~ Technology tutorials and blogs written by GPT Tutor ~
~ Technology tutorials and blogs written by GPT Tutor ~
🧩 Part 3/10
✔ What is a Chatbot Persona and Why is it Important?
✔ Chatbot Persona Examples
✔ How to Create a Chatbot Persona
✔ What is a Conversation Flow and Why is it Important?
✔ Conversation Flow Examples
✔ How to Create a Conversation Flow
✔ How to Use User Stories to Define Your Chatbot's Goals and Scenarios
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🧩 Part 2/10
✔ What are Chatbot Frameworks and Platforms?
✔ Chatbot Frameworks: Rasa, Microsoft Bot Framework, and Dialogflow
✔ Rasa
✔ Microsoft Bot Framework
✔ Dialogflow
✔ Chatbot Platforms: Facebook Messenger, Slack, and Telegram
✔ Facebook Messenger
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🧩 Part 1/10
✔ What is NLP and why is it important?
✔ What are chatbots and how do they use NLP?
✔ Types of chatbots and their applications
✔ Challenges and limitations of chatbots
✔ How to build a simple chatbot using NLP tools
✔ Choosing a platform and a framework
✔ Designing the conversation flow and the intents
✔ Implementing natural language understanding and generation
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🧩 Part 3/8
✔ Overview of Model Selection and Training in AWS AutoML
✔ How to Create an AutoML Job
✔ Specify the Data Source and Target Attribute
✔ Choose the Problem Type and Objective Metric
✔ Configure the AutoML Job Settings
✔ How to Monitor the AutoML Job Progress
✔ How to Compare and Select the Best Candidate Model
✔ How to Evaluate the Model Performance on Test Data
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🧩 Part 2/8
✔ Data Preparation with AWS Data Wrangler
✔ Install and Import AWS Data Wrangler
✔ Load Data from Amazon S3
✔ Clean and Transform Data
✔ Save Data to Amazon S3
✔ Data Analysis with AWS Glue
✔ Create a Glue Crawler
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🧩 Part 1/8
✔ What is AWS AutoML?
✔ Why use AWS AutoML?
✔ Benefits of AWS AutoML
✔ Use cases of AWS AutoML
✔ How to get started with AWS AutoML?
✔ Create an AWS account
✔ Configure your AWS environment
✔ Access the AWS AutoML console
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🧩 Part 3/12
✔ Loading and Exploring Text Data
✔ Preprocessing Text Data
✔ Tokenizing Text Data
✔ Encoding Text Data
✔ Using torchtext for Text Data
✔ Building a Text Classifier with PyTorch
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🧩 Part 2/12
✔ What are Tensors?
✔ How to Create Tensors in PyTorch
✔ How to Manipulate Tensors in PyTorch
✔ Slicing and Indexing Tensors
✔ Reshaping Tensors
✔ Broadcasting Tensors
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🧩 Part 1/12
✔ What is PyTorch?
✔ Why use PyTorch for NLP?
✔ Dynamic computation graphs
✔ Rich set of libraries and tools
✔ Easy integration with other frameworks
✔ How to install PyTorch?
✔ Check the system requirements
✔ Choose the right package manager
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🧩 Part 9/10
✔ What are Transactions and Why are They Important?
✔ Testing Transactions: Challenges and Best Practices
✔ Testing Transaction Isolation Levels
✔ Testing Transaction Concurrency and Locking
✔ Testing Transaction Rollback and Recovery
✔ Debugging Transactions: Tools and Techniques
✔ Logging and Tracing Transactions
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🧩 Part 8/10
✔ What are Distributed Transactions and Why are They Needed?
✔ What are the Challenges of Implementing Distributed Transactions across Multiple Databases?
✔ What is the Two-Phase Commit Protocol and How Does It Work?
✔ What are XA Transactions and How Do They Support Distributed Transactions?
✔ How to Implement Distributed Transactions across Multiple Databases using XA Transactions in Java?
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🧩 Part 7/10
✔ What are Nested Transactions and Savepoints?
