Cisco Implementing Cisco Enterprise Advanced Routing and Services v1 (ENARSI)

1 – SOLVING BUSINESS PROBLEMS USING AI AND ML

• Topic A: Identify AI and ML Solutions for Business Problems
• Topic B: Follow a Machine Learning Workflow
• Topic C: Formulate a Machine Learning Problem
• Topic D: Select Appropriate Tools

2 – COLLECTING AND REFINING THE DATASET

• Topic A: Collect the Dataset
• Topic B: Analyze the Dataset to Gain Insights
• Topic C: Use Visualizations to Analyze Data
• Topic D: Prepare Data

3 – SETTING UP AND TRAINING A MODEL

• Topic A: Set Up a Machine Learning Model
• Topic B: Train the Model

4 – FINALIZING A MODEL

• Topic A: Translate Results into Business Actions
• Topic B: Incorporate a Model into a Long-Term Business Solution

5 – BUILDING LINEAR REGRESSION MODELS

• Topic A: Build Regression Models Using Linear Algebra
• Topic B: Build Regularized Regression Models Using Linear Algebra
• Topic C: Build Iterative Linear Regression Models

6 – BUILDING CLASSIFICATION MODELS

• Topic A: Train Binary Classification Models
• Topic B: Train Multi-Class Classification Models
• Topic C: Evaluate Classification Models
• Topic D: Tune Classification Models

7 – BUILDING CLUSTERING MODELS

• Topic A: Build k-Means Clustering Models
• Topic B: Build Hierarchical Clustering Models

8 – BUILDING DECISION TREES AND RANDOM FORESTS

• Topic A: Build Decision Tree Models
• Topic B: Build Random Forest Models

9 – BUILDING SUPPORT-VECTOR MACHINES

• Topic A: Build SVM Models for Classification
• Topic B: Build SVM Models for Regression

10 – BUILDING ARTIFICIAL NEURAL NETWORKS

• Topic A: Build Multi-Layer Perceptrons (MLP)
• Topic B: Build Convolutional Neural Networks (CNN)
• Topic C: Build Recurrent Neural Networks (RNN)

11 – PROMOTING DATA PRIVACY AND ETHICAL PRACTICES

• Topic A: Protect Data Privacy
• Topic B: Promote Ethical Practices
• Topic C: Establish Data Privacy and Ethics Policies