Machine Learning Handbook

Description

In this course, you will learn:

• An awareness of machine learning (ML) basics in data-driven decision-making processes
• familiarity with basic data preparation libraries and techniques
• Practical knowledge of machine learning (ML) applications in image processing, computer vision, text analysis, and natural language processing (NLP).
• Ability to discriminate between several types of ML techniques
• Understanding of the differences between ML methods and advanced concepts

Syllabus :

1. Introduction to Machine Learning

• Overview of Machine Learning
• Machine Learning’s Significance in Data-Driven Decisions

2. Common Libraries and Tools for Machine Learning Tasks

• Libraries for Machine Learning
• Tools for Data Preprocessing and Model Development

3. Types of Machine Learning

• Supervised Learning
• Unsupervised Learning
• Reinforcement LearningTraditional Machine Learning vs. Deep Learning

4. Applications of Machine Learning

• Image Processing and Computer Vision
• Natural Language Processing (NLP)

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