Advanced Machine Learning with Python and TensorFlow

          Module 1: Introduction to Machine Learning and Python Fundamentals

Section 1: What is Machine Learning?  
- Lesson 1: Definition and Concepts  
  - Module 1, Section 1, Lesson 1 Definition and Concepts  
- Lesson 2: History and Everyday Examples  
  - Module 1, Section 1, Lesson 2 History and Everyday Examples  

Section 2: Python Basics for Machine Learning  
- Lesson 1: Python Syntax and Data Types  
  - Module 1, Section 2, Lesson 1 Python Syntax and Data Types  
- Lesson 2: Variables, Operators, and Control Flow  
  - Module 1, Section 2, Lesson 2 Variables, Operators, and Control Flow  

Section 3: Setting Up Your Python Environment  
- Lesson 1: Installing Python and Essential Tools  
  - Module 1, Section 3, Lesson 1 Installing Python and Essential Tools  
- Lesson 2: Using IDEs and Notebooks for Coding  
  - Module 1, Section 3, Lesson 2 Using IDEs and Notebooks for Coding  

Section 4: Introduction to Essential Python Libraries  
- Lesson 1: Overview of NumPy and Pandas  
  - Module 1, Section 4, Lesson 1 Overview of NumPy and Pandas  
- Lesson 2: Basic Data Manipulation with Pandas  
  - Module 1, Section 4, Lesson 2 Basic Data Manipulation with Pandas  

Section 5: Data Visualization Basics  
- Lesson 1: Introduction to Matplotlib  
  - Module 1, Section 5, Lesson 1 Introduction to Matplotlib  
- Lesson 2: Creating and Interpreting Basic Plots  
  - Module 1, Section 5, Lesson 2 Creating and Interpreting Basic Plots  


Module 2: Data Handling and Preprocessing

Section 1: Data Collection and Sources  
- Lesson 1: Types of Data and Sources  
  - Module 2, Section 1, Lesson 1 Types of Data and Sources  
- Lesson 2: Methods for Collecting Data  
  - Module 2, Section 1, Lesson 2 Methods for Collecting Data  

Section 2: Data Cleaning Essentials  
- Lesson 1: Handling Missing Values and Outliers  
  - Module 2, Section 2, Lesson 1 Handling Missing Values and Outliers  
- Lesson 2: Data Type Corrections and Consistency  
  - Module 2, Section 2, Lesson 2 Data Type Corrections and Consistency  

Section 3: Exploratory Data Analysis (EDA)  
- Lesson 1: Introduction to EDA Concepts  
  - Module 2, Section 3, Lesson 1 Introduction to EDA Concepts  
- Lesson 2: Visual Techniques for Data Exploration  
  - Module 2, Section 3, Lesson 2 Visual Techniques for Data Exploration  

Section 4: Feature Engineering Fundamentals  
- Lesson 1: Creating and Selecting Features  
  - Module 2, Section 4, Lesson 1 Creating and Selecting Features  
- Lesson 2: Encoding Categorical Data  
  - Module 2, Section 4, Lesson 2 Encoding Categorical Data  

Section 5: Data Transformation Techniques  
- Lesson 1: Scaling and Normalization Methods  
  - Module 2, Section 5, Lesson 1 Scaling and Normalization Methods  
- Lesson 2: Data Splitting: Train, Validation, Test  
  - Module 2, Section 5, Lesson 2 Data Splitting: Train, Validation, Test  


Module 3: Fundamentals of TensorFlow and Neural Networks

Section 1: Introduction to TensorFlow Framework  
- Lesson 1: Overview of TensorFlow and Its Ecosystem  
  - Module 3, Section 1, Lesson 1 Overview of TensorFlow and Its Ecosystem  
- Lesson 2: Installing and Setting Up TensorFlow  
  - Module 3, Section 1, Lesson 2 Installing and Setting Up TensorFlow  

Section 2: TensorFlow Basics: Tensors and Computation Graphs  
- Lesson 1: Understanding Tensors and Their Operations  
  - Module 3, Section 2, Lesson 1 Understanding Tensors and Their Operations  
- Lesson 2: Introduction to Computation Graphs  
  - Module 3, Section 2, Lesson 2 Introduction to Computation Graphs  

Section 3: Building Your First Neural Network  
- Lesson 1: Layers, Nodes, and Weights Fundamentals  
  - Module 3, Section 3, Lesson 1 Layers, Nodes, and Weights Fundamentals  
- Lesson 2: Creating a Simple Model with TensorFlow  
  - Module 3, Section 3, Lesson 2 Creating a Simple Model with TensorFlow  

