This course is a comprehensive introduction to machine learning for beginners. You will learn the basics of machine learning, including supervised learning. You will also learn how to build and evaluate machine learning models in Python.
In the first section of the course, you will learn what machine learning is and how it works. You will also learn about the different types of machine learning algorithms and their applications.
In the second section of the course, you will learn about supervised learning. Supervised learning is a type of machine learning in which the algorithm is trained on a set of labeled data. The algorithm learns to predict the output for new data based on the patterns in the training data.
In the third section of the course, you will learn about unsupervised learning. Unsupervised learning is a type of machine learning in which the algorithm is trained on a set of unlabeled data. The algorithm learns to identify patterns in the data without being told what the patterns are.
In the fourth section of the course, you will learn about reinforcement learning.
In the fifth section of the course, you will learn how to build and evaluate machine learning models in Python. You will learn how to use popular Python libraries such as NumPy, pandas, and scikit-learn to build and evaluate different types of machine learning models.
By the end of this course, you will have a solid understanding of the basics of machine learning and you will be able to build and evaluate machine learning models in Python. You will also be able to apply machine learning to solve real-world problems.
Machine Learning for Beginners
Learn Machine Learning Step-by-Step
Machine Learning for Real-World Applications
Introduction to Machine Learning
Supervised Learning Algorithms
Machine Learning for Medical Diagnosis
Machine Learning for Financial Trading
Machine Learning for the Future
3 Machine Learning Projects
Machine Learning Algorithms classification and regression
How Machine Learning Works
