Implement data imputation techniques using Pandas.

  • Goal: Master the art of filling missing values using mean, median, or mode.
  • Pomodoro: Apply techniques to Titanic dataset and record your observations (45 minutes).

Day 4: Handling Outliers

• Task 1: Learn to identify and handle outliers using Z-scores and IQR (Interquartile Range).

  • Goal: Be able to detect outliers using basic statistical methods.
  • Pomodoro: Watch tutorial videos on outliers (25 minutes) and implement the techniques on a dataset.

• Task 2: Hands-on with outlier detection and treatment.

  • Apply Z-score and IQR methods to the Titanic dataset.
  • Pomodoro: Execute the task in 3 cycles, analyzing the distribution of features and detecting outliers (75 minutes total).

Day 5: Advanced Data Exploration

• Task 1: Study correlation matrices and how to interpret them.

  • Goal: Learn how to analyze relationships between features using heatmaps.
  • Pomodoro: Watch tutorial videos on heatmaps (25 minutes) and explore the Titanic dataset.

• Task 2: Practice Pairwise correlations and Heatmap visualization with Seaborn.

  • Goal: Use Seaborn to create heatmaps that display correlations between features.
  • Pomodoro: Spend 1 Pomodoro (25 minutes) analyzing Titanic dataset correlation and visualize it.

Day 6: Transforming Data

• Task 1: Study data transformation techniques:

  • Learn about transformations like logarithmic and Box-Cox.
  • Goal: Learn how to apply transformations to make data more normally distributed.
  • Pomodoro: Watch tutorials on data transformations (25 minutes) and try them out on a sample dataset.

• Task 2: Practice log transformations and Box-Cox transformations on the Titanic dataset.

  • Goal: Apply transformations and examine how the feature distributions change.
  • Pomodoro: Apply and visualize the effects of transformations (50 minutes).

Day 7: Review and Consolidation

• Task 1: Review everything learned during the week.

  • Goal: Reinforce your understanding by revisiting key concepts.
  • Pomodoro: Spend 1 Pomodoro (25 minutes) reviewing EDA, visualization, and transformations.

• Task 2: Consolidate your knowledge by applying EDA and transformations to a new dataset.

  • Goal: Practice hands-on techniques and ensure you can perform EDA independently.
  • Pomodoro: Complete the entire EDA process with a new dataset (75 minutes total).

Week 2: Data Preprocessing and Feature Engineering

Day 8: Introduction to Data Preprocessing

• Task 1: Study data preprocessing concepts such as scaling and normalization.

  • Goal: Understand why scaling is important, especially for algorithms like SVM and KNN.
  • Pomodoro: Watch a tutorial on data scaling and normalization (25 minutes).

• Task 2: Implement Min-Max Scaling and Z-score normalization using Scikit-learn.

  • Goal: Be able to preprocess datasets by scaling numeric features.
  • Pomodoro: Apply both techniques to the Iris dataset (45 minutes).

Day 9: Encoding Categorical Data

• Task 1: Study the importance of encoding categorical data for machine learning models.

  • Goal: Understand the difference between Label Encoding and One-Hot Encoding.
  • Pomodoro: Watch videos explaining different encoding techniques (30 minutes).

• Task 2: Implement One-Hot Encoding and Label Encoding on a sample dataset.

  • Goal: Practice encoding techniques and understand their effects.
  • Pomodoro: Apply One-Hot Encoding and Label Encoding to the Titanic dataset (45 minutes).

Day 10: Advanced Feature Engineering

• Task 1: Study Feature Engineering and how to create new features.

  • Goal: Learn how to create new features from existing ones through operations like polynomial features.
  • Pomodoro: Watch tutorials on feature engineering (30 minutes).

• Task 2: Implement feature creation methods like Polynomial Features and Interaction Features.

  • Goal: Practice creating meaningful features to improve model accuracy.
  • Pomodoro: Create polynomial and interaction features in a sample dataset (45 minutes).

Day 11: Principal Component Analysis (PCA)

• Task 1: Learn the concept of PCA (Principal Component Analysis) for dimensionality reduction.

  • Goal: Understand how PCA helps to reduce the number of features while retaining most of the data's information.
  • Pomodoro: Watch a tutorial on PCA theory and its practical implementation (30 minutes).

• Task 2: Apply PCA to a dataset to reduce the feature space.

  • Goal: Be able to reduce dimensions of a dataset using PCA and visualize the reduced space.
  • Pomodoro: Implement PCA on the Iris dataset and visualize the reduced dimensions (45 minutes).

Day 12: Data Preprocessing for Advanced Models

• Task 1: Study advanced feature selection techniques, including Recursive Feature Elimination (RFE).