✔ Nested Transactions
✔ Savepoints
✔ How to Use Nested Transactions and Savepoints in SQL
✔ Creating a Nested Transaction
✔ Creating a Savepoint
✔ Rolling Back to a Savepoint
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🧩 Part 9/15
✔ TensorFlow Basics for RL
✔ Installing and Importing TensorFlow
✔ Building and Training a Neural Network Model
✔ RL Concepts and Terminology
✔ RL Algorithms and Methods
✔ Value-Based Methods
✔ Policy-Based Methods
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🧩 Part 8/15
✔ What are Generative Adversarial Networks?
✔ The Generator
✔ The Discriminator
✔ The Training Process
✔ How to Implement GANs in TensorFlow
✔ Define the Model Architecture
✔ Define the Loss Functions and Optimizers
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🧩 Part 7/15
✔ What is an Autoencoder?
✔ Encoder
✔ Decoder
✔ Loss Function
✔ Building an Autoencoder with TensorFlow
✔ Importing Libraries and Data
✔ Defining the Model Architecture
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🧩 Part 3/12
✔ Data Preparation
✔ Data Cleaning
✔ Data Transformation
✔ Exploratory Data Analysis
✔ Descriptive Statistics
✔ Correlation Analysis
✔ Outlier Detection
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🧩 Part 2/12
✔ What is Predictive Maintenance and Why is it Important?
✔ Data Collection Methods for Predictive Maintenance
✔ Sensors and IoT Devices
✔ Cloud Services and Data Storage
✔ Data Preparation Techniques for Predictive Maintenance
✔ Data Cleaning and Quality Assessment
✔ Data Transformation and Feature Engineering
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🧩 Part 1/12
✔ What is Predictive Maintenance?
✔ Why is Predictive Maintenance Important?
✔ How Machine Learning Enables Predictive Maintenance
✔ Data Collection and Processing
✔ Feature Engineering and Selection
✔ Model Building and Evaluation
✔ Model Deployment and Monitoring
✔ Challenges and Opportunities of Predictive Maintenance with Machine Learning
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🧩 Part 3/8
✔ What is Swagger and Why Use It?
✔ Swagger Features
✔ Swagger Benefits
✔ How to Add Swagger to Spring MVC Project
✔ Adding Dependencies
✔ Creating Docket Bean
✔ Configuring API Information
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🧩 Part 2/8
✔ Setting Up Spring MVC and Swagger
✔ Creating a Controller Class
✔ Annotating the Controller Class
✔ Defining the Request Mapping Methods
✔ Creating a Model Class
✔ Annotating the Model Class
✔ Adding the Properties and Getters/Setters
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🧩 Part 1/8
✔ What is Spring MVC?
✔ What is Swagger and Why Use It?
✔ Setting Up the Maven Project
✔ Adding Dependencies
✔ Configuring the Application Properties
✔ Creating the Spring MVC Controller
✔ Enabling Swagger in Spring Boot
✔ Adding Annotations
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🧩 Part 3/10
✔ Image Preprocessing Techniques for OCR
✔ Binarization
✔ Skew Correction
✔ Noise Removal
✔ Morphological Operations
✔ Image Preprocessing with Python and OpenCV
✔ Installing and Importing the Libraries
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🧩 Part 2/10
✔ What is OCR and why is it useful for NLP?
✔ What is Tesseract OCR and how does it work?
✔ How to install Tesseract OCR on Windows, Linux, and Mac OS
✔ How to use Tesseract OCR from the command line and with Python
✔ How to improve the accuracy and performance of Tesseract OCR
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🧩 Part 1/10
✔ What is OCR and How Does it Work?
✔ Why is OCR Important for NLP Applications?
✔ Text Extraction and Analysis
✔ Document Classification and Summarization
✔ Information Retrieval and Question Answering
✔ Challenges and Limitations of OCR for NLP
✔ Image Quality and Preprocessing
✔ Language and Script Diversity
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🧩 Part 3/10
✔ What is Tkinter and Why Use It?
