~ Tutorials, reflections, and everything in between ~
~ Tutorials, reflections, and everything in between ~
🧩 Part 8/12
✔ Computer Vision Basics
✔ Image Processing and Analysis
✔ Image Recognition and Classification
✔ Object Detection and Localization
✔ Face Recognition and Verification
✔ Financial Applications of Computer Vision
✔ Document Analysis and Extraction
...
🧩 Part 7/12
✔ Natural Language Processing for Text Analysis
✔ Tokenization and Vectorization
✔ Topic Modeling and Clustering
✔ Named Entity Recognition and Relation Extraction
✔ Natural Language Processing for Text Generation
✔ Language Models and Transformers
✔ Text Summarization and Paraphrasing
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🧩 Part 6/8
✔ What is Deep Learning and Why Use Matlab?
✔ Neural Networks in Matlab
✔ Creating and Training a Neural Network
✔ Evaluating and Testing a Neural Network
✔ Convolutional Neural Networks in Matlab
✔ Creating and Training a Convolutional Neural Network
✔ Evaluating and Testing a Convolutional Neural Network
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🧩 Part 5/8
✔ What is Unsupervised Learning?
✔ Types of Unsupervised Learning
✔ Applications of Unsupervised Learning
✔ How to Perform Unsupervised Learning in Matlab
✔ Data Preparation and Visualization
✔ Clustering Algorithms
✔ Dimensionality Reduction Algorithms
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🧩 Part 4/8
✔ Matlab Basics for Machine Learning
✔ Data Import and Preprocessing
✔ Visualization and Exploration
✔ Regression Models
✔ Linear Regression
✔ Nonlinear Regression
✔ Regularization and Validation
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🧩 Part 9/12
✔ What is Anomaly Detection?
✔ Types of Anomalies
✔ Applications of Anomaly Detection
✔ Anomaly Detection Models
✔ Statistical Methods
✔ Machine Learning Methods
✔ Deep Learning Methods
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🧩 Part 8/12
✔ What is Predictive Maintenance?
✔ What are Clustering Models?
✔ K-Means Clustering
✔ Hierarchical Clustering
✔ Density-Based Clustering
✔ How to Use Clustering Models for Health State Identification?
✔ Data Preprocessing
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🧩 Part 7/12
✔ What is Predictive Maintenance?
✔ What are Classification Models?
✔ Logistic Regression
✔ Decision Trees
✔ Support Vector Machines
✔ Neural Networks
✔ How to Apply Classification Models to Predictive Maintenance?
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🧩 Part 9/10
✔ Exploratory Data Analysis (EDA)
✔ Understanding the Credit Card Fraud Dataset
✔ Data Preprocessing
✔ Feature Engineering
✔ Creating New Features
✔ Handling Imbalanced Classes
✔ Model Selection
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🧩 Part 8/10
✔ Model Deployment with Flask
✔ Building a Flask API
✔ Testing the Flask API
✔ Model Monitoring with Streamlit
✔ Creating a Streamlit Dashboard
✔ Visualizing Model Performance
✔ Model Deployment and Monitoring with Azure ML
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🧩 Part 7/10
✔ Fraud Detection Problem and Data
✔ Machine Learning Models for Fraud Detection
✔ Logistic Regression
✔ Decision Tree
✔ Random Forest
✔ Support Vector Machine
✔ Neural Network
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🧩 Part 12/12
✔ Building a NLP Model with PyTorch
✔ Loading and Preprocessing the Data
✔ Defining the Model Architecture
✔ Training and Evaluating the Model
✔ Creating a Web App with Flask
✔ Setting Up the Flask Environment
✔ Designing the Web Interface
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🧩 Part 11/12
✔ What is Text Summarization?
✔ Extractive Summarization
✔ Abstractive Summarization
✔ What is BART?
✔ How to Load BART with HuggingFace
✔ How to Fine-Tune BART for Text Summarization
✔ How to Evaluate BART for Text Summarization
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🧩 Part 10/12
✔ What is Question Answering?