  • Goal: Learn methods to select the most important features for machine learning models.
  • Pomodoro: Watch tutorials on RFE and its applications (30 minutes).

• Task 2: Implement RFE on the Iris dataset.

  • Goal: Understand and practice feature selection to improve model performance.
  • Pomodoro: Apply RFE in Scikit-learn (45 minutes).

Day 13: Review Preprocessing Techniques

• Task 1: Review all data preprocessing techniques learned during the week.

  • Goal: Reinforce your understanding and improve your ability to preprocess data for machine learning models.
  • Pomodoro: Spend 1 Pomodoro reviewing all techniques (25 minutes).

• Task 2: Apply preprocessing techniques to a new dataset.

  • Goal: Gain confidence in applying preprocessing independently.
  • Pomodoro: Preprocess a new dataset (75 minutes).

Day 14: Consolidation and Practical Application

• Task 1: Consolidate your knowledge by applying everything learned so far.
  • Goal: Complete end-to-end EDA, preprocessing, and feature engineering.
  • Pomodoro: Spend 1 Pomodoro (25 minutes) preparing for a hands-on

project.

• Task 2: Work on a mini project applying all the preprocessing, EDA, and feature engineering techniques.
  • Goal: Ensure you can complete a full cycle of EDA, preprocessing, and feature engineering.
  • Pomodoro: Complete a small project (75 minutes total).

Week 3: Supervised Learning Models

Day 15: Introduction to Supervised Learning

• Task 1: Study the concepts of Supervised Learning.

  • Goal: Understand the basics of supervised learning, its applications, and how models learn from labeled data.
  • Pomodoro: Watch videos/tutorials introducing supervised learning (30 minutes).
  • Outcome: Write a brief summary of supervised learning types, its importance, and common algorithms (10 minutes).

• Task 2: Introduction to Classification Algorithms.

  • Goal: Understand classification problems and algorithms like Logistic Regression and K-Nearest Neighbors (KNN).
  • Pomodoro: Watch tutorials on Logistic Regression (25 minutes), followed by KNN (25 minutes).

Day 16: Logistic Regression

• Task 1: Study the concept of Logistic Regression for classification problems.
  • Goal: Learn how logistic regression works and when to use it.
  • Pomodoro: Watch tutorial videos on Logistic Regression theory (30 minutes).
• Task 2: Implement Logistic Regression on a dataset.
  • Goal: Apply logistic regression to solve a classification problem, like predicting whether a passenger survived the Titanic disaster.
  • Pomodoro: Code the Logistic Regression model and evaluate its performance (45 minutes).
• Task 3: Model evaluation using Confusion Matrix and Accuracy.
  • Goal: Learn how to evaluate classification models using a confusion matrix, precision, recall, F1-score, etc.
  • Pomodoro: Implement and interpret evaluation metrics (25 minutes).

Day 17: K-Nearest Neighbors (KNN)

• Task 1: Study the K-Nearest Neighbors (KNN) algorithm.

  • Goal: Understand how KNN works and its advantages and disadvantages.
  • Pomodoro: Watch tutorial on KNN (30 minutes).

• Task 2: Implement KNN on a classification dataset.

  • Goal: Apply KNN to classify the Iris dataset or Titanic dataset.
  • Pomodoro: Build KNN models, tune hyperparameters like k (number of neighbors) (45 minutes).

• Task 3: Evaluate model performance using accuracy, precision, and recall.

  • Goal: Learn to assess KNN using performance metrics.
  • Pomodoro: Evaluate the model with confusion matrix and interpret results (30 minutes).

Day 18: Support Vector Machines (SVM)

• Task 1: Learn about Support Vector Machines (SVM).

  • Goal: Study how SVM works for binary classification problems.
  • Pomodoro: Watch videos/tutorials explaining SVM theory (30 minutes).

• Task 2: Implement SVM using Scikit-learn.

  • Goal: Apply the SVM algorithm to classify a dataset.
  • Pomodoro: Train SVM models and test them on the Iris or Titanic dataset (45 minutes).

• Task 3: Model evaluation using Hyperplane and Margin.

  • Goal: Learn how SVM works by visualizing the decision boundary and margin.
  • Pomodoro: Evaluate SVM results, visualize the decision boundary (25 minutes).

Day 19: Decision Trees

• Task 1: Study Decision Trees for classification tasks.

  • Goal: Understand how decision trees split data at each node and make predictions.
  • Pomodoro: Watch tutorials explaining decision trees (30 minutes).

• Task 2: Implement Decision Tree Classifier using Scikit-learn.