✔ How to Install and Import Tkinter
✔ How to Create and Configure Widgets
✔ How to Layout Widgets Using Geometry Managers
✔ How to Bind User Events to Widgets
✔ How to Use Common Widgets in Tkinter
✔ Buttons
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🧩 Part 2/10
✔ What are Events and Why are They Useful?
✔ How to Use the Event Module in Python
✔ Creating Event Objects
✔ Setting and Clearing Events
✔ Waiting for Events
✔ How to Implement the Observer Pattern in Python
✔ Defining the Subject and Observer Classes
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🧩 Part 1/10
✔ What is Event-Driven Programming?
✔ How Event-Driven Programming Works
✔ The Event Loop
✔ The Event Queue
✔ The Callback Functions
✔ Advantages and Disadvantages of Event-Driven Programming
✔ Examples of Event-Driven Programming Languages and Frameworks
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🧩 Part 3/8
✔ Lock-Based Concurrency Control
✔ Lock Types and Modes
✔ Lock Compatibility Matrix
✔ Lock Granularity
✔ Two-Phase Locking Protocol
✔ Basic Two-Phase Locking
✔ Strict Two-Phase Locking
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🧩 Part 2/8
✔ Concurrency Control Techniques
✔ Locking
✔ Timestamping
✔ Validation
✔ Concurrency Control Protocols
✔ Two-Phase Locking Protocol
✔ Timestamp Ordering Protocol
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🧩 Part 1/8
✔ What is Concurrency Control and Why is it Important?
✔ Types of Concurrency Control Techniques
✔ Locking-Based Techniques
✔ Timestamp-Based Techniques
✔ Validation-Based Techniques
✔ Multiversion Techniques
✔ How to Choose the Right Concurrency Control Technique for Your Database
✔ Challenges and Future Directions of Concurrency Control
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🧩 Part 6/8
✔ Web Scraping Basics
✔ What is Web Scraping?
✔ Why Web Scraping?
✔ How Web Scraping Works?
✔ BeautifulSoup4 and requests
✔ Installing and Importing BeautifulSoup4 and requests
✔ Making a Simple HTTP Request
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🧩 Part 5/8
✔ What is Dynamic Content and JavaScript?
✔ How to Install and Set Up Selenium and BeautifulSoup4
✔ How to Use Selenium to Navigate and Interact with Web Pages
✔ How to Use BeautifulSoup4 to Parse and Extract Data from HTML Documents
✔ How to Integrate Selenium and BeautifulSoup4 for Web Scraping
✔ Examples of Web Scraping Dynamic Content and JavaScript with Selenium and BeautifulSoup4
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🧩 Part 4/8
✔ What is BeautifulSoup4 and Why Use It?
✔ Installing and Importing BeautifulSoup4
✔ Choosing a Parser
✔ Creating a BeautifulSoup Object
✔ Navigating the HTML Tree
✔ Searching and Filtering HTML Elements
✔ Extracting Data from HTML Elements
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🧩 Part 3/8
✔ How to create and edit collections
✔ How to create a collection
✔ How to edit a collection
✔ How to use folders to organize requests
✔ How to use variables to store and reuse data
✔ How to create and use collection variables
✔ How to create and use environment variables
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🧩 Part 2/8
✔ How to create a Postman collection
✔ How to add requests to a Postman collection
✔ Create a new request
✔ Save a request to a collection
✔ Edit a request in a collection
✔ How to run a Postman collection
✔ How to share a Postman collection
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🧩 Part 1/8
✔ What is Postman?
✔ What are Postman collections?
✔ How to create a Postman collection
✔ How to run a Postman collection
✔ Why use Postman collections?
✔ To organize and group your API requests
✔ To automate and test your API workflows
✔ To document and share your API collections
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🧩 Part 3/12
✔ Supervised Learning Basics
✔ Regression
✔ Classification
✔ Supervised Learning Applications in Finance
✔ Stock Price Prediction
✔ Credit Risk Assessment
✔ Fraud Detection
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🧩 Part 2/12
✔ Data Sources for Financial Machine Learning
✔ Public Data Sources
✔ Private Data Sources
✔ Alternative Data Sources
✔ Data Preprocessing for Financial Machine Learning
✔ Data Cleaning and Validation
✔ Data Transformation and Normalization
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🧩 Part 1/12
✔ What is Financial Machine Learning?