✔ What is BERT and How Does It Work?
✔ How to Use HuggingFace Transformers Library for Question Answering
✔ How to Fine-Tune BERT on SQuAD Dataset
✔ How to Evaluate BERT on Question Answering Tasks
✔ How to Deploy BERT for Question Answering on Web Applications
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🧩 Part 10/10
✔ Transaction Management Concepts
✔ ACID Properties
✔ Transaction States and Operations
✔ Concurrency Control and Locking
✔ Recovery and Logging
✔ Transaction Management Design Principles
✔ Define Transaction Boundaries and Granularity
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🧩 Part 10/10
✔ What is Elasticsearch for ML?
✔ How does it work?
✔ What are the benefits?
✔ Image Recognition: A Challenging Problem
✔ How to Use Elasticsearch for ML for Image Recognition
✔ Preparing the Image Data
✔ Creating an Index and a Pipeline
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🧩 Part 10/10
✔ What is Dialogflow and Why Use It for Chatbots?
✔ Dialogflow Features and Benefits
✔ Dialogflow Concepts and Terminology
✔ How to Deploy Your Chatbot Using Dialogflow
✔ Setting Up Your Dialogflow Agent and Intents
✔ Integrating Your Chatbot with Different Platforms
✔ Testing and Debugging Your Chatbot
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🧩 Part 6/10
✔ What are Robust Neural Networks?
✔ Definition and Motivation
✔ Challenges and Solutions
✔ How to Use Deep Learning for Complex Data?
✔ Convolutional Neural Networks
✔ Recurrent Neural Networks
✔ How to Prevent Overfitting and Improve Generalization?
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🧩 Part 5/10
✔ What are Robust Tree-Based Models?
✔ Decision Trees
✔ Advantages and Disadvantages of Decision Trees
✔ What are Ensemble Methods?
✔ Bagging
✔ Random Forest
✔ Boosting
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🧩 Part 4/10
✔ What are Robust Linear Models?
✔ How to Perform Feature Selection?
✔ Filter Methods
✔ Wrapper Methods
✔ Embedded Methods
✔ Lasso, Ridge, and Elastic Net Regression
✔ Lasso Regression
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🧩 Part 9/12
✔ Background
✔ Reinforcement Learning
✔ Active Learning
✔ Sequential Decision Making
✔ Methodology
✔ Problem Formulation
✔ Active Learning for Reinforcement Learning
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🧩 Part 8/12
✔ What is Anomaly Detection and Why is it Important?
✔ What is Active Learning and How Does it Work?
✔ A Case Study: Outlier Detection on a Credit Card Fraud Dataset
✔ Data Exploration and Preprocessing
✔ Applying Outlier Detection Methods: Isolation Forest and Local Outlier Factor
✔ Evaluating the Results and Selecting the Most Informative Samples
✔ Iterating the Process and Improving the Model Performance
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🧩 Part 7/12
✔ Active Learning for Clustering
✔ What is Active Learning?
✔ Why Use Active Learning for Clustering?
✔ How to Implement Active Learning for Clustering?
✔ Case Study: Clustering Customer Segments
✔ Data Description and Visualization
✔ K-means Clustering
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🧩 Part 9/10
✔ What are Component-Based Events?
✔ The Concept of Components in React
✔ The Concept of Events in React
✔ How to Create Components in React
✔ Using JSX Syntax
✔ Using Functional Components
✔ Using Class Components
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🧩 Part 8/10
✔ What are Client-Side Events and Why are They Important?
✔ How to Select Elements using jQuery
✔ How to Manipulate the DOM using jQuery
✔ How to Create and Handle Custom Events using jQuery
✔ How to Use AJAX with jQuery
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🧩 Part 7/10
✔ What are Server-Side Events and Why Use Them?
✔ The Benefits of Server-Side Events
✔ The Challenges of Server-Side Events
✔ How Node.js Supports Server-Side Event-Driven Programming
✔ The Node.js Event Loop and Event Emitter
✔ How to Create and Listen to Custom Events in Node.js
✔ How to Use Streams and Sockets for Server-Side Events in Node.js
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🧩 Part 6/8
✔ What is F1 Score and Why is it Important?
