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 To create an EKS Cluster using the `eksctl` command in Windows 11, you need to install the `eksctl` software. Follow these steps to install `eksctl`: 1. **Install `eksctl`:**    - Open your preferred terminal (Command Prompt, PowerShell, or Windows Terminal).    - Download the `eksctl` binary by running the following command:      ```powershell      Invoke-WebRequest -Uri "https://github.com/weaveworks/eksctl/releases/latest/download/eksctl_windows_amd64.zip" -OutFile "eksctl.zip"      ```    - Extract the downloaded zip file:      ```powershell      Expand-Archive -Path "eksctl.zip" -DestinationPath "$HOME\.eksctl"      ```    - Add the `eksctl` binary to your PATH:      ```powershell      $env:Path += ";$HOME\.eksctl"      ```    Alternatively, you can manually download the `eksctl` binary from the [official GitHub releases page](https://github.com/weaveworks/eksctl/releases) and extract it to a directory that is included in your sys

 ### AI Skills \ #### Binary Classification Algorithms - **Naive Bayes**: This algorithm guesses the class of data based on probabilities. - **Logistic Regression**: It predicts if something belongs to a category or not, like yes or no. - **K-Nearest Neighbors (KNN)**: It classifies data by looking at the closest examples in the dataset. - **Support Vector Machine (SVM)**: It finds the best line that separates different classes of data. - **Decision Tree**: It makes decisions by splitting data into branches based on features. - **Random Forest**: It uses many decision trees to make more accurate predictions. - **Neural Network (Deep Learning)**: It mimics the human brain to learn from data and make predictions. #### Unsupervised Machine Learning Algorithms - **K-means Clustering**: It groups similar data points into clusters. - **PCA (Principal Component Analysis)**: It reduces the number of features in data while keeping important information. - **CNN (Convolutional Neural Network)**:

 ### IT Job Interview Dialogue between Deniz and Timothy **Scene: Timothy arrives at the company for his scheduled interview. He approaches the reception desk where a receptionist is seated.** **Receptionist**: Good morning! How can I help you today? **Timothy**: Good morning. I have an interview scheduled at 11 o'clock with Deniz. **Receptionist**: Alright, let me check the schedule. Could I have your name, please? **Timothy**: It's Timothy. **Receptionist**: Okay, Timothy. I see your appointment here. Deniz should be with you shortly. Please have a seat in the waiting area. **Timothy**: Thank you. By the way, do I need to complete any programming tests before the interview? **Receptionist**: Let me check with Deniz on that. Please wait for a moment. **Timothy**: Sure, thank you. *After a few minutes, Deniz walks into the reception area.* **Deniz**: Hi Timothy, nice to meet you. I’m Deniz. Shall we head to the meeting room? **Timothy**: Nice to meet you too, Deniz. Sure, let&#

 To enhance embedding in your Retrieval-Augmented Generation (RAG) application based on the "Python RAG Tutorial.txt" notes, follow these steps in simple English: 1. **Use High-Quality Embeddings**:    - Choose high-quality embeddings to ensure accurate matching between your queries and the relevant data chunks. Consider using services like OpenAI or AWS Bedrock, as they provide reliable embeddings. 2. **Consistent Embedding Function**:    - Use the same embedding function for both creating the database and querying it. This ensures consistency and better performance. 3. **Manage Large Documents**:    - Split large documents into smaller chunks. Use tools like Langchain's recursive text splitter. Smaller chunks improve indexing and retrieval accuracy. 4. **Update the Vector Database**:    - Add a unique ID to each data chunk. Use the file path, page number, and chunk number to create these IDs. This helps in updating the database without duplicating entries. 5. **Local an

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