Machine Learning: From Basics to Training Your First Model
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Machine Learning: From Basics to Training Your First Model

Beginner

1. Introduction to Machine Learning

  • 1. What is Machine Learning? Understanding the Basics
  • 2. Types of Machine Learning: Supervised, Unsupervised, and Reinforcement
  • 3. Understanding Data: Features, Labels, and Datasets
  • 4. Common Use Cases and Applications of Machine Learning
  • 5. Practice: Explore Sample Datasets and Their Structures

2. Preparing Your Data for Machine Learning

  • 1. Data Cleaning: Handling Missing Values and Outliers
  • 2. Feature Engineering: Creating and Selecting Features
  • 3. Data Splitting: Training, Validation, and Test Sets
  • 4. Practice: Prepare a Dataset for Model Training

3. Training Your First Machine Learning Model

  • 1. Introduction to Supervised Learning Algorithms: Regression and Classification
  • 2. Training a Simple Linear Regression Model with scikit-learn
  • 3. Evaluating Model Performance: Metrics and Error Analysis
  • 4. Improving Your Model: Overfitting, Underfitting, and Regularization Basics
  • 5. Practice: Train and Evaluate a Model on Real Data

4. Next Steps and Practical Considerations

  • 1. Model Deployment Basics and Tools Overview
  • 2. Ethics and Bias in Machine Learning
  • 3. Resources for Further Learning and Practice