✔ What is Hyperparameter Tuning and How Does it Work?
✔ Grid Search vs Random Search: Pros and Cons
✔ How to Implement Grid Search and Random Search in Python
✔ How to Evaluate and Compare the Results of Grid Search and Random Search
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🧩 Part 5/8
✔ What is F1 Score and Why is it Important?
✔ Sampling Techniques for Imbalanced Data
✔ Oversampling Methods
✔ Undersampling Methods
✔ How to Apply SMOTE and NearMiss in Python
✔ How to Evaluate F1 Score and Compare Different Models
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🧩 Part 4/8
✔ What is Class Imbalance and Why is it a Problem?
✔ What is F1 Score and Why is it a Good Metric for Imbalanced Data?
✔ How to Use Class Weights to Adjust the Loss Function
✔ How to Implement Class Weights in Sklearn
✔ How to Evaluate the Performance of the Model with F1 Score
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🧩 Part 6/8
✔ Golang and Gorgonia: A Brief Overview
✔ What is Golang and Why Use It for Machine Learning?
✔ What is Gorgonia and How Does It Work?
✔ Natural Language Processing with Golang and Gorgonia
✔ Preprocessing Text Data in Golang
✔ Creating and Training Word Embeddings with Gorgonia
✔ Building and Evaluating a Sentiment Analysis Model with Gorgonia
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🧩 Part 5/8
✔ Golang and Gorgonia: A Brief Overview
✔ Neural Networks: Concepts and Applications
✔ What are Neural Networks?
✔ How do Neural Networks Learn?
✔ Types of Neural Networks and Their Uses
✔ Deep Learning: The Next Level of Neural Networks
✔ What is Deep Learning?
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🧩 Part 4/8
✔ Golang and Gonum: A Brief Overview
✔ What is Golang?
✔ What is Gonum?
✔ Linear Regression with Golang and Gonum
✔ The Linear Regression Model
✔ Data Preparation and Visualization
✔ Model Fitting and Evaluation
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🧩 Part 6/8
✔ What is Pruning and Why is it Important?
✔ Challenges of Pruning Neural Networks
✔ Pruning Methods for Neural Networks
✔ Weight Pruning
✔ Unit Pruning
✔ Structured Pruning
✔ Lottery Ticket Hypothesis
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🧩 Part 5/8
✔ Decision Trees and Overfitting
✔ What are Decision Trees?
✔ How to Measure Overfitting?
✔ Reduced Error Pruning
✔ How does Reduced Error Pruning Work?
✔ Advantages and Disadvantages of Reduced Error Pruning
✔ Example of Reduced Error Pruning
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🧩 Part 4/8
✔ Decision Trees and Error Rate
✔ How to Build a Decision Tree
✔ How to Measure the Error Rate of a Decision Tree
✔ Minimum Error Pruning
✔ What is Minimum Error Pruning
✔ How to Apply Minimum Error Pruning to a Decision Tree
✔ Example of Minimum Error Pruning
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🧩 Part 6/10
✔ What is Asynchronous Programming?
✔ Synchronous vs Asynchronous
✔ Benefits and Challenges of Asynchronous Programming
✔ How Asyncio Works in Python
✔ The Event Loop
✔ Coroutines
✔ Futures and Tasks
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🧩 Part 5/10
✔ What is Flask and Why Use It?