  • Goal: Train a decision tree model on a classification problem.
  • Pomodoro: Build a decision tree and evaluate its performance using cross-validation (45 minutes).

• Task 3: Learn about Overfitting in Decision Trees and how to avoid it (pruning).

  • Goal: Understand how to avoid overfitting by pruning the tree.
  • Pomodoro: Practice decision tree pruning on a dataset (30 minutes).

Day 20: Random Forest

• Task 1: Learn the concept of Random Forest.

  • Goal: Understand how Random Forest is an ensemble method that combines multiple decision trees.
  • Pomodoro: Watch tutorial explaining Random Forest theory and implementation (30 minutes).

• Task 2: Implement Random Forest Classifier using Scikit-learn.

  • Goal: Train a Random Forest model and evaluate its performance.
  • Pomodoro: Apply Random Forest on the Titanic dataset, analyze the results (45 minutes).

• Task 3: Hyperparameter tuning of Random Forest using GridSearchCV.

  • Goal: Learn how to fine-tune hyperparameters like number of trees and maximum depth for optimal performance.
  • Pomodoro: Experiment with hyperparameters (25 minutes).

Day 21: Model Comparison and Evaluation

• Task 1: Review all supervised learning algorithms studied in the week.

  • Goal: Reinforce your understanding by reviewing and comparing all models.
  • Pomodoro: Spend 1 Pomodoro reviewing logistic regression, KNN, SVM, decision trees, and random forests (25 minutes).

• Task 2: Perform a model comparison.

  • Goal: Apply all the models (Logistic Regression, KNN, SVM, Decision Tree, Random Forest) on the same dataset and compare their performance.
  • Pomodoro: Evaluate models using accuracy, confusion matrix, precision, and recall metrics (75 minutes).

Week 4: Advanced Supervised Learning Models

Day 22: Gradient Boosting

• Task 1: Learn the concept of Gradient Boosting.

  • Goal: Understand how boosting works and how it improves model performance by combining weak learners.
  • Pomodoro: Watch tutorial on gradient boosting theory (30 minutes).

• Task 2: Implement Gradient Boosting using Scikit-learn.

  • Goal: Apply the gradient boosting model on the Titanic dataset.
  • Pomodoro: Train Gradient Boosting models, and evaluate their performance (45 minutes).

• Task 3: Tune hyperparameters of Gradient Boosting.

  • Goal: Learn how to adjust parameters like learning rate and number of estimators for better model performance.
  • Pomodoro: Implement hyperparameter tuning using GridSearchCV (25 minutes).

Day 23: XGBoost

• Task 1: Study XGBoost (Extreme Gradient Boosting).

  • Goal: Understand the advantages of XGBoost over traditional gradient boosting.
  • Pomodoro: Watch tutorial on XGBoost theory and its applications (30 minutes).

• Task 2: Implement XGBoost using Python.

  • Goal: Apply XGBoost on the Titanic dataset.
  • Pomodoro: Train XGBoost models, test their performance (45 minutes).

• Task 3: Tune XGBoost Hyperparameters for optimal performance.

  • Goal: Learn how to tune XGBoost’s hyperparameters for optimal results.
  • Pomodoro: Implement hyperparameter tuning using GridSearchCV (25 minutes).

Day 24: LightGBM

• Task 1: Learn about LightGBM (Light Gradient Boosting Machine).

  • Goal: Understand how LightGBM works and its efficiency compared to other boosting algorithms.
  • Pomodoro: Watch tutorials on LightGBM (30 minutes).

• Task 2: Implement LightGBM for classification problems.

  • Goal: Use LightGBM on a classification dataset and evaluate its performance.
  • Pomodoro: Train and test LightGBM models (45 minutes).

• Task 3: Tune LightGBM hyperparameters.

  • Goal: Understand and apply hyperparameter tuning to improve LightGBM performance.
  • Pomodoro: Apply GridSearchCV to fine-tune LightGBM (25 minutes).

Day 25: Model Evaluation and Ensemble Techniques

• Task 1: Review and consolidate knowledge of model evaluation.

  • Goal: Reinforce your understanding of model evaluation techniques (cross-validation, confusion matrix, etc.).
  • Pomodoro: Review evaluation techniques (25 minutes).

• Task 2: Study Ensemble Techniques (Voting, Stacking, Bagging, Boosting).

  • Goal: Understand different ensemble techniques and their importance in improving model performance.
  • Pomodoro: Watch videos on ensemble methods (30 minutes).

• Task 3: Implement Voting Classifier for ensemble learning.

  • Goal: Learn how to combine multiple models (e.g., Logistic Regression, SVM, Random Forest) into an ensemble.
  • Pomodoro: Train a voting classifier and

evaluate its performance (45 minutes).