✔ How Financial Machine Learning Works
✔ Data Sources and Preprocessing
✔ Algorithms and Models
✔ Evaluation and Deployment
✔ Applications of Financial Machine Learning
✔ Algorithmic Trading
✔ Credit Scoring and Fraud Detection
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🧩 Part 9/12
✔ What are Davies-Bouldin index and Dunn index?
✔ Davies-Bouldin index
✔ Dunn index
✔ How to calculate Davies-Bouldin index and Dunn index?
✔ Davies-Bouldin index formula
✔ Dunn index formula
✔ How to interpret Davies-Bouldin index and Dunn index?
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🧩 Part 8/12
✔ What is Clustering and Why is it Important?
✔ How to Evaluate Clustering Performance?
✔ Silhouette Score
✔ Calinski-Harabasz Index
✔ How to Use Silhouette Score and Calinski-Harabasz Index in Python?
✔ How to Interpret and Compare the Results?
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🧩 Part 7/12
✔ What is Regression?
✔ Linear Regression
✔ Nonlinear Regression
✔ What is Adjusted R-Squared?
✔ What is Root Mean Squared Error?
✔ How to Calculate Adjusted R-Squared and Root Mean Squared Error in Python
✔ How to Compare and Evaluate Regression Models Using Adjusted R-Squared and Root Mean Squared Error
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🧩 Part 6/15
✔ What is a Recurrent Neural Network?
✔ The Basic Structure of an RNN
✔ The Challenges of Training an RNN
✔ How to Implement an RNN with TensorFlow
✔ Preparing the Data
✔ Building the Model
✔ Training and Evaluating the Model
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🧩 Part 5/15
✔ What is a Convolutional Neural Network?
✔ Convolutional Layer
✔ Pooling Layer
✔ Fully Connected Layer
✔ How to Implement a Convolutional Neural Network with TensorFlow?
✔ Importing Libraries and Data
✔ Preprocessing Data
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🧩 Part 4/15
✔ What is a Neural Network?
✔ Neurons and Layers
✔ Activation Functions
✔ Network Architecture
✔ How to Train a Neural Network?
✔ Loss Function
✔ Gradient Descent
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🧩 Part 6/10
✔ What is Dialogflow and why use it for chatbot development?
✔ What is context and how does it work in Dialogflow?
✔ How to create and use context in Dialogflow?
✔ What are follow-up intents and how do they help in conversational flow?
✔ How to create and use follow-up intents in Dialogflow?
✔ How to handle complex dialogues with multiple contexts and follow-up intents?
✔ Best practices and tips for managing context and conversational flow in Dialogflow
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🧩 Part 5/10
✔ What is Fulfillment and Why Use It?
✔ How to Enable Fulfillment in Dialogflow
✔ How to Use Dialogflow's Fulfillment Library
✔ What is a Webhook and How Does It Work?
✔ How to Set Up a Webhook for Your Chatbot
✔ How to Use Different APIs with Your Webhook
✔ Examples of Chatbots with Fulfillment and Webhooks
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🧩 Part 4/10
✔ What are Dialogflow Responses?
✔ How to Create Text Responses
✔ How to Add Images to Responses
✔ How to Create Cards and Suggestion Chips
✔ How to Use Rich Messages and Custom Payloads
✔ How to Test and Debug Responses
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🧩 Part 9/15
✔ What are Sorting Algorithms?
✔ Types of Sorting Algorithms
✔ Comparison-based Sorting Algorithms
✔ Non-comparison-based Sorting Algorithms
✔ How to Implement Sorting Algorithms in Java
✔ Selection Sort
✔ Bubble Sort
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🧩 Part 8/15
✔ Graph Representation in Java
✔ Adjacency Matrix
✔ Adjacency List
✔ Graph Traversal Algorithms
✔ Depth-First Search (DFS)
✔ Breadth-First Search (BFS)
✔ Graph Applications and Problems
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🧩 Part 7/15
✔ What are Heaps and Priority Queues?