✔ Setting Up Flask and Creating a Basic Web Application
✔ Handling Web Events with Routes and Views
✔ Using Templates to Render Dynamic Web Pages
✔ Working with Forms and User Input
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🧩 Part 4/10
✔ Installing and Importing PyGame
✔ Creating a Game Window and a Game Loop
✔ Handling Game Events with PyGame
✔ Drawing and Moving Sprites with PyGame
✔ Detecting Collisions and Adding Sound Effects with PyGame
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🧩 Part 6/8
✔ The apply method
✔ Applying a function to each row or column of a dataframe
✔ Applying a function to each element of a dataframe
✔ The map method
✔ Mapping a dictionary or a function to a series
✔ Mapping a series to another series
✔ The lambda function
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🧩 Part 5/8
✔ Filtering dataframes
✔ Filtering by column values
✔ Filtering by multiple conditions
✔ Filtering by string methods
✔ Filtering by query method
✔ Sorting dataframes
✔ Sorting by column values
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🧩 Part 4/8
✔ Basic indexing and slicing
✔ Label-based indexing with loc
✔ Position-based indexing with iloc
✔ Boolean indexing with conditions
✔ Summary and exercises
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🧩 Part 9/12
✔ What is GPT-2 and why is it important for NLP?
✔ How to install and import PyTorch and HuggingFace transformers
✔ How to load and use a pre-trained GPT-2 model
✔ How to fine-tune GPT-2 on your own text data
✔ How to generate text with GPT-2 using different decoding strategies
✔ How to evaluate the quality and diversity of the generated text
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🧩 Part 8/12
✔ What are transformers and why are they important for NLP?
✔ The transformer architecture
✔ The attention mechanism
✔ What is BERT and how does it work?
✔ The BERT pre-training process
✔ The BERT fine-tuning process
✔ How to use PyTorch and HuggingFace to implement transformers and BERT
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🧩 Part 7/12
✔ Sequence to Sequence Models
✔ Encoder-Decoder Architecture
✔ Teacher Forcing and Scheduled Sampling
✔ Attention Mechanisms
✔ Dot-Product Attention
✔ Multi-Head Attention
✔ Machine Translation with PyTorch
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🧩 Part 6/12
✔ What is Predictive Maintenance?
✔ What is Remaining Useful Life?
✔ How to Use Regression Models for RUL Prediction?
✔ Linear Regression
✔ Polynomial Regression
✔ Support Vector Regression
✔ Random Forest Regression
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🧩 Part 5/12
✔ Predictive Maintenance: Concepts and Challenges
✔ What is Predictive Maintenance?
✔ Why is Predictive Maintenance Important?
✔ What are the Challenges of Predictive Maintenance?
✔ Machine Learning Models for Predictive Maintenance
✔ Supervised Learning Models
✔ Unsupervised Learning Models
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🧩 Part 4/12
✔ What is Predictive Maintenance and Why is it Important?
✔ The Challenges of Feature Engineering for Predictive Maintenance
✔ Data Quality and Availability
✔ Domain Knowledge and Expertise
✔ Computational Complexity and Scalability
✔ Feature Engineering Techniques for Predictive Maintenance
✔ Data Preprocessing and Transformation
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🧩 Part 3/10
✔ What are Robust Loss Functions?
✔ Huber Loss
✔ Log-Cosh Loss
✔ What is Regularization?
✔ L1 and L2 Regularization
✔ Dropout and Batch Normalization
✔ How to Apply Robust Loss Functions and Regularization in TensorFlow
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🧩 Part 2/10
✔ Data Cleaning
✔ Handling Missing Values
✔ Handling Outliers
✔ Handling Duplicates
✔ Data Transformation
✔ Scaling and Normalization
✔ Encoding Categorical Variables
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🧩 Part 1/10
✔ What is Robust Machine Learning?
✔ Why is Robust Machine Learning Important?
✔ Applications of Robust Machine Learning
✔ Benefits of Robust Machine Learning
✔ What are the Challenges of Robust Machine Learning?
✔ Data Quality and Diversity
✔ Model Uncertainty and Bias
✔ Adversarial Attacks and Defenses
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🧩 Part 6/12
✔ Active Learning for Regression
✔ What is Active Learning?
✔ Why Active Learning for Regression?
✔ How to Implement Active Learning for Regression?
✔ Case Study: Predicting House Prices
✔ Data Description and Preprocessing
✔ Linear Regression Model
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🧩 Part 5/12
✔ Active Learning: What, Why, and How
✔ What is Active Learning?
