This course is designed for anyone who wants to learn about machine learning, from the basics to the advanced topics. Machine learning is a subfield of artificial intelligence that allows computers to learn from data without being explicitly programmed. It is used in various applications such as image recognition, natural language processing, and autonomous vehicles.
The course will start by introducing the basic concepts of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. You will learn how to work with popular machine learning algorithms such as linear regression, decision trees, and k-nearest neighbors.
As you progress through the course, you will explore advanced topics such as deep learning, neural networks, and reinforcement learning. You will also learn how to use popular machine learning frameworks such as TensorFlow and Keras to build and train machine learning models.
Throughout the course, you will gain hands-on experience working with real-world datasets and using Python programming language for machine learning. By the end of the course, you will have a solid understanding of machine learning from the basics to the advanced level.
Prerequisites:
Some programming experience is recommended, preferably in Python.
Basic knowledge of statistics and linear algebra is helpful but not required.
Who this course is for:
Anyone who wants to learn about machine learning from the basics to the advanced level.
Developers who want to enhance their skills in building machine learning models.
Learn the basic concepts of machine learning, including supervised and unsupervised learning, regression, classification, and clustering.
4 LessonsUnderstand and work with popular machine learning algorithms such as linear regression, decision trees, and k-nearest neighbors.
4 LessonsExplore advanced topics such as deep learning, neural networks, and reinforcement learning.
4 LessonsLearn how to use popular machine learning frameworks such as TensorFlow and Keras to build and train machine learning models.
2 LessonsGain hands-on experience working with real-world datasets and learn about data preprocessing, feature engineering, and evaluation metrics for machine learning models.
3 Lessons
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