✔ Heaps
✔ Priority Queues
✔ How to Implement Heaps and Priority Queues in Java?
✔ The Heap Class
✔ The PriorityQueue Class
✔ How to Use Heaps and Priority Queues for Min-Max Data Structures?
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🧩 Part 9/15
✔ Setting Up the Project
✔ Creating Database Models
✔ Configuring Flask-Admin
✔ Customizing the Admin Interface
✔ Adding User Authentication and Authorization
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🧩 Part 8/15
✔ What is Flask-SocketIO and Why Use It?
✔ Setting Up the Environment and Dependencies
✔ Creating a Basic Flask Application with SocketIO
✔ Adding Chat Functionality with SocketIO Events
✔ Adding Notifications with SocketIO Rooms and Broadcasts
✔ Testing and Deploying the Application
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🧩 Part 7/15
✔ What is Flask-RESTful and Why Use It?
✔ Setting Up the Project
✔ Creating the Database Model
✔ Defining the API Resources
✔ Testing the API with Postman
✔ Adding Authentication and Authorization
✔ Handling Errors and Exceptions
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🧩 Part 3/8
✔ What is the isin method and why use it?
✔ How to use the isin method with a single column
✔ How to use the isin method with multiple columns
✔ How to combine the isin method with other filtering methods
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🧩 Part 2/8
✔ What is the Query Method and How to Use It
✔ Basic Syntax and Examples of the Query Method
✔ Using Operators and Logical Conditions with the Query Method
✔ Using Variables and Expressions with the Query Method
✔ Query Method vs Other Filtering Methods in Pandas
✔ Practical Examples of the Query Method with Real-World Datasets
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🧩 Part 1/8
✔ What is Filtering and Why is it Useful?
✔ How to Create Boolean Masks for Filtering
✔ Using Comparison Operators
✔ Using Logical Operators
✔ Using Methods and Attributes
✔ How to Apply Boolean Masks to DataFrames
✔ Filtering Rows
✔ Filtering Columns
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🧩 Part 3/15
✔ Logistic Regression
✔ The Sigmoid Function
✔ The Cost Function
✔ The Gradient Descent Algorithm
✔ TensorFlow Basics
✔ Tensors and Operations
✔ Variables and Placeholders
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🧩 Part 2/15
✔ Linear Regression: Theory and Implementation
✔ What is Linear Regression?
✔ How to Implement Linear Regression with TensorFlow?
✔ Dataset: Boston Housing Prices
✔ Data Preprocessing and Visualization
✔ Model Training and Evaluation
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🧩 Part 1/15
✔ What is Deep Learning?
✔ Why Learn Deep Learning?
✔ Applications of Deep Learning
✔ Challenges of Deep Learning
✔ How to Learn Deep Learning?
✔ Prerequisites for Deep Learning
✔ Resources for Deep Learning
✔ How to Set Up Your Environment for Deep Learning?
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🧩 Part 6/10
✔ What is Testing and Why is it Important?
✔ Types of Testing: Unit Testing vs Integration Testing
✔ Pytest: A Powerful Testing Framework for Python
✔ FastAPI Test Client: A Simple Way to Test Your API Endpoints
✔ Writing and Running Tests with Pytest and FastAPI Test Client
✔ Setting Up the Project and Dependencies
✔ Creating a Simple API with FastAPI
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🧩 Part 5/10
✔ Authentication
✔ Basic Authentication
✔ OAuth2 Authentication
✔ JWT Authentication
✔ Authorization
✔ Role-Based Access Control
✔ Permission-Based Access Control
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🧩 Part 4/10
✔ What are Dependencies in FastAPI?
✔ How to Define and Use Dependencies
✔ How to Share and Reuse Dependencies
✔ How to Handle Errors and Exceptions with Dependencies
✔ What are Background Tasks in FastAPI?
✔ How to Create and Run Background Tasks
✔ How to Access Request Data in Background Tasks
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🧩 Part 5/5
✔ What is the difference between DROP TABLE and TRUNCATE?