✔ Why Use Active Learning for Image Classification?
✔ How to Implement Active Learning?
✔ Image Classification: A Computer Vision Task
✔ What is Image Classification?
✔ How to Preprocess Images for Classification?
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🧩 Part 4/12
✔ Active Learning for Text Classification
✔ What is Active Learning?
✔ Why Use Active Learning for Text Classification?
✔ How to Implement Active Learning for Text Classification?
✔ A Case Study: Sentiment Analysis of Movie Reviews
✔ Data Collection and Preprocessing
✔ Feature Extraction using TF-IDF
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🧩 Part 6/10
✔ Model Testing
✔ Accuracy Metrics
✔ Latency Evaluation
✔ Power Consumption Analysis
✔ Model Evaluation
✔ Trade-offs and Considerations
✔ Benchmarking
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🧩 Part 5/10
✔ Data Collection
✔ Collecting Data from Embedded Devices
✔ Simulated Data Generation
✔ Data Preprocessing
✔ Data Augmentation Techniques
✔ Data Labeling
✔ Model Training
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🧩 Part 4/10
✔ Model Design Techniques
✔ Quantization
✔ Pruning
✔ Distillation
✔ Optimization
✔ Choosing the Right Hardware
✔ Understanding Constraints
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🧩 Part 6/8
✔ What are Dashboards in Graylog?
✔ How to Create a Dashboard
✔ Adding Widgets to a Dashboard
✔ Configuring Widget Settings
✔ How to Manage Dashboards
✔ Editing and Deleting Dashboards
✔ Sharing and Exporting Dashboards
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🧩 Part 5/8
✔ Searching Logs in Graylog
✔ Using the Search Bar
✔ Applying Filters and Operators
✔ Saving and Sharing Searches
✔ Analyzing Logs in Graylog
✔ Creating Dashboards
✔ Adding Widgets and Visualizations
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🧩 Part 4/8
✔ What are Streams in Graylog?
✔ How to Create a Stream in Graylog
✔ Define the Stream Name and Description
✔ Configure the Stream Rules
✔ Assign the Stream to an Index Set
✔ How to Manage Streams in Graylog
✔ Edit or Delete a Stream
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🧩 Part 6/10
✔ Quantum Machine Learning Basics
✔ Quantum Computing Concepts
✔ Quantum Machine Learning Algorithms
✔ Quantum Machine Learning Applications
✔ Quantum Chemistry
✔ Quantum Cryptography
✔ Quantum Error Correction
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🧩 Part 5/10
✔ Quantum Machine Learning Frameworks
✔ Qiskit
✔ PennyLane
✔ TensorFlow Quantum
✔ Quantum Machine Learning Libraries
✔ QMLT
✔ Qiskit Machine Learning
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🧩 Part 4/10
✔ Quantum Computing Basics
✔ Qubits and Quantum Gates
✔ Quantum Circuits and Algorithms
✔ Quantum Hardware and Software
✔ Quantum Machine Learning Concepts
✔ Quantum Data and Feature Maps
✔ Quantum Kernels and Distance Measures
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🧩 Part 3/12
✔ What are Activation Functions and Optimizers?
✔ Activation Functions
✔ Optimizers
✔ How to Choose the Right Activation Function and Optimizer?
✔ How to Implement Different Activation Functions and Optimizers in Keras and TensorFlow?
✔ How to Evaluate the Performance of Different Activation Functions and Optimizers?
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🧩 Part 2/12
✔ What is a Neural Network?
✔ Basic Concepts and Terminology
✔ Types of Neural Networks
✔ How to Install Keras and TensorFlow
✔ How to Create a Simple Neural Network with Keras
✔ Define the Model Architecture
✔ Compile the Model
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🧩 Part 1/12
✔ What is Keras and why use it?
✔ What is TensorFlow and how does it work?
✔ The core concepts of TensorFlow
✔ The benefits and challenges of TensorFlow
✔ How to install Keras and TensorFlow on your machine?
