Here is a general outline of the topics that are typically covered in a beginner's course on machine learning:
Introduction to machine learning: This section will introduce you to the basics of machine learning, including the different types of machine learning (supervised, unsupervised, semi-supervised, and reinforcement learning), and the main applications of machine learning.
Data pre-processing: This section will cover techniques for cleaning, organizing, and preparing data for use in machine learning models. This may include topics such as data wrangling, data visualization, and feature engineering.
Linear regression: This section will introduce you to the concept of linear regression, which is a simple but powerful algorithm that can be used for a wide range of predictive tasks. You will learn how to fit linear regression models to data, how to interpret the results, and how to evaluate the performance of linear regression models.
Classification: This section will cover the basics of classification, which is a supervised learning task that involves predicting a categorical output based on a set of inputs. You will learn about different classification algorithms such as k-Nearest Neighbors, Logistic Regression, Decision Trees and Random Forest
Unsupervised learning: This section will cover unsupervised learning techniques such as clustering (K-means, Hierarchical clustering) and dimensional reduction (PCA, t-SNE).
Neural networks and deep learning: This section will introduce you to the concepts of neural networks and deep learning, which are powerful machine learning techniques that are based on the structure of the human brain. Topics covered may include feedforward neural networks, backpropagation, convolutional neural networks, and recurrent neural networks.
Evaluation and model selection: This section will cover techniques for evaluating the performance of machine learning models, including techniques for model selection such as cross-validation, regularization and techniques to avoid overfitting.
Applications: This section will cover some real-world applications of machine learning, and examples of how it is used in different industries.
Depending on the course, the topic coverage can vary, but this is a rough guide of what you can expect to learn in a beginner's machine learning course.
It is important to keep in mind that Machine Learning is a vast field, you may find more advanced courses that will cover additional topics, but the above-mentioned topics are the ones considered as the foundation for understanding machine learning.
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