✔ Syntax and examples
✔ Effects on constraints, indexes, and sequences
✔ How to delete multiple tables at once
✔ How to undo a DROP TABLE or TRUNCATE operation
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🧩 Part 4/5
✔ What is a JOIN clause?
✔ Syntax of a JOIN clause
✔ Types of joins
✔ INNER JOIN
✔ Example of an INNER JOIN
✔ Benefits and drawbacks of an INNER JOIN
✔ LEFT JOIN
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🧩 Part 10/10
✔ What are Large Language Models and Why Fine-Tune Them?
✔ Future Trends of Fine-Tuning Large Language Models
✔ Scalability: How to Train and Fine-Tune Larger Models Efficiently?
✔ Interpretability: How to Explain and Understand the Model's Behavior?
✔ Generalization: How to Transfer the Model's Knowledge to New Domains and Tasks?
✔ Challenges of Fine-Tuning Large Language Models
✔ Data Quality and Availability: How to Ensure the Model Learns from Reliable and Diverse Sources?
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🧩 Part 6/10
✔ Elasticsearch Machine Learning Overview
✔ Data Frames and Analytics
✔ Anomaly Detection and Forecasting
✔ Accessing Machine Learning Results with APIs
✔ Get Data Frame Analytics Stats API
✔ Get Data Frame Analytics API
✔ Get Anomaly Detection Job Stats API
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🧩 Part 5/10
✔ What is Elasticsearch and why use it for ML?
✔ How to set up Elasticsearch and Kibana for ML
✔ How to create and run anomaly detection jobs
✔ How to create and run data frame analytics jobs
✔ How to monitor and manage ML jobs
✔ How to use ML APIs and integrations
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🧩 Part 4/10
✔ What is Elasticsearch and why use it for data analysis?
✔ How to install and configure Elasticsearch
✔ How to index and search data using Elasticsearch queries
✔ How to perform basic data analysis using Elasticsearch metrics aggregations
✔ How to perform advanced data analysis using Elasticsearch buckets aggregations
✔ How to combine queries and aggregations for complex data analysis
✔ How to visualize and explore data using Kibana
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🧩 Part 6/15
✔ What are Trees?
✔ Terminology and Properties
✔ Types of Trees
✔ How to Implement Trees in Java
✔ Node Class
✔ Tree Class
✔ How to Traverse Trees in Java
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🧩 Part 5/15
✔ What are Hash Tables and Maps?
✔ Hashing Function and Hash Code
✔ Collision Handling and Load Factor
✔ How to Implement Hash Tables and Maps in Java
✔ The HashMap Class
✔ The Hashtable Class
✔ The LinkedHashMap Class
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🧩 Part 4/15
✔ What are Queues and Deques?
✔ Queue: A FIFO Data Structure
✔ Deque: A LIFO Data Structure
✔ How to Implement Queues and Deques in Java?
✔ Using the java.util.Queue Interface
✔ Using the java.util.Deque Interface
✔ How to Enqueue and Dequeue Elements in Java?
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🧩 Part 6/15
✔ What is Kafka Connect?
✔ How to Install and Run Kafka Connect
✔ How to Use Kafka Connect with Python
✔ Kafka Connect Python API
✔ Kafka Connect REST API
✔ How to Configure Kafka Connectors
✔ Source Connectors
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🧩 Part 6/12
✔ Uncertainty in Clustering
✔ Sources and Types of Uncertainty
✔ Methods for Quantifying and Representing Uncertainty
✔ Applications and Challenges of Uncertainty-Aware Clustering
✔ Uncertainty in Dimensionality Reduction
✔ Sources and Types of Uncertainty
✔ Methods for Quantifying and Representing Uncertainty
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🧩 Part 5/12
✔ What is Uncertainty and Why Does it Matter?
✔ Sources of Uncertainty
✔ Types of Uncertainty
✔ Uncertainty in Regression
✔ Challenges of Uncertainty in Regression
✔ Solutions for Uncertainty in Regression
✔ Uncertainty in Classification
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🧩 Part 4/12
✔ What is Calibration and Why is it Important?