✔ Installing Keras and TensorFlow on Windows
✔ Installing Keras and TensorFlow on Linux
✔ Installing Keras and TensorFlow on Mac OS
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🧩 Part 3/8
✔ What are Features and Why are They Important?
✔ Feature Extraction Methods in Matlab
✔ Principal Component Analysis (PCA)
✔ Linear Discriminant Analysis (LDA)
✔ Independent Component Analysis (ICA)
✔ Feature Selection Methods in Matlab
✔ Filter Methods
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🧩 Part 2/8
✔ Data Import and Exploration
✔ Importing Data from Different Sources
✔ Exploring Data with Statistics and Plots
✔ Data Cleaning and Transformation
✔ Handling Missing Values and Outliers
✔ Scaling, Normalizing, and Encoding Data
✔ Data Visualization and Analysis
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🧩 Part 1/8
✔ What is Matlab and Why Use it for Machine Learning?
✔ How to Install and Set Up Matlab on Your Computer
✔ How to Use Matlab for Data Preprocessing and Visualization
✔ How to Use Matlab for Supervised Learning Algorithms
✔ Linear Regression
✔ Logistic Regression
✔ Support Vector Machines
✔ Decision Trees and Random Forests
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🧩 Part 3/8
✔ What are Gaussian processes?
✔ Definition and properties
✔ Prior and posterior distributions
✔ How to choose kernel functions?
✔ Common kernels and their characteristics
✔ Hyperparameter optimization and model selection
✔ How to perform Gaussian process regression?
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🧩 Part 2/8
✔ What is Uncertainty and Why is it Important?
✔ Types of Uncertainty: Aleatoric and Epistemic
✔ Challenges of Modeling Uncertainty in Deep Learning
✔ What are Bayesian Neural Networks?
✔ Bayesian Inference and Bayes' Theorem
✔ Prior, Posterior, and Likelihood Distributions
✔ Advantages and Limitations of Bayesian Neural Networks
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🧩 Part 1/8
✔ What is Bayesian inference and why is it important?
✔ How to apply Bayes' theorem to update your beliefs
✔ The components of Bayes' theorem: prior, likelihood, and posterior
✔ The Bayes' rule formula and how to use it
✔ Examples of Bayesian inference in deep learning
✔ Bayesian neural networks and how they differ from standard neural networks
✔ Variational inference and how it approximates the posterior distribution
✔ Challenges and limitations of Bayesian inference in deep learning
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🧩 Part 3/8
✔ What is F1 Score and Why is it Important?
✔ How to Calculate F1 Score for Binary Classification
✔ How to Plot ROC Curve and Precision-Recall Curve
✔ How to Find the Optimal Threshold Using F1 Score
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🧩 Part 2/8
✔ What is F1 Score and Why is it Important?
✔ Precision and Recall
✔ Harmonic Mean and F1 Score
✔ How to Calculate F1 Score in Python using sklearn.metrics
✔ Binary Classification Example
✔ Multiclass Classification Example
✔ How to Interpret F1 Score and Improve Model Performance
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🧩 Part 1/8
✔ What is F1 score and why is it useful?
✔ How to calculate F1 score from precision and recall
✔ What are precision and recall and how to interpret them?
✔ How to compute F1 score from a confusion matrix?
✔ How to use F1 score to evaluate and compare classification models
✔ How to choose the best threshold for a binary classifier?
✔ How to handle imbalanced classes and multi-class problems?
✔ How to improve F1 score and model performance
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🧩 Part 3/10
✔ Quantum Computing Basics
✔ Qubits and Quantum Gates
✔ Quantum Circuits and Algorithms
✔ Quantum Linear Algebra
✔ Quantum Matrix Multiplication
✔ Quantum Matrix Inversion
✔ Quantum Linear Systems
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🧩 Part 2/10
✔ Quantum Bits and States
✔ Qubit Representation
✔ Qubit Operations
✔ Quantum Gates and Circuits
✔ Single-Qubit Gates
✔ Multi-Qubit Gates
✔ Quantum Circuit Design
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🧩 Part 1/10
✔ What is Quantum Computing?