Day 26: Model Tuning and Optimization

• Task 1: Learn about model tuning and optimization techniques.

  • Goal: Understand the importance of model tuning in improving model accuracy and performance.
  • Pomodoro: Watch tutorials on hyperparameter optimization (30 minutes).

• Task 2: Tune the models using GridSearchCV and RandomizedSearchCV.

  • Goal: Practice hyperparameter tuning to find the best-performing model configuration.
  • Pomodoro: Use GridSearchCV and RandomizedSearchCV to tune models (45 minutes).

• Task 3: Evaluate final models using appropriate metrics (e.g., AUC, ROC curve).

  • Goal: Learn advanced evaluation techniques and metrics to assess model performance.
  • Pomodoro: Implement advanced evaluation metrics (30 minutes).

Day 27: Review and Consolidation

• Task 1: Review everything covered this week: from supervised learning models to ensemble techniques and hyperparameter tuning.

  • Goal: Reinforce all concepts learned so far.
  • Pomodoro: Review all models and their implementations (30 minutes).

• Task 2: Practice model comparison and evaluation with all algorithms.

  • Goal: Perform a model comparison on a fresh dataset.
  • Pomodoro: Evaluate and compare models such as Logistic Regression, KNN, SVM, Random Forest, and Gradient Boosting (75 minutes).

Week 5: Advanced Concepts in Azure Machine Learning and Model Deployment

Day 29: Introduction to Azure Machine Learning Studio

• Task 1: Familiarize with Azure ML Studio Interface.

  • Goal: Understand the layout and features of Azure Machine Learning Studio. Learn how to navigate through different sections such as datasets, experiments, and models.
  • Pomodoro: Spend 45 minutes exploring the Azure ML Studio interface, watching tutorial videos, and reading basic documentation.

• Task 2: Create and Manage Datasets in Azure ML.

  • Goal: Learn how to upload, manage, and preprocess datasets in Azure ML Studio.
  • Pomodoro: Upload a sample dataset (e.g., Titanic dataset) and practice basic preprocessing like handling missing values and feature scaling (45 minutes).

• Task 3: Experiment with Data Transformation.

  • Goal: Practice data transformation techniques such as normalization, encoding categorical features, and splitting datasets.
  • Pomodoro: Apply data transformation techniques to the uploaded dataset (30 minutes).

Day 30: Supervised Learning with Azure Machine Learning

• Task 1: Train Supervised Models in Azure ML.

  • Goal: Learn how to train models like Linear Regression, Logistic Regression, and Decision Trees in Azure ML Studio.
  • Pomodoro: Train a model using the Titanic dataset and evaluate performance (45 minutes).

• Task 2: Hyperparameter Tuning in Azure.

  • Goal: Understand how to tune the hyperparameters of models using Azure ML. Learn about different search methods for hyperparameter optimization.
  • Pomodoro: Implement a hyperparameter tuning job in Azure ML (45 minutes).

• Task 3: Evaluate Models in Azure ML Studio.

  • Goal: Learn how to evaluate models using various metrics such as Accuracy, Precision, Recall, and F1-Score.
  • Pomodoro: Evaluate your trained model and interpret the evaluation metrics (30 minutes).

Day 31: Unsupervised Learning with Azure Machine Learning

• Task 1: Learn Clustering Algorithms (e.g., K-Means).

  • Goal: Understand how to implement and apply clustering algorithms like K-Means in Azure ML Studio.
  • Pomodoro: Implement K-Means clustering on a sample dataset and visualize the results (45 minutes).

• Task 2: Dimensionality Reduction (PCA).

  • Goal: Learn about dimensionality reduction techniques such as Principal Component Analysis (PCA) and how to apply it in Azure ML Studio.
  • Pomodoro: Implement PCA on a dataset and interpret the results (45 minutes).

• Task 3: Practice Unsupervised Learning.

  • Goal: Apply the techniques you've learned by experimenting with unsupervised learning models on a new dataset.
  • Pomodoro: Experiment with clustering and PCA on the Iris dataset (30 minutes).

Day 32: Model Deployment Concepts in Azure

• Task 1: Introduction to Model Deployment in Azure.

  • Goal: Understand the steps and concepts involved in deploying machine learning models using Azure Machine Learning service.
  • Pomodoro: Watch videos on model deployment basics in Azure and read documentation (30 minutes).

• Task 2: Deploying Models as Web Services.

  • Goal: Learn how to deploy a trained model as a REST API using Azure ML.
  • Pomodoro: Deploy a sample model and test it via a REST API (60 minutes).