✔ Definition and Types of Calibration
✔ Calibration Metrics and Methods
✔ How to Report Confidence Intervals for Predictions?
✔ Definition and Interpretation of Confidence Intervals
✔ Confidence Interval Methods and Examples
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🧩 Part 5/12
✔ Java Lists: ArrayList, LinkedList, and Vector
✔ ArrayList vs LinkedList vs Vector
✔ How to Create, Iterate, and Modify Lists in Java
✔ Java Sets: HashSet, LinkedHashSet, and TreeSet
✔ HashSet vs LinkedHashSet vs TreeSet
✔ How to Create, Iterate, and Modify Sets in Java
✔ Java Maps: HashMap, LinkedHashMap, and TreeMap
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🧩 Part 6/12
✔ The Try-Catch Block
✔ How to Use the Try-Catch Block
✔ How to Handle Multiple Exceptions
✔ How to Handle Nested Try-Catch Blocks
✔ The Throw Statement
✔ How to Throw an Exception
✔ How to Create Custom Exceptions
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🧩 Part 4/12
✔ What is Object-Oriented Programming?
✔ What is Polymorphism in Java?
✔ Types of Polymorphism
✔ Examples of Polymorphism
✔ What is Abstraction in Java?
✔ Abstract Classes and Methods
✔ Examples of Abstraction
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🧩 Part 6/10
✔ Evaluation Metrics for QA Systems
✔ Precision, Recall, and F1-score
✔ BLEU and ROUGE
✔ METEOR and CIDEr
✔ Evaluation Methods
✔ Human Evaluation
✔ Crowdsourcing and Annotation
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🧩 Part 5/10
✔ Answer Extraction Techniques
✔ Named Entity Recognition (NER)
✔ Dependency Parsing
✔ Coreference Resolution
✔ Answer Generation Methods
✔ Template-Based Approaches
✔ Sequence-to-Sequence Models
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🧩 Part 4/10
✔ Understanding Question Types
✔ Fact-Based Questions
✔ Opinion-Based Questions
✔ Complex Questions
✔ Techniques for Question Reformulation
✔ Paraphrasing Strategies
✔ Synonym Replacement
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🧩 Part 5/15
✔ What is Kafka Streams and Why Use It?
✔ How to Install and Set Up Kafka Streams with Python
✔ How to Create and Consume Data Streams with Kafka Streams
✔ How to Apply Operators and Transformations on Data Streams
✔ Filtering and Mapping
✔ Aggregating and Reducing
✔ Joining and Merging
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🧩 Part 4/15
✔ Common Errors and Exceptions in Kafka and Python
✔ Broker Errors
✔ Producer Errors
✔ Consumer Errors
✔ How to Handle Errors and Exceptions in Kafka and Python
✔ Using Try-Except Blocks
✔ Using Error Callbacks
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🧩 Part 3/10
✔ Data Sources for Question Answering
✔ Web Scraping and APIs
✔ Pre-existing QA Datasets
✔ Data Preprocessing Techniques
✔ Text Cleaning and Tokenization
✔ Stop Word Removal and Lemmatization
✔ Building a QA Corpus
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🧩 Part 2/10
✔ Types of Questions
✔ Closed-Ended Questions
✔ Open-Ended Questions
✔ Hypothetical Questions
✔ Types of Answers
✔ Direct Answers
✔ Indirect Answers
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🧩 Part 1/10
✔ What Is Question Answering?
✔ The Importance of Question Answering in NLP
✔ How Does Question Answering Work?
✔ Common Challenges in Question Answering
✔ Types of Question Answering Models
✔ Retrieval-Based QA
✔ Generative QA
✔ Evaluation Metrics for QA Systems
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🧩 Part 3/10
✔ What is FastAPI?
✔ What is Data Validation?
✔ How to Validate Data with FastAPI
✔ Define a Pydantic Model
✔ Declare a Path Operation with a Model
✔ Test the Validation
✔ What is OpenAPI Schema?
