Lesson 1: Introduction to Machine Learning
In this lesson, we will cover the basic concepts of machine learning, its types, and the overall workflow. Here are some key topics to explore:
- What is machine learning? Understand the fundamental principles behind machine learning and its applications.
- Types of machine learning: Learn about the different types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
- Supervised learning: Dive into supervised learning, where you have labeled data and aim to build models that can make predictions or classify new, unseen data points.
- Unsupervised learning: Explore unsupervised learning, which deals with unlabeled data and aims to discover hidden patterns or structures within the data.
- Model evaluation: Understand how to assess the performance of machine learning models using various evaluation metrics, such as accuracy, precision, recall, and F1-score.
- Overfitting and underfitting: Learn about the concepts of overfitting and underfitting, and techniques such as cross-validation and regularization to address these issues.
- Feature engineering: Gain insights into the importance of feature engineering and how to preprocess and transform data to improve model performance.
- Introduction to popular libraries: Familiarize yourself with popular Python libraries for machine learning, such as scikit-learn and TensorFlow, and explore their basic functionality.
Throughout this lesson, I’ll provide explanations, examples, and code snippets to help you understand and apply the concepts. Feel free to ask questions or request clarification whenever needed.
Let’s start with the basics, and once you feel comfortable, we can gradually move on to more advanced topics in subsequent lessons.