• Task 3: Evaluate Deployed Models.

  • Goal: Learn how to monitor and evaluate the performance of deployed models.
  • Pomodoro: Test the deployed API with new data and evaluate its performance (30 minutes).

Day 33: Cross-Validation and Model Performance

• Task 1: Learn Cross-Validation Techniques.

  • Goal: Understand the importance of cross-validation for model selection and tuning.
  • Pomodoro: Study K-fold cross-validation and implement it for model evaluation (45 minutes).

• Task 2: Apply Cross-Validation to Models.

  • Goal: Implement cross-validation for a regression model (e.g., Linear Regression).
  • Pomodoro: Perform cross-validation and compare the results with a simple train-test split (45 minutes).

• Task 3: Review Model Evaluation Metrics.

  • Goal: Deep dive into various model evaluation metrics, such as AUC-ROC, R-Squared, MSE, and MAE, and understand their application.
  • Pomodoro: Review evaluation metrics and analyze model performance based on these metrics (30 minutes).

Day 34: Introduction to Azure AutoML

• Task 1: Overview of Azure AutoML.

  • Goal: Understand how Azure AutoML simplifies model creation and selection, and how it handles tasks like feature engineering and hyperparameter tuning.
  • Pomodoro: Watch tutorials and read documentation on how to set up AutoML experiments (30 minutes).

• Task 2: Set Up an AutoML Experiment in Azure.

  • Goal: Learn how to create an AutoML experiment for classification tasks.
  • Pomodoro: Set up and run an AutoML experiment on the Iris dataset or Titanic dataset (60 minutes).

• Task 3: Evaluate AutoML Results.

  • Goal: Learn how to interpret the results of an AutoML experiment, including feature importance and best-performing models.
  • Pomodoro: Analyze the AutoML experiment results and evaluate the best model (30 minutes).

Day 35: Model Versioning and Model Management in Azure

• Task 1: Understand Model Versioning in Azure.

  • Goal: Learn how to manage different versions of your models and track them effectively.
  • Pomodoro: Review the process of versioning models and setting up version control in Azure ML (30 minutes).

• Task 2: Use Azure Model Management.

  • Goal: Learn how to manage, register, and deploy multiple versions of models.
  • Pomodoro: Implement model versioning and manage models using Azure ML (45 minutes).

• Task 3: Practice Model Rollback and Redeployment.

  • Goal: Learn how to rollback to a previous model version if necessary and redeploy.
  • Pomodoro: Experiment with model rollback and redeployment in the Azure ML interface (30 minutes).

Week 6: Advanced Model Techniques and Operationalizing ML Solutions

Day 36: Introduction to Model Scaling in Azure

• Task 1: Understand Scaling in Azure ML.

  • Goal: Learn how to scale machine learning models for large datasets and high-performance requirements in Azure.
  • Pomodoro: Watch tutorials on scaling models in Azure and read relevant documentation (45 minutes).

• Task 2: Set Up Scalable Compute Resources.

  • Goal: Learn how to configure and set up scalable compute resources (e.g., Azure ML compute clusters, VM instances).
  • Pomodoro: Set up an Azure ML compute instance for training and scaling large models (45 minutes).

• Task 3: Test Model Performance with Scalable Resources.

  • Goal: Test your model's performance using scalable compute resources and analyze how scaling impacts performance and training time.
  • Pomodoro: Run a model on the compute cluster and compare performance (30 minutes).

Day 37: Distributed Machine Learning and Parallelization

• Task 1: Learn about Distributed Training.

  • Goal: Understand distributed machine learning and how to parallelize model training using Azure ML.
  • Pomodoro: Study how distributed training works in Azure ML, focusing on techniques like data parallelism and model parallelism (45 minutes).

• Task 2: Set Up Distributed Training.

  • Goal: Configure and implement distributed training for a large model (e.g., deep learning model) in Azure.
  • Pomodoro: Set up distributed training on a large dataset and monitor training performance (45 minutes).

• Task 3: Monitor and Analyze Distributed Training.

  • Goal: Learn how to monitor the performance of distributed training jobs, focusing on resource utilization and model convergence.
  • Pomodoro: Use Azure ML's monitoring tools to analyze the results of distributed training (30 minutes).

Day 38: Model Deployment with Azure Kubernetes Service (AKS)

• Task 1: Understand AKS for Model Deployment.

  • Goal: Learn about Azure Kubernetes Service (AKS) and its role in deploying scalable machine learning models.
  • Pomodoro: Study the basics of Kubernetes and how Azure ML integrates with AKS for deploying models (30 minutes).

• Task 2: Deploy a Model on AKS.

