Machine Learning in 30 mins: fast practice

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.

• these are the main things will be explained in this course

1. Machine Learning for Beginners
2. Learn Machine Learning Step-by-Step
3. Machine Learning for Real-World Applications
4. Introduction to Machine Learning
5. Supervised Learning Algorithms
6. Machine Learning for Medical Diagnosis
7. Machine Learning for Financial Trading
8. Machine Learning for the Future
9. 3 Machine Learning Projects
10. Machine Learning Algorithms classification and regression
11. How Machine Learning Works