We take a journey of 21-days exploring Artificial Intelligence (AI), focusing on fundamentals, hands-on learning and building skills through practical exercises.
- Day 1: Introduction to AI
- What is AI? Different branches of AI (Machine Learning, Deep Learning, Natural Language Processing, etc.).
- Day 2: History, Impact and ethical considerations of AI
- History, impact & ethical considerations of AI (bias, fairness, transparency).
- Day 3: Math for AI (Linear Algebra)
- Vectors, matrices, and basic linear algebra operations
- Day 4: Derivatives and Integrals in AI
- Introduction to derivatives, integrals, and their applications in AI.
- Day 5: Math for AI (Probability & Statistics)
- Probability, statistics, and their role in AI algorithms.
- Day 6: Python Programming
- Python basics to get started with Machine Learning programming.
- Day 7: Introduction to Machine Learning (ML)
- Introduction to Machine Learning concepts (supervised vs unsupervised learning, common ML algorithms).
- Day 8: Supervised Learning Algorithms (Linear Regression)
- Understand the concept of linear regression and its applications.
- Day 9: Supervised Learning Algorithms (Logistic Regression)
- Learn about logistic regression for classification problems.
- Day 10: Unsupervised Learning Algorithms (K-means Clustering)
- Understand K-means clustering for grouping data points.
- Day 11: Unsupervised Learning Algorithms (Principal Component Analysis - PCA)
- Online tutorials or textbook: Learn about PCA for dimensionality reduction in data.
- Day 12: Model Evaluation
- Learn about evaluation metrics for machine learning models (accuracy, precision, recall, etc.).
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