✔ What is Machine Learning?
✔ What is Quantum Machine Learning?
✔ Quantum Algorithms for Machine Learning
✔ Quantum Hardware for Machine Learning
✔ What is Quantum Advantage?
✔ What are the Applications of Quantum Machine Learning?
✔ Quantum Chemistry
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🧩 Part 3/8
✔ Data Preprocessing with Gota
✔ Loading and Exploring Data
✔ Cleaning and Transforming Data
✔ Data Visualization with Gonum/plot
✔ Creating and Customizing Plots
✔ Plotting Different Types of Charts
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🧩 Part 2/8
✔ Why Golang for Machine Learning?
✔ Golang Data Structures for Machine Learning
✔ Slices
✔ Maps
✔ Structs and Interfaces
✔ Golang Algorithms for Machine Learning
✔ Sort
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🧩 Part 1/8
✔ What is Golang and why use it for machine learning?
✔ How to install Golang on your system
✔ Windows installation
✔ Linux installation
✔ Mac installation
✔ How to set up your Golang environment for machine learning
✔ Installing and using Go modules
✔ Installing and using Go tools
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🧩 Part 3/8
✔ Why IntelliJ IDEA?
✔ Smart Code Completion
✔ Advanced Refactoring
✔ Seamless Integration
✔ How to Install and Set Up IntelliJ IDEA
✔ How to Use IntelliJ IDEA
✔ Creating a Project
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🧩 Part 2/8
✔ What is Eclipse IDE?
✔ How to Install and Set Up Eclipse IDE
✔ How to Create and Manage a Java Project in Eclipse IDE
✔ How to Use Eclipse Plugins to Enhance Your Java Development
✔ How to Debug and Test Your Java Code in Eclipse IDE
✔ How to Customize and Optimize Eclipse IDE for Your Preferences
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🧩 Part 1/8
✔ What is an IDE and Why Do You Need One?
✔ The Benefits of Using a Java IDE
✔ Code Completion and Syntax Highlighting
✔ Debugging and Testing Tools
✔ Refactoring and Code Analysis
✔ The Criteria for Choosing a Java IDE
✔ Compatibility and Performance
✔ Customizability and Extensibility
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🧩 Part 3/8
✔ Prerequisites
✔ Configuring log4j2 to send logs to Graylog
✔ Adding log4j2 and GELF dependencies
✔ Creating a log4j2 configuration file
✔ Writing log messages in your Java code
✔ Verifying that logs are sent to Graylog
✔ Starting Graylog and creating a GELF input
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🧩 Part 2/8
✔ Prerequisites
✔ Installing Graylog Server
✔ Configuring Graylog Server
✔ Installing Graylog Web Interface
✔ Configuring Graylog Web Interface
✔ Testing Graylog Functionality
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🧩 Part 1/8
✔ What is Graylog and why use it?
✔ How to install and configure Graylog
✔ Prerequisites and system requirements
✔ Installing Graylog server and web interface
✔ Configuring Graylog inputs and outputs
✔ How to integrate Graylog with Java applications
✔ Using log4j2 appender for Graylog
✔ Using GELF library for Graylog
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🧩 Part 3/8
✔ What is Carbon and why use it?
✔ Installing and setting up the Carbon IDE
✔ Writing your first Carbon program
✔ Creating a new project
✔ Writing the code
✔ Running and debugging the code
✔ Exploring the features of the Carbon IDE
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🧩 Part 2/8
✔ What is Carbon Programming and why use it?
✔ How to download Carbon Programming for your operating system
✔ For Windows users
✔ For Mac users
✔ For Linux users
✔ How to install Carbon Programming on your computer
✔ How to set up Carbon Programming environment and tools
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🧩 Part 1/8
✔ What is Carbon Programming?
✔ What are the features of Carbon Programming?
