Essentials of Machine Learning
This comprehensive course is designed to introduce the fundamentals of machine learning (ML), a pivotal technology in the AI revolution. Through engaging lectures and hands-on projects, participants will explore the various algorithms and tools that underpin the most successful machine learning solutions. Dive deep into both supervised and unsupervised learning, understand how decision trees, neural networks, and clustering function, and apply these methods to real-world problems.
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Course Overview
5 sections . 20 lessons .
Introduction to Machine Learning
Supervised Learning: Regression and Classification
Unsupervised Learning: Clustering and Dimensionality Reduction
Neural Networks and Deep Learning
Real-world Applications and Ethical Implications
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Course Objectives
Understand the core principles of machine learning and its applications.
Learn to interpret machine learning models and their outputs.
Gain hands-on experience with both supervised and unsupervised learning models.
Develop practical skills in designing and implementing algorithms using Python.
Prerequisites
Basic programming knowledge, particularly in Python.
A general understanding of statistics and probability.
Familiarity with basic algebra and calculus concepts.
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