  • Goal: Learn how to deploy a machine learning model on Azure Kubernetes Service.
  • Pomodoro: Deploy a trained model on AKS, ensuring it is accessible via REST API for real-time predictions (60 minutes).

• Task 3: Test AKS Deployment.

  • Goal: Test the deployed model on AKS by sending requests to the deployed API and validating the model’s response.
  • Pomodoro: Perform API testing by sending requests and analyzing model outputs (30 minutes).

Day 39: Continuous Integration and Continuous Deployment (CI/CD)

• Task 1: Understand CI/CD Pipelines in Azure ML.

  • Goal: Learn how to implement continuous integration and continuous deployment (CI/CD) pipelines for machine learning models in Azure.
  • Pomodoro: Study the concept of CI/CD pipelines in machine learning, focusing on how Azure DevOps integrates with Azure ML (45 minutes).

• Task 2: Set Up CI/CD Pipeline for ML Models.

  • Goal: Learn how to automate the process of training, testing, and deploying machine learning models using CI/CD pipelines.
  • Pomodoro: Set up a basic CI/CD pipeline using Azure DevOps and Azure ML (60 minutes).

• Task 3: Monitor and Automate Model Retraining.

  • Goal: Implement a system where models are automatically retrained when new data becomes available, using CI/CD pipelines.
  • Pomodoro: Set up automated retraining for a model using the CI/CD pipeline (30 minutes).

Day 40: Monitor and Maintain Models in Production

• Task 1: Understand Model Monitoring.

  • Goal: Learn about best practices for monitoring models in production environments.
  • Pomodoro: Watch a video on model monitoring tools and techniques in Azure ML (30 minutes).

• Task 2: Implement Model Monitoring in Azure.

  • Goal: Learn how to monitor deployed models and track key performance metrics (e.g., accuracy, latency).
  • Pomodoro: Implement model monitoring using Azure ML and track real-time metrics (60 minutes).

• Task 3: Set Up Alerts and Retraining Triggers.

  • Goal: Learn how to set up automated alerts for when model performance degrades and trigger retraining if necessary.
  • Pomodoro: Configure alerts and retraining triggers in Azure ML (30 minutes).

Day 41: Cost Management and Resource Optimization

• Task 1: Understand Azure ML Cost Management.

  • Goal: Learn how to monitor and optimize the costs associated with machine learning workflows in Azure.
  • Pomodoro: Study Azure cost management tools and explore how to optimize resource usage (45 minutes).

• Task 2: Optimize Resource Usage in Azure.

  • Goal: Learn how to select the right compute resources to optimize both performance and cost in Azure ML.
  • Pomodoro: Experiment with different resource configurations to optimize cost and performance (45 minutes).

• Task 3: Set Up Budgeting and Alerts.

  • Goal: Set up budgeting and cost alerts in Azure to ensure your machine learning projects stay within budget.
  • Pomodoro: Set up cost alerts and review budget reports (30 minutes).

Day 42: Review and Practice

• Task 1: Review All Topics Covered in Week 6.

  • Goal: Review all concepts learned throughout the week, including model scaling, AKS deployment, CI/CD pipelines, and monitoring.
  • Pomodoro: Review notes, documentation, and recorded sessions to reinforce learning (45 minutes).

• Task 2: Hands-On Practice.

  • Goal: Apply what you've learned throughout the week by working on a small end-to-end project involving scaling and deployment.
  • Pomodoro: Start an end-to-end project that involves training a model, deploying it to AKS, and setting up monitoring and retraining (60 minutes).

• Task 3: Prepare for Next Week.

  • Goal: Review any difficult topics and prepare for next week's advanced concepts.
  • Pomodoro: Review and mark down any unclear topics for further clarification (30 minutes).

Week 7:Finalizing Your Skills and Exam Preparation

Day 43: Review of Core Concepts

• Task 1: Review Key Concepts from Week 1 to Week 6 (Focus on weak points).

  • Goal: Consolidate foundational knowledge in Azure ML, model training, deployment, and operationalization.
  • Pomodoro: Spend 45 minutes revisiting the main topics such as training models, deployment strategies, and scaling.

• Task 2: Revisit Azure ML Tools and Services.

  • Goal: Familiarize yourself with Azure's main tools like Azure ML studio, AKS, Azure Databricks.
  • Pomodoro: Spend 45 minutes watching tutorials or reading documentation on these tools.

• Task 3: Hands-On Practice with Azure ML Studio.

  • Goal: Strengthen your practical skills in model training and deployment.
  • Pomodoro: Spend 30 minutes creating a simple project on Azure ML studio and deploying it.

