Practical Machine Learning for AI: Foundational Skills and Experiments

Overview

Unlock the potential of machine learning and take your first steps towards mastering this revolutionary technology. Enrol in this course to uncover its fundamental principles, understand ethical considerations, and gain practical skills that set you apart in the field of artificial intelligence.

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

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Taught by

Dev Kant

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Subjects

Data Science