✔ High-performance and low-overhead
✔ Cross-platform and interoperable
✔ Expressive and concise
✔ What are the benefits of learning Carbon Programming?
✔ Enhance your skills and knowledge
✔ Expand your career opportunities
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🧩 Part 3/8
✔ Accessing dataframe rows
✔ Using loc and iloc
✔ Using boolean indexing
✔ Updating dataframe rows
✔ Using assignment operator
✔ Using update method
✔ Appending dataframe rows
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🧩 Part 2/8
✔ Accessing dataframe columns
✔ Renaming dataframe columns
✔ Adding dataframe columns
✔ Deleting dataframe columns
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🧩 Part 1/8
✔ What is pandas?
✔ How to install pandas
✔ How to create a dataframe from a list of dictionaries
✔ Define a list of dictionaries
✔ Convert the list to a dataframe
✔ View and inspect the dataframe
✔ How to access and manipulate dataframe elements
✔ Select columns and rows
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🧩 Part 3/10
✔ What are Data Sources and Sinks?
✔ What are Azure Data Factory Connectors?
✔ How to Connect to Azure Blob Storage as a Data Source or Sink
✔ How to Connect to Azure SQL Database as a Data Source or Sink
✔ How to Copy Data between Different Data Sources and Sinks
✔ How to Monitor and Troubleshoot Data Integration Pipelines
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🧩 Part 2/10
✔ What is a Data Pipeline?
✔ Azure Data Factory Overview
✔ Creating a Data Pipeline using Azure Data Factory UI
✔ Creating a Data Pipeline using Azure Data Factory SDK
✔ Creating a Data Pipeline using Azure Resource Manager Template
✔ Configuring and Monitoring a Data Pipeline
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🧩 Part 1/10
✔ What is Azure Data Factory?
✔ Why use Azure Data Factory?
✔ Benefits of Azure Data Factory
✔ Use cases of Azure Data Factory
✔ How does Azure Data Factory work?
✔ Data Factory components
✔ Data Factory architecture
✔ How to create and manage Azure Data Factory?
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🧩 Part 6/10
✔ Why integrate your chatbot with external APIs and services?
✔ How to integrate your chatbot with external APIs and services?
✔ Using databases to store and retrieve chatbot data
✔ Using webhooks to send and receive chatbot events
✔ Using cloud functions to execute chatbot logic
✔ Examples of chatbot integration with external APIs and services
✔ Booking a flight with a chatbot and Skyscanner API
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🧩 Part 5/10
✔ Configuring the NLU Pipeline and the Policies
✔ Training the Chatbot with Rasa CLI and Rasa X
✔ Training with Rasa CLI
✔ Training with Rasa X
✔ Testing the Chatbot with Different Evaluation Methods and Metrics
✔ Testing with Rasa Test
✔ Testing with Rasa X
✔ Testing with Rasa Interactive
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🧩 Part 4/10
✔ What is Rasa and why use it for chatbot development?
✔ How Rasa works: Rasa NLU, Rasa Core, and Rasa X
✔ How to install and set up Rasa on your machine
✔ How to create a simple chatbot with Rasa: a step-by-step guide
✔ Define your chatbot's domain and intents
✔ Write your chatbot's stories and rules
✔ Train your chatbot's model
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🧩 Part 6/10
✔ What is Fraud Detection and Why is it Important?
✔ How Machine Learning Can Help with Fraud Detection
✔ Deep Learning Models for Fraud Detection
✔ Autoencoders
✔ Long Short-Term Memory (LSTM)
✔ Generative Adversarial Networks (GAN)
✔ How to Train and Evaluate Deep Learning Models for Fraud Detection
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🧩 Part 5/10
✔ What is Unsupervised Learning and Why is it Useful for Fraud Detection?
✔ Unsupervised Learning vs Supervised Learning
✔ Challenges and Benefits of Unsupervised Learning for Fraud Detection
✔ How to Prepare Data for Unsupervised Learning Models
✔ Data Cleaning and Preprocessing
✔ Feature Engineering and Selection
✔ Data Scaling and Normalization
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