Day 44: Model Deployment and Monitoring Review

• Task 1: Deep Dive into Deployment Strategies.

  • Goal: Focus on real-time, batch, and AKS deployment strategies, and their use cases.
  • Pomodoro: Spend 45 minutes reviewing deployment concepts and best practices.

• Task 2: Practice Deployment on AKS.

  • Goal: Recreate the process of deploying a model using Azure Kubernetes Service (AKS).
  • Pomodoro: Spend 60 minutes deploying a model on AKS and test it with REST APIs.

• Task 3: Monitor Model Performance.

  • Goal: Revisit monitoring tools in Azure to track model performance.
  • Pomodoro: Spend 30 minutes configuring model monitoring and setting alerts.

Day 45: CI/CD Pipeline and Cost Management

• Task 1: Study CI/CD Pipeline Setup.

  • Goal: Review the steps for setting up CI/CD pipelines in Azure to automate model deployment.
  • Pomodoro: Spend 45 minutes learning about automated workflows for retraining and deploying models.

• Task 2: Implement a CI/CD Pipeline.

  • Goal: Set up and test a CI/CD pipeline with a machine learning model.
  • Pomodoro: Spend 60 minutes implementing a CI/CD pipeline using Azure ML tools.

• Task 3: Cost Management.

  • Goal: Learn how to manage and optimize costs in Azure while deploying models.
  • Pomodoro: Spend 30 minutes reviewing best practices for cost management in Azure.

Day 46: Advanced Model Optimization Techniques

• Task 1: Hyperparameter Tuning.

  • Goal: Learn about hyperparameter optimization techniques such as grid search and random search.
  • Pomodoro: Spend 45 minutes reading and practicing advanced hyperparameter tuning methods.

• Task 2: Apply Hyperparameter Tuning to a Model.

  • Goal: Tune the hyperparameters of a chosen model using grid search or random search.
  • Pomodoro: Spend 60 minutes applying these techniques on a machine learning model.

• Task 3: Model Evaluation.

  • Goal: Review model evaluation metrics such as accuracy, F1 score, ROC-AUC, and more.
  • Pomodoro: Spend 30 minutes revisiting the evaluation metrics and applying them to model performance.

Day 47: Exam-Specific Review

• Task 1: Review the DP-100 Exam Topics.

  • Goal: Review all key topics covered in the DP-100 exam syllabus.
  • Pomodoro: Spend 45 minutes going over the official exam guide and refreshing any weak areas.

• Task 2: Practice with Sample Exam Questions.

  • Goal: Test your knowledge by practicing exam questions.
  • Pomodoro: Spend 45 minutes completing practice questions from the exam guide.

• Task 3: Identify Weak Areas and Review.

  • Goal: Identify topics where you struggled and spend additional time revisiting them.
  • Pomodoro: Spend 30 minutes reviewing the weak areas that were identified from your practice test.

Day 48: Mock Exam and Simulation

• Task 1: Take a Full-Length Mock Exam.

  • Goal: Simulate the actual exam experience to assess your readiness.
  • Pomodoro: Spend 90 minutes completing a full-length practice exam under timed conditions.

• Task 2: Review Practice Exam Results.

  • Goal: Review incorrect answers and study the corresponding materials.
  • Pomodoro: Spend 45 minutes analyzing the mock exam results.

• Task 3: Focus on Weak Points.

  • Goal: Deep dive into areas where you scored poorly on the mock exam.
  • Pomodoro: Spend 30 minutes revisiting weak areas and reinforcing your knowledge.

Day 49: Final Review and Exam Strategy

• Task 1: Review Key Concepts.

  • Goal: Revisit the most critical exam concepts such as deployment, scaling, and cost management.
  • Pomodoro: Spend 45 minutes quickly reviewing key topics before the exam.

• Task 2: Time Management for the Exam.

  • Goal: Learn how to pace yourself during the exam.
  • Pomodoro: Spend 30 minutes reviewing exam time management strategies.

• Task 3: Final Mock Exam Review.

  • Goal: Take one final set of practice exam questions to consolidate your knowledge.
  • Pomodoro: Spend 45 minutes doing a final mock test and reviewing your answers.

Week 8: Final Countdown-Perfecting Your Skills and Confidence for the Exam

Day 50: Final Review of Core Concepts

• Task 1: Review Key Topics from All Weeks

  • Goal: Consolidate everything you've learned by reviewing all the main topics. Focus on areas where you feel less confident.
  • Pomodoro: Spend 45 minutes on each of the main categories (model training, deployment strategies, scaling, cost management).

