Welcome to the Machine Learning Introduction course! 🚀
This pathway provides foundational knowledge for beginners exploring the world of AI and data science.
📚 Course Overview
Machine learning (ML) is a subset of artificial intelligence that enables systems to learn from data. Through this course, you'll:
- Understand core concepts like supervised/unsupervised learning
- Explore algorithms including linear regression, decision trees, and clustering
- Learn to apply ML techniques using Python and popular libraries
🎯 Learning Objectives
- Grasp the principles of data-driven decision making
- Implement basic ML models with real-world datasets
- Analyze ethical implications of AI technologies 🧠
- Discover career opportunities in ML engineering
📘 Course Outline
Introduction to ML
- What is machine learning?
- History and evolution of ML
Data Preprocessing
- Cleaning and transforming datasets
- Feature engineering techniques
- Tools: Pandas, NumPy
Core Algorithms
- Linear regression 📈
- K-means clustering 🧩
- Neural networks 🤖
Model Evaluation
- Metrics: Accuracy, Precision, Recall
- Cross-validation techniques
- Overfitting vs. underfitting ⚖️
Ethical AI Practices
- Bias in algorithms ❗
- Privacy considerations 🔒
- Sustainable ML development 🌱
🌐 Expand Your Knowledge
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