Section 4: Activation Functions and Loss Basics  
- Lesson 1: Common Activation Functions Explained  
  - Module 3, Section 4, Lesson 1 Common Activation Functions Explained  
- Lesson 2: Introduction to Loss Functions  
  - Module 3, Section 4, Lesson 2 Introduction to Loss Functions  

Section 5: Optimizers and the Training Process  
- Lesson 1: Overview of Optimizers in TensorFlow  
  - Module 3, Section 5, Lesson 1 Overview of Optimizers in TensorFlow  
- Lesson 2: Understanding the Model Training Cycle  
  - Module 3, Section 5, Lesson 2 Understanding the Model Training Cycle  


Module 4: Model Training, Evaluation, and Tuning

Section 1: Preparing Data for Model Training  
- Lesson 1: Splitting Data: Concepts and Best Practices  
  - Module 4, Section 1, Lesson 1 Splitting Data: Concepts and Best Practices  
- Lesson 2: Shuffling and Batching Data  
  - Module 4, Section 1, Lesson 2 Shuffling and Batching Data  

Section 2: Training Models with TensorFlow  
- Lesson 1: Running a Training Loop in TensorFlow  
  - Module 4, Section 2, Lesson 1 Running a Training Loop in TensorFlow  
- Lesson 2: Monitoring Training with Metrics  
  - Module 4, Section 2, Lesson 2 Monitoring Training with Metrics  

Section 3: Evaluating Model Performance  
- Lesson 1: Standard Evaluation Metrics for ML Models  
  - Module 4, Section 3, Lesson 1 Standard Evaluation Metrics for ML Models  
- Lesson 2: Interpreting Accuracy, Precision, and Recall  
  - Module 4, Section 3, Lesson 2 Interpreting Accuracy, Precision, and Recall  

Section 4: Introduction to Hyperparameter Tuning  
- Lesson 1: What are Hyperparameters and Why They Matter  
  - Module 4, Section 4, Lesson 1 What are Hyperparameters and Why They Matter  
- Lesson 2: Basic Techniques for Tuning Models  
  - Module 4, Section 4, Lesson 2 Basic Techniques for Tuning Models  

Section 5: Preventing Overfitting and Regularization  
- Lesson 1: Understanding Overfitting and Its Risks  
  - Module 4, Section 5, Lesson 1 Understanding Overfitting and Its Risks  
- Lesson 2: Introduction to Regularization Techniques  
  - Module 4, Section 5, Lesson 2 Introduction to Regularization Techniques  


Module 5: Real-World Applications and Ethics in AI

Section 1: Case Studies in Machine Learning  
- Lesson 1: Examining Successful ML Projects  
  - Module 5, Section 1, Lesson 1 Examining Successful ML Projects  
- Lesson 2: Learning from Practical Examples  
  - Module 5, Section 1, Lesson 2 Learning from Practical Examples  

Section 2: Interpreting Model Results for Decision Making  
- Lesson 1: Understanding Model Outputs  
  - Module 5, Section 2, Lesson 1 Understanding Model Outputs  
- Lesson 2: Using Results to Drive Business Decisions  
  - Module 5, Section 2, Lesson 2 Using Results to Drive Business Decisions  

Section 3: Deploying Models with TensorFlow Serving Basics  
- Lesson 1: Introduction to Model Deployment Concepts  
  - Module 5, Section 3, Lesson 1 Introduction to Model Deployment Concepts  
- Lesson 2: Basic Steps in Deploying a Model  
  - Module 5, Section 3, Lesson 2 Basic Steps in Deploying a Model  

Section 4: Introduction to AI Ethics and Responsible Use  
- Lesson 1: Fundamental Principles of AI Ethics  
  - Module 5, Section 4, Lesson 1 Fundamental Principles of AI Ethics  
- Lesson 2: Balancing Innovation with Responsibility  
  - Module 5, Section 4, Lesson 2 Balancing Innovation with Responsibility  

Section 5: Future Trends in Machine Learning Fundamentals  
- Lesson 1: Emerging Areas in ML and AI  
  - Module 5, Section 5, Lesson 1 Emerging Areas in ML and AI  
- Lesson 2: Preparing for the Future of Technology  
  - Module 5, Section 5, Lesson 2 Preparing for the Future of Technology