• Task 2: Revisit Azure ML Tools

  • Goal: Ensure you're comfortable with Azure tools like AKS, Azure ML studio, and monitoring.
  • Pomodoro: Spend 45 minutes revisiting practical tasks you've done with these tools, focusing on hands-on experience.

• Task 3: Read Official Documentation (If Necessary)

  • Goal: Go over any remaining doubts or tricky areas in the official Microsoft documentation.
  • Pomodoro: Spend 30 minutes reading through any relevant sections in the official Azure documentation.

Day 51: Review of Deployment and Monitoring

• Task 1: Deep Dive into Deployment Strategies

  • Goal: Refine your understanding of deployment options like real-time and batch, and review the tools for deployment like AKS.
  • Pomodoro: Spend 45 minutes revisiting deployment concepts, focusing on real-world applications of model deployment.

• Task 2: Deploy a Model Again (Hands-On)

  • Goal: Practice deploying a model, this time focusing on Azure Kubernetes Services (AKS) and Azure Functions.
  • Pomodoro: Spend 60 minutes deploying and testing your model in AKS and ensuring proper scaling.

• Task 3: Test Model Monitoring

  • Goal: Test and tweak monitoring setups to track performance over time.
  • Pomodoro: Spend 30 minutes setting up alerts, logging, and monitoring tools in Azure.

Day 52: Advanced Model Optimization

• Task 1: Review Hyperparameter Tuning

  • Goal: Refine your skills in hyperparameter tuning and learn how to set up grid search and random search.
  • Pomodoro: Spend 45 minutes studying hyperparameter optimization strategies and experimenting with them.

• Task 2: Apply Hyperparameter Tuning to Your Model

  • Goal: Fine-tune a model and test it by modifying hyperparameters to improve performance.
  • Pomodoro: Spend 60 minutes applying these tuning methods to one of your previous models.

• Task 3: Revisit Model Evaluation Metrics

  • Goal: Ensure you understand all evaluation metrics like accuracy, F1-score, ROC-AUC for classification, and R-squared for regression.
  • Pomodoro: Spend 30 minutes reviewing and applying the metrics to the models you’ve worked on.

Day 53: Exam-Specific Preparation

• Task 1: Go Through Exam Questions

  • Goal: Practice with official sample questions to simulate the exam environment.
  • Pomodoro: Spend 45 minutes completing questions from the DP-100 exam guide.

• Task 2: Identify and Revisit Weak Points

  • Goal: Spend more time on areas where you're still unsure or made errors during practice.
  • Pomodoro: Spend 45 minutes revisiting those weak areas, focusing on Azure tools and deployment strategies.

• Task 3: Review Official Exam Resources

  • Goal: Review any remaining sections from the official Microsoft learning path or documentation that might need reinforcement.
  • Pomodoro: Spend 30 minutes reviewing any last-minute details.

Day 54: Mock Exam

• Task 1: Take a Full-Length Mock Exam

  • Goal: Simulate the full exam experience by completing a full-length practice exam under timed conditions.
  • Pomodoro: Spend 90 minutes taking the mock exam.

• Task 2: Review Mock Exam Results

  • Goal: Carefully go over all the incorrect answers and study why they were wrong. Focus on the mistakes to correct them before the actual exam.
  • Pomodoro: Spend 45 minutes analyzing your mock exam results and studying the areas where you performed poorly.

• Task 3: Test Time Management Skills

  • Goal: Learn how to pace yourself for the actual exam. Don’t spend too much time on one question.
  • Pomodoro: Spend 30 minutes practicing time management strategies.

Day 55: Last Review and Final Adjustments

• Task 1: Review Key Topics One More Time

  • Goal: Go over important concepts you feel unsure about. Prioritize model training, deployment, and scaling.
  • Pomodoro: Spend 45 minutes reviewing your weak points from earlier weeks.

• Task 2: Finalize Exam Strategy

  • Goal: Have a clear plan for your exam day. Reassure yourself with a quick review of exam tips and tricks.
  • Pomodoro: Spend 30 minutes reviewing time management, and relax.

• Task 3: Relax and Prepare Mentally for the Exam

  • Goal: Relax, do some light review if necessary, but avoid cramming. Mental readiness is key.
  • Pomodoro: Spend 30 minutes doing some deep breathing or relaxation exercises to calm any nerves.

Day 56: The Exam Day!

• Task 1: Final Relaxation

  • Goal: Get a good night’s rest and eat a healthy meal before the exam.
  • Pomodoro: Stay calm, stay confident, and do light review if needed.

• Task 2: Take the Exam!

  • Goal: Focus, stay calm, and pace yourself during the exam.
  • Pomodoro: Follow your time management strategies, read questions carefully, and review answers if time allows.