The Ultimate Beginner's Guide to AI and Machine Learning

Overview

This course provides the essential foundations for any beginner who truly wants to master AI and machine learning. Crucial, foundational AI concepts, all bundled into one course.

These concepts will be relevant for years to come. Mastering any craft, requires that you have solid foundations.

Anyone who is thinking about starting a career in AI and machine learning will benefit from this. Non-technical professionals such as marketers, business analysts, etc. will be able to effectively converse and work with data scientists, machine learning engineers, or even data scientists if they apply themselves to understanding the concepts in this course.

Syllabus

• Introduction to AI and Machine Learning
Definition and History of AI
Key Differences between AI, Machine Learning, and Deep Learning
• Fundamental Concepts of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
• Key Algorithms and Techniques
Linear Regression
Classification Algorithms (e.g., Decision Trees, SVMs)
Clustering (e.g., K-Means)
Neural Networks and Deep Learning Basics
• Data Preparation and Feature Engineering
Data Cleaning and Preprocessing
Feature Selection and Extraction
Handling Missing Data
• Model Evaluation and Optimization
Training and Test Sets
Cross-Validation
Evaluation Metrics (e.g., Accuracy, Precision, Recall)
• Practical Applications of AI and ML
AI in Business and Industry
Use Cases in Marketing, Healthcare, Finance, and more
• Tools and Environments
Overview of Popular ML Tools (e.g., Python libraries such as NumPy, pandas, scikit-learn, TensorFlow)
Setting up a Development Environment
• Ethical Considerations and Future Trends
Bias and Fairness in AI
Responsible AI and Privacy Concerns
Future Directions in AI Research
• Course Wrap-up and Next Steps
Summary of Key Concepts
Resources for Further Learning
Career Paths in AI and Machine Learning

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Taught by

Irlon Terblanche and Peter Alkema

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Subjects

Data Science