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🧩 Part 2/10
✔ What are Path Parameters?
✔ How to Define Path Parameters in FastAPI
✔ How to Validate Path Parameters with Pydantic Models
✔ What are Query Parameters?
✔ How to Define Query Parameters in FastAPI
✔ How to Validate Query Parameters with Pydantic Models
✔ What are Request Body Parameters?
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🧩 Part 1/10
✔ Setting up the Environment
✔ Installing Python and Pip
✔ Installing FastAPI and Uvicorn
✔ Creating a Hello World Endpoint
✔ Writing the Code
✔ Running the Server
✔ Testing the Endpoint
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🧩 Part 6/8
✔ Transformer Architecture and BERT
✔ Transformer Encoder and Decoder
✔ BERT and Masked Language Modeling
✔ XLNet and Permutation Language Modeling
✔ Permutation Language Modeling and Autoencoding
✔ Two-Stream Attention and Autoregressive Modeling
✔ Comparison and Applications of XLNet and BERT
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🧩 Part 5/8
✔ Transformer-XL Architecture
✔ Recurrence Mechanism
✔ Relative Positional Encoding
✔ Transformer-XL Applications
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🧩 Part 4/8
✔ What is Text-to-Text Transfer Learning?
✔ What is T5 and How Does It Work?
✔ The T5 Model Architecture
✔ The T5 Pre-training and Fine-tuning Objectives
✔ How to Use T5 for Various NLP Tasks?
✔ Text Summarization
✔ Text Generation
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🧩 Part 6/12
✔ What are Mean Absolute Error and Mean Absolute Percentage Error?
✔ Mean Absolute Error (MAE)
✔ Mean Absolute Percentage Error (MAPE)
✔ How to Calculate Mean Absolute Error and Mean Absolute Percentage Error in Python?
✔ How to Interpret Mean Absolute Error and Mean Absolute Percentage Error?
✔ Advantages and Disadvantages of MAE and MAPE
✔ How to Compare MAE and MAPE with Other Metrics?
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🧩 Part 5/12
✔ What is Mean Squared Error?
✔ How to Calculate Mean Squared Error
✔ How to Interpret Mean Squared Error
✔ What is R-Squared?
✔ How to Calculate R-Squared
✔ How to Interpret R-Squared
✔ How to Compare Mean Squared Error and R-Squared
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🧩 Part 4/12
✔ What is ROC Curve and AUC?
✔ ROC Curve for Binary Classification
✔ AUC for Binary Classification
✔ ROC Curve and AUC for Multi-class Classification
✔ How to Plot ROC Curve and AUC in Python?
✔ Load and Prepare the Data
✔ Train and Test Different Classifiers
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🧩 Part 3/12
✔ What is Uncertainty and Why is it Important?
✔ Types of Uncertainty
✔ Sources of Uncertainty
✔ Uncertainty Metrics
✔ Confidence Intervals
✔ Entropy and Information Gain
✔ Bayesian Model Evidence
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🧩 Part 2/12
✔ What is Uncertainty in Machine Learning?
✔ Aleatoric Uncertainty
✔ Epistemic Uncertainty
✔ How to Measure and Quantify Uncertainty?
✔ How to Reduce and Mitigate Uncertainty?
✔ Applications and Benefits of Uncertainty Estimation
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🧩 Part 1/12
✔ What is Uncertainty in Machine Learning?
✔ Why is Uncertainty Important for Machine Learning?
✔ Uncertainty for Model Evaluation
✔ Uncertainty for Decision Making
✔ How to Quantify and Estimate Uncertainty in Machine Learning?
✔ Uncertainty in Supervised Learning
✔ Uncertainty in Unsupervised Learning
✔ Uncertainty in Reinforcement Learning
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🧩 Part 6/10
✔ What are Transactions and Why are They Important?
✔ Factors Affecting Transaction Performance and Scalability
✔ Transaction Size
✔ Transaction Isolation Level
✔ Transaction Concurrency
✔ Transaction Locking and Deadlocks
✔ How to Optimize Transaction Performance and Scalability
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