Practical Machine Learning for AI: Foundational Skills and Experiments

Overview

Discover machine learning principles and applications on this course for non-ML specialists.

Syllabus

- Introduction to Machine Learning -- What is Machine Learning? -- Historical context and development -- Overview of current applications - Fundamental Principles of Machine Learning -- Types of Machine Learning: Supervised, Unsupervised, Reinforcement -- Key concepts: features, labels, models -- Data collection and preprocessing - Supervised Learning -- Linear regression -- Classification algorithms (e.g., decision trees, SVMs) -- Evaluation metrics: accuracy, precision, recall, F1 score - Unsupervised Learning -- Clustering techniques: K-means, hierarchical clustering -- Dimensionality reduction: PCA, t-SNE -- Anomaly detection - Introduction to Neural Networks -- Basics of neural networks and deep learning -- Understanding architecture: layers, nodes, activation functions -- Training process: forward and backward propagation - Model Evaluation and Optimization -- Cross-validation techniques -- Hyperparameter tuning -- Avoiding overfitting and underfitting - Tools and Frameworks -- Overview of key libraries: Scikit-learn, TensorFlow, PyTorch -- Environment setup: Anaconda, Jupyter Notebooks - Ethics in Machine Learning -- Bias and fairness in AI systems -- Data privacy and security issues -- Accountability and transparency in models - Practical Experiments -- Hands-on project: building and evaluating a simple ML model -- Kaggle challenges and competitions -- Collaborative exercises and peer reviews - Future Directions in Machine Learning -- Trends in AI and ML research -- Careers and roles in machine learning and AI -- Resources for continued learning and development

Taught by

Dev Kant

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